Contributor Code of Conduct
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
@@ -345,7 +345,7 @@Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/CODE_OF_CONDUCT.html", "identifier": "https://ccb-hms.github.io/osca-carpentries/CODE_OF_CONDUCT.html", "dateCreated": "2024-01-10", - "dateModified": "2024-05-23", + "dateModified": "2024-06-04", "datePublished": "2024-06-04" } diff --git a/LICENSE.html b/LICENSE.html index 7dda612..d8fe475 100644 --- a/LICENSE.html +++ b/LICENSE.html @@ -262,7 +262,7 @@Licenses
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
@@ -396,7 +396,7 @@Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/LICENSE.html", "identifier": "https://ccb-hms.github.io/osca-carpentries/LICENSE.html", "dateCreated": "2024-01-10", - "dateModified": "2024-05-23", + "dateModified": "2024-06-04", "datePublished": "2024-06-04" } diff --git a/aio.html b/aio.html index 72ebd9e..6b6f0ea 100644 --- a/aio.html +++ b/aio.html @@ -310,7 +310,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -758,7 +758,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -825,7 +825,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1005,7 +1005,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1813,7 +1813,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1913,7 +1913,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -2891,7 +2891,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2903,24 +2903,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2940,7 +2940,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3149,7 +3149,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -3977,16 +3977,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -3998,11 +3998,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4011,7 +4011,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4026,10 +4026,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4173,7 +4173,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -4611,21 +4611,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4634,7 +4639,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4669,7 +4674,7 @@ Singular value decomposition
+
R
@@ -4695,7 +4700,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4743,7 +4748,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4765,7 +4770,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4773,7 +4778,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4782,7 +4787,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4796,7 +4801,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4807,7 +4812,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4840,7 +4845,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4850,7 +4855,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4876,7 +4881,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4891,7 +4896,7 @@ RSession Info
-
+
R
@@ -4931,11 +4936,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4952,16 +4957,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4980,7 +4985,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4989,7 +4994,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5008,7 +5013,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5195,7 +5200,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index ed861c4..6801350 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1250,7 +1250,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1262,24 +1262,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1299,7 +1299,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1559,7 +1559,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -457,7 +457,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1397,7 +1397,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -975,7 +975,7 @@ Key Points
Contributor Code of Conduct
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -347,7 +347,7 @@ Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index d24049f..80fd3f9 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -262,7 +262,7 @@
Licenses
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -398,7 +398,7 @@ Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/aio.html b/instructor/aio.html
index 464f60c..c1d6c00 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -828,7 +828,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1917,7 +1917,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2908,24 +2908,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2945,7 +2945,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3154,7 +3154,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -4004,11 +4004,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4017,7 +4017,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4032,10 +4032,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4179,7 +4179,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4641,7 +4646,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4676,7 +4681,7 @@ Singular value decomposition
+
R
@@ -4702,7 +4707,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4750,7 +4755,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4772,7 +4777,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4780,7 +4785,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4789,7 +4794,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4803,7 +4808,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4814,7 +4819,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4847,7 +4852,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4857,7 +4862,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4883,7 +4888,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4898,7 +4903,7 @@ RSession Info
-
+
R
@@ -4938,11 +4943,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4959,16 +4964,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4987,7 +4992,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4996,7 +5001,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5015,7 +5020,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5202,7 +5207,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 4db20fd..1bf40c7 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1252,7 +1252,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1264,24 +1264,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1301,7 +1301,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1561,7 +1561,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -459,7 +459,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1399,7 +1399,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -977,7 +977,7 @@ Key Points
Introduction to Bioconductor and the SingleCellExperiment class
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -717,7 +717,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -836,7 +836,7 @@ Key Points
Working with large data
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -716,21 +716,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -739,7 +744,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -773,7 +778,7 @@ Singular value decomposition
+
R
@@ -799,7 +804,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -845,7 +850,7 @@ Seurat<
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -867,7 +872,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -875,7 +880,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -884,7 +889,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -898,7 +903,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -909,7 +914,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -941,7 +946,7 @@ Scanpy<
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -951,7 +956,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -977,7 +982,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
Content from Introduction to Bioconductor and the SingleCellExperiment class
-
Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -825,7 +825,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1005,7 +1005,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1813,7 +1813,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1913,7 +1913,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -2891,7 +2891,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2903,24 +2903,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2940,7 +2940,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3149,7 +3149,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -3977,16 +3977,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -3998,11 +3998,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4011,7 +4011,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4026,10 +4026,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4173,7 +4173,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -4611,21 +4611,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4634,7 +4639,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4669,7 +4674,7 @@ Singular value decomposition
+
R
@@ -4695,7 +4700,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4743,7 +4748,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4765,7 +4770,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4773,7 +4778,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4782,7 +4787,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4796,7 +4801,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4807,7 +4812,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4840,7 +4845,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4850,7 +4855,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4876,7 +4881,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4891,7 +4896,7 @@ RSession Info
-
+
R
@@ -4931,11 +4936,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4952,16 +4957,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4980,7 +4985,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4989,7 +4994,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5008,7 +5013,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5195,7 +5200,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index ed861c4..6801350 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1250,7 +1250,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1262,24 +1262,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1299,7 +1299,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1559,7 +1559,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -457,7 +457,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1397,7 +1397,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -975,7 +975,7 @@ Key Points
Contributor Code of Conduct
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -347,7 +347,7 @@ Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index d24049f..80fd3f9 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -262,7 +262,7 @@
Licenses
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -398,7 +398,7 @@ Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/aio.html b/instructor/aio.html
index 464f60c..c1d6c00 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -828,7 +828,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1917,7 +1917,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2908,24 +2908,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2945,7 +2945,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3154,7 +3154,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -4004,11 +4004,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4017,7 +4017,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4032,10 +4032,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4179,7 +4179,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4641,7 +4646,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4676,7 +4681,7 @@ Singular value decomposition
+
R
@@ -4702,7 +4707,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4750,7 +4755,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4772,7 +4777,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4780,7 +4785,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4789,7 +4794,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4803,7 +4808,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4814,7 +4819,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4847,7 +4852,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4857,7 +4862,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4883,7 +4888,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4898,7 +4903,7 @@ RSession Info
-
+
R
@@ -4938,11 +4943,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4959,16 +4964,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4987,7 +4992,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4996,7 +5001,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5015,7 +5020,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5202,7 +5207,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 4db20fd..1bf40c7 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1252,7 +1252,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1264,24 +1264,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1301,7 +1301,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1561,7 +1561,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -459,7 +459,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1399,7 +1399,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -977,7 +977,7 @@ Key Points
Introduction to Bioconductor and the SingleCellExperiment class
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -717,7 +717,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -836,7 +836,7 @@ Key Points
Working with large data
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -716,21 +716,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -739,7 +744,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -773,7 +778,7 @@ Singular value decomposition
+
R
@@ -799,7 +804,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -845,7 +850,7 @@ Seurat<
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -867,7 +872,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -875,7 +880,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -884,7 +889,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -898,7 +903,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -909,7 +914,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -941,7 +946,7 @@ Scanpy<
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -951,7 +956,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -977,7 +982,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1005,7 +1005,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1813,7 +1813,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1913,7 +1913,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -2891,7 +2891,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2903,24 +2903,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2940,7 +2940,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3149,7 +3149,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -3977,16 +3977,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -3998,11 +3998,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4011,7 +4011,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4026,10 +4026,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4173,7 +4173,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -4611,21 +4611,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4634,7 +4639,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4669,7 +4674,7 @@ Singular value decomposition
+
R
@@ -4695,7 +4700,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4743,7 +4748,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4765,7 +4770,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4773,7 +4778,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4782,7 +4787,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4796,7 +4801,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4807,7 +4812,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4840,7 +4845,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4850,7 +4855,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4876,7 +4881,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4891,7 +4896,7 @@ RSession Info
-
+
R
@@ -4931,11 +4936,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4952,16 +4957,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4980,7 +4985,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4989,7 +4994,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5008,7 +5013,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5195,7 +5200,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index ed861c4..6801350 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1250,7 +1250,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1262,24 +1262,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1299,7 +1299,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1559,7 +1559,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -457,7 +457,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1397,7 +1397,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -975,7 +975,7 @@ Key Points
Contributor Code of Conduct
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -347,7 +347,7 @@ Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index d24049f..80fd3f9 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -262,7 +262,7 @@
Licenses
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -398,7 +398,7 @@ Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/aio.html b/instructor/aio.html
index 464f60c..c1d6c00 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -828,7 +828,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1917,7 +1917,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2908,24 +2908,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2945,7 +2945,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3154,7 +3154,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -4004,11 +4004,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4017,7 +4017,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4032,10 +4032,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4179,7 +4179,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4641,7 +4646,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4676,7 +4681,7 @@ Singular value decomposition
+
R
@@ -4702,7 +4707,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4750,7 +4755,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4772,7 +4777,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4780,7 +4785,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4789,7 +4794,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4803,7 +4808,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4814,7 +4819,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4847,7 +4852,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4857,7 +4862,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4883,7 +4888,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4898,7 +4903,7 @@ RSession Info
-
+
R
@@ -4938,11 +4943,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4959,16 +4964,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4987,7 +4992,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4996,7 +5001,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5015,7 +5020,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5202,7 +5207,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 4db20fd..1bf40c7 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1252,7 +1252,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1264,24 +1264,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1301,7 +1301,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1561,7 +1561,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -459,7 +459,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1399,7 +1399,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -977,7 +977,7 @@ Key Points
Introduction to Bioconductor and the SingleCellExperiment class
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -717,7 +717,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -836,7 +836,7 @@ Key Points
Working with large data
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -716,21 +716,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -739,7 +744,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -773,7 +778,7 @@ Singular value decomposition
+
R
@@ -799,7 +804,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -845,7 +850,7 @@ Seurat<
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -867,7 +872,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -875,7 +880,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -884,7 +889,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -898,7 +903,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -909,7 +914,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -941,7 +946,7 @@ Scanpy<
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -951,7 +956,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -977,7 +982,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1813,7 +1813,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1913,7 +1913,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -2891,7 +2891,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2903,24 +2903,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2940,7 +2940,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3149,7 +3149,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -3977,16 +3977,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -3998,11 +3998,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4011,7 +4011,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4026,10 +4026,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4173,7 +4173,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -4611,21 +4611,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4634,7 +4639,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4669,7 +4674,7 @@ Singular value decomposition
+
R
@@ -4695,7 +4700,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4743,7 +4748,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4765,7 +4770,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4773,7 +4778,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4782,7 +4787,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4796,7 +4801,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4807,7 +4812,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4840,7 +4845,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4850,7 +4855,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4876,7 +4881,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4891,7 +4896,7 @@ RSession Info
-
+
R
@@ -4931,11 +4936,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4952,16 +4957,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4980,7 +4985,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4989,7 +4994,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5008,7 +5013,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5195,7 +5200,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index ed861c4..6801350 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1250,7 +1250,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1262,24 +1262,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1299,7 +1299,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1559,7 +1559,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -457,7 +457,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1397,7 +1397,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -975,7 +975,7 @@ Key Points
Contributor Code of Conduct
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -347,7 +347,7 @@ Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index d24049f..80fd3f9 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -262,7 +262,7 @@
Licenses
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -398,7 +398,7 @@ Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/aio.html b/instructor/aio.html
index 464f60c..c1d6c00 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -828,7 +828,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1917,7 +1917,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2908,24 +2908,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2945,7 +2945,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3154,7 +3154,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -4004,11 +4004,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4017,7 +4017,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4032,10 +4032,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4179,7 +4179,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4641,7 +4646,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4676,7 +4681,7 @@ Singular value decomposition
+
R
@@ -4702,7 +4707,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4750,7 +4755,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4772,7 +4777,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4780,7 +4785,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4789,7 +4794,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4803,7 +4808,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4814,7 +4819,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4847,7 +4852,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4857,7 +4862,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4883,7 +4888,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4898,7 +4903,7 @@ RSession Info
-
+
R
@@ -4938,11 +4943,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4959,16 +4964,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4987,7 +4992,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4996,7 +5001,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5015,7 +5020,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5202,7 +5207,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 4db20fd..1bf40c7 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1252,7 +1252,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1264,24 +1264,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1301,7 +1301,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1561,7 +1561,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -459,7 +459,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1399,7 +1399,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -977,7 +977,7 @@ Key Points
Introduction to Bioconductor and the SingleCellExperiment class
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -717,7 +717,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -836,7 +836,7 @@ Key Points
Working with large data
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -716,21 +716,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -739,7 +744,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -773,7 +778,7 @@ Singular value decomposition
+
R
@@ -799,7 +804,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -845,7 +850,7 @@ Seurat<
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -867,7 +872,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -875,7 +880,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -884,7 +889,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -898,7 +903,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -909,7 +914,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -941,7 +946,7 @@ Scanpy<
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -951,7 +956,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -977,7 +982,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
Whenever your code involves the generation of random numbers, it’s a good practice to set the random seed in R with @@ -1813,7 +1813,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1913,7 +1913,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -2891,7 +2891,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2903,24 +2903,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2940,7 +2940,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3149,7 +3149,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -3977,16 +3977,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -3998,11 +3998,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4011,7 +4011,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4026,10 +4026,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4173,7 +4173,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -4611,21 +4611,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4634,7 +4639,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4669,7 +4674,7 @@ Singular value decomposition
+
R
@@ -4695,7 +4700,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4743,7 +4748,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4765,7 +4770,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4773,7 +4778,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4782,7 +4787,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4796,7 +4801,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4807,7 +4812,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4840,7 +4845,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4850,7 +4855,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4876,7 +4881,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4891,7 +4896,7 @@ RSession Info
-
+
R
@@ -4931,11 +4936,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4952,16 +4957,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4980,7 +4985,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4989,7 +4994,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5008,7 +5013,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5195,7 +5200,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index ed861c4..6801350 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1250,7 +1250,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1262,24 +1262,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1299,7 +1299,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1559,7 +1559,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -457,7 +457,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1397,7 +1397,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -975,7 +975,7 @@ Key Points
Contributor Code of Conduct
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -347,7 +347,7 @@ Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index d24049f..80fd3f9 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -262,7 +262,7 @@
Licenses
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -398,7 +398,7 @@ Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/aio.html b/instructor/aio.html
index 464f60c..c1d6c00 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -828,7 +828,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1917,7 +1917,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2908,24 +2908,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2945,7 +2945,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3154,7 +3154,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -4004,11 +4004,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4017,7 +4017,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4032,10 +4032,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4179,7 +4179,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4641,7 +4646,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4676,7 +4681,7 @@ Singular value decomposition
+
R
@@ -4702,7 +4707,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4750,7 +4755,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4772,7 +4777,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4780,7 +4785,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4789,7 +4794,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4803,7 +4808,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4814,7 +4819,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4847,7 +4852,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4857,7 +4862,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4883,7 +4888,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4898,7 +4903,7 @@ RSession Info
-
+
R
@@ -4938,11 +4943,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4959,16 +4964,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4987,7 +4992,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4996,7 +5001,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5015,7 +5020,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5202,7 +5207,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 4db20fd..1bf40c7 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1252,7 +1252,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1264,24 +1264,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1301,7 +1301,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1561,7 +1561,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -459,7 +459,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1399,7 +1399,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -977,7 +977,7 @@ Key Points
Introduction to Bioconductor and the SingleCellExperiment class
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -717,7 +717,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -836,7 +836,7 @@ Key Points
Working with large data
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -716,21 +716,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -739,7 +744,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -773,7 +778,7 @@ Singular value decomposition
+
R
@@ -799,7 +804,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -845,7 +850,7 @@ Seurat<
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -867,7 +872,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -875,7 +880,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -884,7 +889,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -898,7 +903,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -909,7 +914,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -941,7 +946,7 @@ Scanpy<
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -951,7 +956,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -977,7 +982,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1913,7 +1913,7 @@
Further Reading
Content from Cell type annotation
-
Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2903,24 +2903,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2940,7 +2940,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3149,7 +3149,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -3977,16 +3977,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -3998,11 +3998,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4011,7 +4011,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4026,10 +4026,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4173,7 +4173,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -4611,21 +4611,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4634,7 +4639,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4669,7 +4674,7 @@ Singular value decomposition
+
R
@@ -4695,7 +4700,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4743,7 +4748,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4765,7 +4770,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4773,7 +4778,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4782,7 +4787,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4796,7 +4801,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4807,7 +4812,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4840,7 +4845,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4850,7 +4855,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4876,7 +4881,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4891,7 +4896,7 @@ RSession Info
-
+
R
@@ -4931,11 +4936,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4952,16 +4957,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4980,7 +4985,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4989,7 +4994,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5008,7 +5013,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5195,7 +5200,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index ed861c4..6801350 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1250,7 +1250,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1262,24 +1262,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1299,7 +1299,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1559,7 +1559,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -457,7 +457,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1397,7 +1397,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -975,7 +975,7 @@ Key Points
Contributor Code of Conduct
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -347,7 +347,7 @@ Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index d24049f..80fd3f9 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -262,7 +262,7 @@
Licenses
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -398,7 +398,7 @@ Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/aio.html b/instructor/aio.html
index 464f60c..c1d6c00 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -828,7 +828,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1917,7 +1917,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2908,24 +2908,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2945,7 +2945,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3154,7 +3154,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -4004,11 +4004,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4017,7 +4017,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4032,10 +4032,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4179,7 +4179,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4641,7 +4646,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4676,7 +4681,7 @@ Singular value decomposition
+
R
@@ -4702,7 +4707,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4750,7 +4755,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4772,7 +4777,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4780,7 +4785,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4789,7 +4794,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4803,7 +4808,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4814,7 +4819,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4847,7 +4852,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4857,7 +4862,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4883,7 +4888,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4898,7 +4903,7 @@ RSession Info
-
+
R
@@ -4938,11 +4943,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4959,16 +4964,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4987,7 +4992,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4996,7 +5001,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5015,7 +5020,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5202,7 +5207,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 4db20fd..1bf40c7 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1252,7 +1252,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1264,24 +1264,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1301,7 +1301,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1561,7 +1561,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -459,7 +459,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1399,7 +1399,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -977,7 +977,7 @@ Key Points
Introduction to Bioconductor and the SingleCellExperiment class
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -717,7 +717,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -836,7 +836,7 @@ Key Points
Working with large data
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -716,21 +716,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -739,7 +744,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -773,7 +778,7 @@ Singular value decomposition
+
R
@@ -799,7 +804,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -845,7 +850,7 @@ Seurat<
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -867,7 +872,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -875,7 +880,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -884,7 +889,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -898,7 +903,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -909,7 +914,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -941,7 +946,7 @@ Scanpy<
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -951,7 +956,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -977,7 +982,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3149,7 +3149,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -3977,16 +3977,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -3998,11 +3998,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4011,7 +4011,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4026,10 +4026,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4173,7 +4173,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -4611,21 +4611,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4634,7 +4639,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4669,7 +4674,7 @@ Singular value decomposition
+
R
@@ -4695,7 +4700,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4743,7 +4748,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4765,7 +4770,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4773,7 +4778,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4782,7 +4787,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4796,7 +4801,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4807,7 +4812,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4840,7 +4845,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4850,7 +4855,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4876,7 +4881,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4891,7 +4896,7 @@ RSession Info
-
+
R
@@ -4931,11 +4936,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4952,16 +4957,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4980,7 +4985,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4989,7 +4994,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5008,7 +5013,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5195,7 +5200,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index ed861c4..6801350 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1250,7 +1250,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1262,24 +1262,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1299,7 +1299,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1559,7 +1559,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -457,7 +457,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1397,7 +1397,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -975,7 +975,7 @@ Key Points
Contributor Code of Conduct
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -347,7 +347,7 @@ Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index d24049f..80fd3f9 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -262,7 +262,7 @@
Licenses
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -398,7 +398,7 @@ Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/aio.html b/instructor/aio.html
index 464f60c..c1d6c00 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -828,7 +828,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1917,7 +1917,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2908,24 +2908,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2945,7 +2945,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3154,7 +3154,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -4004,11 +4004,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4017,7 +4017,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4032,10 +4032,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4179,7 +4179,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4641,7 +4646,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4676,7 +4681,7 @@ Singular value decomposition
+
R
@@ -4702,7 +4707,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4750,7 +4755,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4772,7 +4777,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4780,7 +4785,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4789,7 +4794,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4803,7 +4808,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4814,7 +4819,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4847,7 +4852,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4857,7 +4862,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4883,7 +4888,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4898,7 +4903,7 @@ RSession Info
-
+
R
@@ -4938,11 +4943,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4959,16 +4964,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4987,7 +4992,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4996,7 +5001,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5015,7 +5020,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5202,7 +5207,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 4db20fd..1bf40c7 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1252,7 +1252,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1264,24 +1264,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1301,7 +1301,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1561,7 +1561,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -459,7 +459,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1399,7 +1399,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -977,7 +977,7 @@ Key Points
Introduction to Bioconductor and the SingleCellExperiment class
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -717,7 +717,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -836,7 +836,7 @@ Key Points
Working with large data
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -716,21 +716,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -739,7 +744,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -773,7 +778,7 @@ Singular value decomposition
+
R
@@ -799,7 +804,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -845,7 +850,7 @@ Seurat<
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -867,7 +872,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -875,7 +880,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -884,7 +889,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -898,7 +903,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -909,7 +914,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -941,7 +946,7 @@ Scanpy<
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -951,7 +956,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -977,7 +982,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
Content from Multi-sample analyses
-
Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -3998,11 +3998,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4011,7 +4011,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4026,10 +4026,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4173,7 +4173,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -4611,21 +4611,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4634,7 +4639,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4669,7 +4674,7 @@ Singular value decomposition
+
R
@@ -4695,7 +4700,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4743,7 +4748,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4765,7 +4770,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4773,7 +4778,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4782,7 +4787,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4796,7 +4801,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4807,7 +4812,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4840,7 +4845,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4850,7 +4855,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4876,7 +4881,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4891,7 +4896,7 @@ RSession Info
-
+
R
@@ -4931,11 +4936,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4952,16 +4957,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4980,7 +4985,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4989,7 +4994,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5008,7 +5013,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5195,7 +5200,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index ed861c4..6801350 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1250,7 +1250,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1262,24 +1262,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1299,7 +1299,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1559,7 +1559,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -457,7 +457,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1397,7 +1397,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -975,7 +975,7 @@ Key Points
Contributor Code of Conduct
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -347,7 +347,7 @@ Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index d24049f..80fd3f9 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -262,7 +262,7 @@
Licenses
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -398,7 +398,7 @@ Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/aio.html b/instructor/aio.html
index 464f60c..c1d6c00 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -828,7 +828,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1917,7 +1917,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2908,24 +2908,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2945,7 +2945,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3154,7 +3154,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -4004,11 +4004,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4017,7 +4017,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4032,10 +4032,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4179,7 +4179,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4641,7 +4646,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4676,7 +4681,7 @@ Singular value decomposition
+
R
@@ -4702,7 +4707,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4750,7 +4755,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4772,7 +4777,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4780,7 +4785,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4789,7 +4794,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4803,7 +4808,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4814,7 +4819,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4847,7 +4852,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4857,7 +4862,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4883,7 +4888,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4898,7 +4903,7 @@ RSession Info
-
+
R
@@ -4938,11 +4943,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4959,16 +4964,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4987,7 +4992,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4996,7 +5001,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5015,7 +5020,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5202,7 +5207,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 4db20fd..1bf40c7 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1252,7 +1252,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1264,24 +1264,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1301,7 +1301,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1561,7 +1561,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -459,7 +459,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1399,7 +1399,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -977,7 +977,7 @@ Key Points
Introduction to Bioconductor and the SingleCellExperiment class
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -717,7 +717,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -836,7 +836,7 @@ Key Points
Working with large data
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -716,21 +716,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -739,7 +744,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -773,7 +778,7 @@ Singular value decomposition
+
R
@@ -799,7 +804,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -845,7 +850,7 @@ Seurat<
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -867,7 +872,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -875,7 +880,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -884,7 +889,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -898,7 +903,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -909,7 +914,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -941,7 +946,7 @@ Scanpy<
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -951,7 +956,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -977,7 +982,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4026,10 +4026,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4173,7 +4173,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -4611,21 +4611,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4634,7 +4639,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4669,7 +4674,7 @@ Singular value decomposition
+
R
@@ -4695,7 +4700,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4743,7 +4748,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4765,7 +4770,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4773,7 +4778,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4782,7 +4787,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4796,7 +4801,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4807,7 +4812,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4840,7 +4845,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4850,7 +4855,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4876,7 +4881,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4891,7 +4896,7 @@ RSession Info
-
+
R
@@ -4931,11 +4936,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4952,16 +4957,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4980,7 +4985,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4989,7 +4994,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5008,7 +5013,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5195,7 +5200,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index ed861c4..6801350 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1250,7 +1250,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1262,24 +1262,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1299,7 +1299,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1559,7 +1559,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -457,7 +457,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1397,7 +1397,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -975,7 +975,7 @@ Key Points
Contributor Code of Conduct
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -347,7 +347,7 @@ Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index d24049f..80fd3f9 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -262,7 +262,7 @@
Licenses
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -398,7 +398,7 @@ Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/aio.html b/instructor/aio.html
index 464f60c..c1d6c00 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -828,7 +828,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1917,7 +1917,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2908,24 +2908,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2945,7 +2945,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3154,7 +3154,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -4004,11 +4004,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4017,7 +4017,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4032,10 +4032,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4179,7 +4179,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4641,7 +4646,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4676,7 +4681,7 @@ Singular value decomposition
+
R
@@ -4702,7 +4707,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4750,7 +4755,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4772,7 +4777,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4780,7 +4785,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4789,7 +4794,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4803,7 +4808,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4814,7 +4819,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4847,7 +4852,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4857,7 +4862,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4883,7 +4888,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4898,7 +4903,7 @@ RSession Info
-
+
R
@@ -4938,11 +4943,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4959,16 +4964,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4987,7 +4992,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4996,7 +5001,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5015,7 +5020,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5202,7 +5207,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 4db20fd..1bf40c7 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1252,7 +1252,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1264,24 +1264,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1301,7 +1301,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1561,7 +1561,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -459,7 +459,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1399,7 +1399,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -977,7 +977,7 @@ Key Points
Introduction to Bioconductor and the SingleCellExperiment class
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -717,7 +717,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -836,7 +836,7 @@ Key Points
Working with large data
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -716,21 +716,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -739,7 +744,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -773,7 +778,7 @@ Singular value decomposition
+
R
@@ -799,7 +804,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -845,7 +850,7 @@ Seurat<
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -867,7 +872,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -875,7 +880,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -884,7 +889,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -898,7 +903,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -909,7 +914,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -941,7 +946,7 @@ Scanpy<
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -951,7 +956,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -977,7 +982,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -4611,21 +4611,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4634,7 +4639,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4669,7 +4674,7 @@ Singular value decomposition
+
R
@@ -4695,7 +4700,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4743,7 +4748,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4765,7 +4770,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4773,7 +4778,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4782,7 +4787,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4796,7 +4801,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4807,7 +4812,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4840,7 +4845,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4850,7 +4855,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4876,7 +4881,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4891,7 +4896,7 @@ RSession Info
-
+
R
@@ -4931,11 +4936,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4952,16 +4957,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4980,7 +4985,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4989,7 +4994,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5008,7 +5013,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5195,7 +5200,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index ed861c4..6801350 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1250,7 +1250,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1262,24 +1262,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1299,7 +1299,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1559,7 +1559,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -457,7 +457,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1397,7 +1397,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -975,7 +975,7 @@ Key Points
Contributor Code of Conduct
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -347,7 +347,7 @@ Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index d24049f..80fd3f9 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -262,7 +262,7 @@
Licenses
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -398,7 +398,7 @@ Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
"dateCreated": "2024-01-10",
- "dateModified": "2024-05-23",
+ "dateModified": "2024-06-04",
"datePublished": "2024-06-04"
}
diff --git a/instructor/aio.html b/instructor/aio.html
index 464f60c..c1d6c00 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -828,7 +828,7 @@ Key PointsContent from Exploratory data analysis and quality control
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1917,7 +1917,7 @@
Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -2908,24 +2908,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -2945,7 +2945,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -3154,7 +3154,7 @@ Key PointsContent from Multi-sample analyses
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@ OUTPUT<
[7] scuttle_1.14.0 MouseGastrulationData_1.18.0
[9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0
-[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
+[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[15] IRanges_2.38.0 S4Vectors_0.42.0
[17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[19] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
- [1] DBI_1.2.2 formatR_1.14
+ [1] DBI_1.2.3 formatR_1.14
[3] gridExtra_2.3 rlang_1.1.3
[5] magrittr_2.0.3 compiler_4.4.0
- [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0
+ [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0
[9] png_0.1-8 vctrs_0.6.5
[11] pkgconfig_2.0.3 crayon_1.5.2
[13] fastmap_1.2.0 dbplyr_2.5.0
@@ -4004,11 +4004,11 @@ OUTPUT<
[25] xfun_0.44 zlibbioc_1.50.0
[27] cachem_1.1.0 beachmat_2.20.0
[29] jsonlite_1.8.8 blob_1.2.4
- [31] highr_0.10 DelayedArray_0.30.1
+ [31] highr_0.11 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 cluster_2.1.6
[35] irlba_2.3.5.1 parallel_4.4.0
[37] R6_2.5.1 Rcpp_1.0.12
- [39] knitr_1.46 splines_4.4.0
+ [39] knitr_1.47 splines_4.4.0
[41] igraph_2.0.3 Matrix_1.7-0
[43] tidyselect_1.2.1 viridis_0.6.5
[45] abind_1.4-5 yaml_2.3.8
@@ -4017,7 +4017,7 @@ OUTPUT<
[51] withr_3.0.0 KEGGREST_1.44.0
[53] BumpyMatrix_1.12.0 Rtsne_0.17
[55] evaluate_0.23 BiocFileCache_2.12.0
- [57] ExperimentHub_2.12.0 Biostrings_2.72.0
+ [57] ExperimentHub_2.12.0 Biostrings_2.72.1
[59] pillar_1.9.0 BiocManager_1.30.23
[61] filelock_1.0.3 renv_1.0.7
[63] generics_0.1.3 BiocVersion_3.19.1
@@ -4032,10 +4032,10 @@ OUTPUT<
[81] BiocSingular_1.20.0 vipor_0.4.7
[83] cli_3.6.2 rsvd_1.0.5
[85] rappdirs_0.3.3 fansi_1.0.6
- [87] viridisLite_0.4.2 S4Arrays_1.4.0
+ [87] viridisLite_0.4.2 S4Arrays_1.4.1
[89] dplyr_1.1.4 ResidualMatrix_1.14.0
[91] gtable_0.3.5 digest_0.6.35
- [93] dqrng_0.4.0 SparseArray_1.4.3
+ [93] dqrng_0.4.1 SparseArray_1.4.8
[95] ggrepel_0.9.5 farver_2.1.2
[97] rjson_0.2.21 memoise_2.0.1
[99] htmltools_0.5.8.1 lifecycle_1.0.4
@@ -4179,7 +4179,7 @@ Key PointsContent from Working with large data
-Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -4641,7 +4646,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -4676,7 +4681,7 @@ Singular value decomposition
+
R
@@ -4702,7 +4707,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -4750,7 +4755,7 @@ Seurat
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -4772,7 +4777,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -4780,7 +4785,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -4789,7 +4794,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -4803,7 +4808,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -4814,7 +4819,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -4847,7 +4852,7 @@ Scanpy
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -4857,7 +4862,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -4883,7 +4888,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
@@ -4898,7 +4903,7 @@ RSession Info
-
+
R
@@ -4938,11 +4943,11 @@ OUTPUT<
[13] ggplot2_3.5.1 scuttle_1.14.0
[15] TENxBrainData_1.24.0 HDF5Array_1.32.0
[17] rhdf5_2.48.0 DelayedArray_0.30.1
-[19] SparseArray_1.4.3 S4Arrays_1.4.0
+[19] SparseArray_1.4.8 S4Arrays_1.4.1
[21] abind_1.4-5 Matrix_1.7-0
[23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[25] Biobase_2.64.0 GenomicRanges_1.56.0
-[27] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[27] GenomeInfoDb_1.40.1 IRanges_2.38.0
[29] S4Vectors_0.42.0 BiocGenerics_0.50.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] BiocStyle_2.32.0
@@ -4959,16 +4964,16 @@ OUTPUT<
[17] rmarkdown_2.27 yaml_2.3.8
[19] metapod_1.12.0 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.10-0
- [23] spatstat.sparse_3.0-3 reticulate_1.36.1
+ [23] spatstat.sparse_3.0-3 reticulate_1.37.0
[25] pbapply_1.7-2 cowplot_1.1.3
- [27] DBI_1.2.2 RColorBrewer_1.1-3
+ [27] DBI_1.2.3 RColorBrewer_1.1-3
[29] zlibbioc_1.50.0 Rtsne_0.17
[31] purrr_1.0.2 BumpyMatrix_1.12.0
[33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12
[35] ggrepel_0.9.5 irlba_2.3.5.1
[37] spatstat.utils_3.0-4 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
- [41] spatstat.random_3.2-3 dqrng_0.4.0
+ [41] spatstat.random_3.2-3 dqrng_0.4.1
[43] fitdistrplus_1.1-11 parallelly_1.37.1
[45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1
[47] codetools_0.2-20 tidyselect_1.2.1
@@ -4987,7 +4992,7 @@ OUTPUT<
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 spatstat.data_3.0-4
- [79] RSQLite_2.3.6 tidyr_1.3.1
+ [79] RSQLite_2.3.7 tidyr_1.3.1
[81] utf8_1.2.4 generics_0.1.3
[83] data.table_1.15.4 renv_1.0.7
[85] htmlwidgets_1.6.4 httr_1.4.7
@@ -4996,7 +5001,7 @@ OUTPUT<
[91] lmtest_0.9-40 XVector_0.44.0
[93] htmltools_0.5.8.1 dotCall64_1.1-1
[95] scales_1.3.0 png_0.1-8
- [97] knitr_1.46 reshape2_1.4.4
+ [97] knitr_1.47 reshape2_1.4.4
[99] rjson_0.2.21 nlme_3.1-164
[101] curl_5.2.1 cachem_1.1.0
[103] zoo_1.8-12 stringr_1.5.1
@@ -5015,7 +5020,7 @@ OUTPUT<
[129] plyr_1.8.9 ggbeeswarm_0.7.2
[131] stringi_1.8.4 deldir_2.0-4
[133] viridisLite_0.4.2 munsell_0.5.1
-[135] Biostrings_2.72.0 lazyeval_0.2.2
+[135] Biostrings_2.72.1 lazyeval_0.2.2
[137] spatstat.geom_3.2-9 dir.expiry_1.12.0
[139] ExperimentHub_2.12.0 RcppHNSW_0.6.0
[141] patchwork_1.2.0 sparseMatrixStats_1.16.0
@@ -5202,7 +5207,7 @@ Key PointsContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 4db20fd..1bf40c7 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -280,7 +280,7 @@
Cell type annotation
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -1252,7 +1252,7 @@ OUTPUT<
[13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0
[15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[17] Biobase_2.64.0 GenomicRanges_1.56.0
-[19] GenomeInfoDb_1.40.0 IRanges_2.38.0
+[19] GenomeInfoDb_1.40.1 IRanges_2.38.0
[21] S4Vectors_0.42.0 BiocGenerics_0.50.0
[23] MatrixGenerics_1.16.0 matrixStats_1.3.0
[25] AUCell_1.26.0 BiocStyle_2.32.0
@@ -1264,24 +1264,24 @@ OUTPUT<
[7] rmarkdown_2.27 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1
- [13] S4Arrays_1.4.0 AnnotationHub_3.12.0
+ [13] S4Arrays_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
- [17] SparseArray_1.4.3 htmlwidgets_1.6.4
+ [17] SparseArray_1.4.8 htmlwidgets_1.6.4
[19] plotly_4.10.4 cachem_1.1.0
[21] igraph_2.0.3 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-0
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.35
- [31] colorspace_2.1-0 dqrng_0.4.0
+ [31] colorspace_2.1-0 dqrng_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
- [35] RSQLite_2.3.6 beachmat_2.20.0
+ [35] RSQLite_2.3.7 beachmat_2.20.0
[37] labeling_0.4.3 filelock_1.0.3
[39] fansi_1.0.6 httr_1.4.7
[41] abind_1.4-5 compiler_4.4.0
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
- [47] DBI_1.2.2 highr_0.10
+ [47] DBI_1.2.3 highr_0.11
[49] R.utils_2.12.3 MASS_7.3-60.2
[51] rappdirs_0.3.3 DelayedArray_0.30.1
[53] rjson_0.2.21 tools_4.4.0
@@ -1301,7 +1301,7 @@ OUTPUT<
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.7 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
- [87] Biostrings_2.72.0 knitr_1.46
+ [87] Biostrings_2.72.1 knitr_1.47
[89] gridExtra_2.3 edgeR_4.2.0
[91] xfun_0.44 mixtools_2.0.0
[93] statmod_1.5.0 UCSC.utils_1.0.0
@@ -1561,7 +1561,7 @@ Key Points
Exploratory data analysis and quality control
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -459,7 +459,7 @@ OUTPUT<
Setting the Random Seed
-
+
Whenever your code involves the generation of random numbers, it’s a
good practice to set the random seed in R with
@@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-
+
First subset the object to include only highly variable genes
(sce2 <- sce[hvg.sce.var,]
) and then apply the
@@ -1399,7 +1399,7 @@
Further Reading
Accessing data from the Human Cell Atlas (HCA)
- Last updated on 2024-05-29 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -977,7 +977,7 @@ Key Points
Introduction to Bioconductor and the SingleCellExperiment class
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -717,7 +717,7 @@ Exercise 1
Give me a hint
-
+
The SingleCellExperiment
constructor function can be
used to create a new SingleCellExperiment
object.
@@ -836,7 +836,7 @@ Key Points
Working with large data
- Last updated on 2024-05-23 |
+
Last updated on 2024-06-04 |
Edit this page
@@ -716,21 +716,26 @@ OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+
+
+ERROR
+
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the
search. Thus, it may not actually be faster than the default exact
@@ -739,7 +744,7 @@
OUTPUT<
also not difficult to find situations where the approximation
deteriorates, especially at high dimensions, though this may not have an
appreciable impact on the biological conclusions.
-
+
R
@@ -773,7 +778,7 @@ Singular value decomposition
+
R
@@ -799,7 +804,7 @@ OUTPUT<
.. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ...
.. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
-
+
R
@@ -845,7 +850,7 @@ Seurat<
types of single-cell data. Seurat is developed and maintained by the Satija lab and is
released under the MIT
license.
-
+
R
@@ -867,7 +872,7 @@ RSeuratData package,
which is available from GitHub only.
-
+
R
@@ -875,7 +880,7 @@ R
We then proceed by loading all required packages and installing the
PBMC dataset:
-
+
R
@@ -884,7 +889,7 @@ R
We then load the dataset as an SeuratObject
and convert
it to a SingleCellExperiment
.
-
+
R
@@ -898,7 +903,7 @@ RSeurat also allows conversion from SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type
chimera mouse gastrulation dataset.
-
+
R
@@ -909,7 +914,7 @@ R
After some processing of the dataset, the actual conversion is
carried out with the as.Seurat
function.
-
+
R
@@ -941,7 +946,7 @@ Scanpy<
to enable users and developers to easily move data between these
frameworks to construct a multi-language analysis pipeline across
R/Bioconductor and Python.
-
+
R
@@ -951,7 +956,7 @@ RSingleCellExperiment from an H5AD file. Here, we use an
example H5AD file contained in the zellkonverter
package.
-
+
R
@@ -977,7 +982,7 @@ OUTPUT<
We can also write a SingleCellExperiment
to an H5AD file
with the writeH5AD()
function. This is demonstrated below
on the wild-type chimera mouse gastrulation dataset.
-
+
R
OUTPUT<
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
- 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
- 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
- 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1
- 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0
- 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
- 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
+ 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0
+ 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0
+ 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0
+ 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0
+ 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0
+ 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
- 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
- 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
- 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0
- 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
- 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
- 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0
+ 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0
+ 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0
+ 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0
+ 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
+ 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0
+ERROR +
+Error: rand > 0.85 is not TRUE
R
@@ -4669,7 +4674,7 @@Singular value decomposition +
-R
@@ -4695,7 +4700,7 @@OUTPUT< .. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ... .. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
+R
@@ -4743,7 +4748,7 @@Seurat types of single-cell data. Seurat is developed and maintained by the Satija lab and is released under the MIT license. -
+R
@@ -4765,7 +4770,7 @@RSeuratData package, which is available from GitHub only. -
+R
@@ -4773,7 +4778,7 @@R
We then proceed by loading all required packages and installing the PBMC dataset:
-+R
@@ -4782,7 +4787,7 @@R
We then load the dataset as an
-SeuratObject
and convert it to aSingleCellExperiment
.+R
@@ -4796,7 +4801,7 @@RSeurat also allows conversion from
SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type chimera mouse gastrulation dataset. -+R
@@ -4807,7 +4812,7 @@R
After some processing of the dataset, the actual conversion is carried out with the
-as.Seurat
function.+R
@@ -4840,7 +4845,7 @@Scanpy to enable users and developers to easily move data between these frameworks to construct a multi-language analysis pipeline across R/Bioconductor and Python. -
+R
@@ -4850,7 +4855,7 @@RSingleCellExperiment from an H5AD file. Here, we use an example H5AD file contained in the zellkonverter package. -
+R
@@ -4876,7 +4881,7 @@OUTPUT<
We can also write a
-SingleCellExperiment
to an H5AD file with thewriteH5AD()
function. This is demonstrated below on the wild-type chimera mouse gastrulation dataset.+R
@@ -4891,7 +4896,7 @@R
Session Info
-+R
@@ -4931,11 +4936,11 @@OUTPUT< [13] ggplot2_3.5.1 scuttle_1.14.0 [15] TENxBrainData_1.24.0 HDF5Array_1.32.0 [17] rhdf5_2.48.0 DelayedArray_0.30.1 -[19] SparseArray_1.4.3 S4Arrays_1.4.0 +[19] SparseArray_1.4.8 S4Arrays_1.4.1 [21] abind_1.4-5 Matrix_1.7-0 [23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 [25] Biobase_2.64.0 GenomicRanges_1.56.0 -[27] GenomeInfoDb_1.40.0 IRanges_2.38.0 +[27] GenomeInfoDb_1.40.1 IRanges_2.38.0 [29] S4Vectors_0.42.0 BiocGenerics_0.50.0 [31] MatrixGenerics_1.16.0 matrixStats_1.3.0 [33] BiocStyle_2.32.0 @@ -4952,16 +4957,16 @@
OUTPUT< [17] rmarkdown_2.27 yaml_2.3.8 [19] metapod_1.12.0 httpuv_1.6.15 [21] sctransform_0.4.1 spam_2.10-0 - [23] spatstat.sparse_3.0-3 reticulate_1.36.1 + [23] spatstat.sparse_3.0-3 reticulate_1.37.0 [25] pbapply_1.7-2 cowplot_1.1.3 - [27] DBI_1.2.2 RColorBrewer_1.1-3 + [27] DBI_1.2.3 RColorBrewer_1.1-3 [29] zlibbioc_1.50.0 Rtsne_0.17 [31] purrr_1.0.2 BumpyMatrix_1.12.0 [33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12 [35] ggrepel_0.9.5 irlba_2.3.5.1 [37] spatstat.utils_3.0-4 listenv_0.9.1 [39] goftest_1.2-3 RSpectra_0.16-1 - [41] spatstat.random_3.2-3 dqrng_0.4.0 + [41] spatstat.random_3.2-3 dqrng_0.4.1 [43] fitdistrplus_1.1-11 parallelly_1.37.1 [45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1 [47] codetools_0.2-20 tidyselect_1.2.1 @@ -4980,7 +4985,7 @@
OUTPUT< [73] R6_2.5.1 mime_0.12 [75] colorspace_2.1-0 scattermore_1.2 [77] tensor_1.5 spatstat.data_3.0-4 - [79] RSQLite_2.3.6 tidyr_1.3.1 + [79] RSQLite_2.3.7 tidyr_1.3.1 [81] utf8_1.2.4 generics_0.1.3 [83] data.table_1.15.4 renv_1.0.7 [85] htmlwidgets_1.6.4 httr_1.4.7 @@ -4989,7 +4994,7 @@
OUTPUT< [91] lmtest_0.9-40 XVector_0.44.0 [93] htmltools_0.5.8.1 dotCall64_1.1-1 [95] scales_1.3.0 png_0.1-8 - [97] knitr_1.46 reshape2_1.4.4 + [97] knitr_1.47 reshape2_1.4.4 [99] rjson_0.2.21 nlme_3.1-164 [101] curl_5.2.1 cachem_1.1.0 [103] zoo_1.8-12 stringr_1.5.1 @@ -5008,7 +5013,7 @@
OUTPUT< [129] plyr_1.8.9 ggbeeswarm_0.7.2 [131] stringi_1.8.4 deldir_2.0-4 [133] viridisLite_0.4.2 munsell_0.5.1 -[135] Biostrings_2.72.0 lazyeval_0.2.2 +[135] Biostrings_2.72.1 lazyeval_0.2.2 [137] spatstat.geom_3.2-9 dir.expiry_1.12.0 [139] ExperimentHub_2.12.0 RcppHNSW_0.6.0 [141] patchwork_1.2.0 sparseMatrixStats_1.16.0 @@ -5195,7 +5200,7 @@
Key Points
Content from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 | +
Last updated on 2024-06-04 | Edit this page
diff --git a/cell_type_annotation.html b/cell_type_annotation.html index ed861c4..6801350 100644 --- a/cell_type_annotation.html +++ b/cell_type_annotation.html @@ -280,7 +280,7 @@
Cell type annotation
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
@@ -1250,7 +1250,7 @@OUTPUT< [13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0 [15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 [17] Biobase_2.64.0 GenomicRanges_1.56.0 -[19] GenomeInfoDb_1.40.0 IRanges_2.38.0 +[19] GenomeInfoDb_1.40.1 IRanges_2.38.0 [21] S4Vectors_0.42.0 BiocGenerics_0.50.0 [23] MatrixGenerics_1.16.0 matrixStats_1.3.0 [25] AUCell_1.26.0 BiocStyle_2.32.0 @@ -1262,24 +1262,24 @@
OUTPUT< [7] rmarkdown_2.27 zlibbioc_1.50.0 [9] vctrs_0.6.5 memoise_2.0.1 [11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1 - [13] S4Arrays_1.4.0 AnnotationHub_3.12.0 + [13] S4Arrays_1.4.1 AnnotationHub_3.12.0 [15] curl_5.2.1 BiocNeighbors_1.22.0 - [17] SparseArray_1.4.3 htmlwidgets_1.6.4 + [17] SparseArray_1.4.8 htmlwidgets_1.6.4 [19] plotly_4.10.4 cachem_1.1.0 [21] igraph_2.0.3 mime_0.12 [23] lifecycle_1.0.4 pkgconfig_2.0.3 [25] rsvd_1.0.5 Matrix_1.7-0 [27] R6_2.5.1 fastmap_1.2.0 [29] GenomeInfoDbData_1.2.12 digest_0.6.35 - [31] colorspace_2.1-0 dqrng_0.4.0 + [31] colorspace_2.1-0 dqrng_0.4.1 [33] irlba_2.3.5.1 ExperimentHub_2.12.0 - [35] RSQLite_2.3.6 beachmat_2.20.0 + [35] RSQLite_2.3.7 beachmat_2.20.0 [37] labeling_0.4.3 filelock_1.0.3 [39] fansi_1.0.6 httr_1.4.7 [41] abind_1.4-5 compiler_4.4.0 [43] bit64_4.0.5 withr_3.0.0 [45] BiocParallel_1.38.0 viridis_0.6.5 - [47] DBI_1.2.2 highr_0.10 + [47] DBI_1.2.3 highr_0.11 [49] R.utils_2.12.3 MASS_7.3-60.2 [51] rappdirs_0.3.3 DelayedArray_0.30.1 [53] rjson_0.2.21 tools_4.4.0 @@ -1299,7 +1299,7 @@
OUTPUT< [81] survival_3.6-4 FNN_1.1.4 [83] renv_1.0.7 bit_4.0.5 [85] tidyselect_1.2.1 locfit_1.5-9.9 - [87] Biostrings_2.72.0 knitr_1.46 + [87] Biostrings_2.72.1 knitr_1.47 [89] gridExtra_2.3 edgeR_4.2.0 [91] xfun_0.44 mixtools_2.0.0 [93] statmod_1.5.0 UCSC.utils_1.0.0 @@ -1559,7 +1559,7 @@
Key Points
Exploratory data analysis and quality control
-Last updated on 2024-05-29 | +
Last updated on 2024-06-04 | Edit this page
@@ -457,7 +457,7 @@OUTPUT<
-
Setting the Random Seed+Whenever your code involves the generation of random numbers, it’s a good practice to set the random seed in R with @@ -1244,7 +1244,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-+First subset the object to include only highly variable genes (
sce2 <- sce[hvg.sce.var,]
) and then apply the @@ -1397,7 +1397,7 @@Further Reading
Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 | +
Last updated on 2024-06-04 | Edit this page
@@ -975,7 +975,7 @@Key Points
Contributor Code of Conduct
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
@@ -347,7 +347,7 @@Contributor Code of Conduct
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html", "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html", "dateCreated": "2024-01-10", - "dateModified": "2024-05-23", + "dateModified": "2024-06-04", "datePublished": "2024-06-04" } diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html index d24049f..80fd3f9 100644 --- a/instructor/LICENSE.html +++ b/instructor/LICENSE.html @@ -262,7 +262,7 @@
Licenses
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
@@ -398,7 +398,7 @@Licenses
"url": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html", "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html", "dateCreated": "2024-01-10", - "dateModified": "2024-05-23", + "dateModified": "2024-06-04", "datePublished": "2024-06-04" } diff --git a/instructor/aio.html b/instructor/aio.html index 464f60c..c1d6c00 100644 --- a/instructor/aio.html +++ b/instructor/aio.html @@ -312,7 +312,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
Estimated time: 30 minutes
@@ -761,7 +761,7 @@Exercise 1
Give me a hint
-+The
@@ -828,7 +828,7 @@SingleCellExperiment
constructor function can be used to create a newSingleCellExperiment
object.Key Points
Content from Exploratory data analysis and quality control
-Last updated on 2024-05-29 | +
Last updated on 2024-06-04 | Edit this page
Estimated time: 45 minutes
@@ -1009,7 +1009,7 @@OUTPUT<
-
Setting the Random Seed+Whenever your code involves the generation of random numbers, it’s a good practice to set the random seed in R with @@ -1817,7 +1817,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-++First subset the object to include only highly variable genes (
sce2 <- sce[hvg.sce.var,]
) and then apply the @@ -1917,7 +1917,7 @@Further Reading
Content from Cell type annotation
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
Estimated time: 45 minutes
@@ -2896,7 +2896,7 @@OUTPUT< [13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0 [15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 [17] Biobase_2.64.0 GenomicRanges_1.56.0 -[19] GenomeInfoDb_1.40.0 IRanges_2.38.0 +[19] GenomeInfoDb_1.40.1 IRanges_2.38.0 [21] S4Vectors_0.42.0 BiocGenerics_0.50.0 [23] MatrixGenerics_1.16.0 matrixStats_1.3.0 [25] AUCell_1.26.0 BiocStyle_2.32.0 @@ -2908,24 +2908,24 @@
OUTPUT< [7] rmarkdown_2.27 zlibbioc_1.50.0 [9] vctrs_0.6.5 memoise_2.0.1 [11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1 - [13] S4Arrays_1.4.0 AnnotationHub_3.12.0 + [13] S4Arrays_1.4.1 AnnotationHub_3.12.0 [15] curl_5.2.1 BiocNeighbors_1.22.0 - [17] SparseArray_1.4.3 htmlwidgets_1.6.4 + [17] SparseArray_1.4.8 htmlwidgets_1.6.4 [19] plotly_4.10.4 cachem_1.1.0 [21] igraph_2.0.3 mime_0.12 [23] lifecycle_1.0.4 pkgconfig_2.0.3 [25] rsvd_1.0.5 Matrix_1.7-0 [27] R6_2.5.1 fastmap_1.2.0 [29] GenomeInfoDbData_1.2.12 digest_0.6.35 - [31] colorspace_2.1-0 dqrng_0.4.0 + [31] colorspace_2.1-0 dqrng_0.4.1 [33] irlba_2.3.5.1 ExperimentHub_2.12.0 - [35] RSQLite_2.3.6 beachmat_2.20.0 + [35] RSQLite_2.3.7 beachmat_2.20.0 [37] labeling_0.4.3 filelock_1.0.3 [39] fansi_1.0.6 httr_1.4.7 [41] abind_1.4-5 compiler_4.4.0 [43] bit64_4.0.5 withr_3.0.0 [45] BiocParallel_1.38.0 viridis_0.6.5 - [47] DBI_1.2.2 highr_0.10 + [47] DBI_1.2.3 highr_0.11 [49] R.utils_2.12.3 MASS_7.3-60.2 [51] rappdirs_0.3.3 DelayedArray_0.30.1 [53] rjson_0.2.21 tools_4.4.0 @@ -2945,7 +2945,7 @@
OUTPUT< [81] survival_3.6-4 FNN_1.1.4 [83] renv_1.0.7 bit_4.0.5 [85] tidyselect_1.2.1 locfit_1.5-9.9 - [87] Biostrings_2.72.0 knitr_1.46 + [87] Biostrings_2.72.1 knitr_1.47 [89] gridExtra_2.3 edgeR_4.2.0 [91] xfun_0.44 mixtools_2.0.0 [93] statmod_1.5.0 UCSC.utils_1.0.0 @@ -3154,7 +3154,7 @@
Key Points
Content from Multi-sample analyses
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
Estimated time: 45 minutes
@@ -3983,16 +3983,16 @@OUTPUT< [7] scuttle_1.14.0 MouseGastrulationData_1.18.0 [9] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0 [11] SummarizedExperiment_1.34.0 Biobase_2.64.0 -[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0 +[13] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1 [15] IRanges_2.38.0 S4Vectors_0.42.0 [17] BiocGenerics_0.50.0 MatrixGenerics_1.16.0 [19] matrixStats_1.3.0 BiocStyle_2.32.0 loaded via a namespace (and not attached): - [1] DBI_1.2.2 formatR_1.14 + [1] DBI_1.2.3 formatR_1.14 [3] gridExtra_2.3 rlang_1.1.3 [5] magrittr_2.0.3 compiler_4.4.0 - [7] RSQLite_2.3.6 DelayedMatrixStats_1.26.0 + [7] RSQLite_2.3.7 DelayedMatrixStats_1.26.0 [9] png_0.1-8 vctrs_0.6.5 [11] pkgconfig_2.0.3 crayon_1.5.2 [13] fastmap_1.2.0 dbplyr_2.5.0 @@ -4004,11 +4004,11 @@
OUTPUT< [25] xfun_0.44 zlibbioc_1.50.0 [27] cachem_1.1.0 beachmat_2.20.0 [29] jsonlite_1.8.8 blob_1.2.4 - [31] highr_0.10 DelayedArray_0.30.1 + [31] highr_0.11 DelayedArray_0.30.1 [33] BiocParallel_1.38.0 cluster_2.1.6 [35] irlba_2.3.5.1 parallel_4.4.0 [37] R6_2.5.1 Rcpp_1.0.12 - [39] knitr_1.46 splines_4.4.0 + [39] knitr_1.47 splines_4.4.0 [41] igraph_2.0.3 Matrix_1.7-0 [43] tidyselect_1.2.1 viridis_0.6.5 [45] abind_1.4-5 yaml_2.3.8 @@ -4017,7 +4017,7 @@
OUTPUT< [51] withr_3.0.0 KEGGREST_1.44.0 [53] BumpyMatrix_1.12.0 Rtsne_0.17 [55] evaluate_0.23 BiocFileCache_2.12.0 - [57] ExperimentHub_2.12.0 Biostrings_2.72.0 + [57] ExperimentHub_2.12.0 Biostrings_2.72.1 [59] pillar_1.9.0 BiocManager_1.30.23 [61] filelock_1.0.3 renv_1.0.7 [63] generics_0.1.3 BiocVersion_3.19.1 @@ -4032,10 +4032,10 @@
OUTPUT< [81] BiocSingular_1.20.0 vipor_0.4.7 [83] cli_3.6.2 rsvd_1.0.5 [85] rappdirs_0.3.3 fansi_1.0.6 - [87] viridisLite_0.4.2 S4Arrays_1.4.0 + [87] viridisLite_0.4.2 S4Arrays_1.4.1 [89] dplyr_1.1.4 ResidualMatrix_1.14.0 [91] gtable_0.3.5 digest_0.6.35 - [93] dqrng_0.4.0 SparseArray_1.4.3 + [93] dqrng_0.4.1 SparseArray_1.4.8 [95] ggrepel_0.9.5 farver_2.1.2 [97] rjson_0.2.21 memoise_2.0.1 [99] htmltools_0.5.8.1 lifecycle_1.0.4 @@ -4179,7 +4179,7 @@
Key Points
Content from Working with large data
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
Estimated time: 12 minutes
@@ -4618,21 +4618,26 @@OUTPUT<
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0 + 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0 + 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0 + 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0 + 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 + 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0 +approx exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 - 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1 - 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0 - 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1 - 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0 - 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0 - 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0 + 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0 + 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0 + 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0 + 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0 + 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0 + 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0 + 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0 8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0 9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0 - 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0 - 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0 - 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0 - 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0 - 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 - 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ERROR +
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the search. Thus, it may not actually be faster than the default exact @@ -4641,7 +4646,7 @@
OUTPUT< also not difficult to find situations where the approximation deteriorates, especially at high dimensions, though this may not have an appreciable impact on the biological conclusions. -
+R
@@ -4676,7 +4681,7 @@Singular value decomposition +
-R
@@ -4702,7 +4707,7 @@OUTPUT< .. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ... .. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
+R
@@ -4750,7 +4755,7 @@Seurat types of single-cell data. Seurat is developed and maintained by the Satija lab and is released under the MIT license. -
+R
@@ -4772,7 +4777,7 @@RSeuratData package, which is available from GitHub only. -
+R
@@ -4780,7 +4785,7 @@R
We then proceed by loading all required packages and installing the PBMC dataset:
-+R
@@ -4789,7 +4794,7 @@R
We then load the dataset as an
-SeuratObject
and convert it to aSingleCellExperiment
.+R
@@ -4803,7 +4808,7 @@RSeurat also allows conversion from
SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type chimera mouse gastrulation dataset. -+R
@@ -4814,7 +4819,7 @@R
After some processing of the dataset, the actual conversion is carried out with the
-as.Seurat
function.+R
@@ -4847,7 +4852,7 @@Scanpy to enable users and developers to easily move data between these frameworks to construct a multi-language analysis pipeline across R/Bioconductor and Python. -
+R
@@ -4857,7 +4862,7 @@RSingleCellExperiment from an H5AD file. Here, we use an example H5AD file contained in the zellkonverter package. -
+R
@@ -4883,7 +4888,7 @@OUTPUT<
We can also write a
-SingleCellExperiment
to an H5AD file with thewriteH5AD()
function. This is demonstrated below on the wild-type chimera mouse gastrulation dataset.+R
@@ -4898,7 +4903,7 @@R
Session Info
-+R
@@ -4938,11 +4943,11 @@OUTPUT< [13] ggplot2_3.5.1 scuttle_1.14.0 [15] TENxBrainData_1.24.0 HDF5Array_1.32.0 [17] rhdf5_2.48.0 DelayedArray_0.30.1 -[19] SparseArray_1.4.3 S4Arrays_1.4.0 +[19] SparseArray_1.4.8 S4Arrays_1.4.1 [21] abind_1.4-5 Matrix_1.7-0 [23] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 [25] Biobase_2.64.0 GenomicRanges_1.56.0 -[27] GenomeInfoDb_1.40.0 IRanges_2.38.0 +[27] GenomeInfoDb_1.40.1 IRanges_2.38.0 [29] S4Vectors_0.42.0 BiocGenerics_0.50.0 [31] MatrixGenerics_1.16.0 matrixStats_1.3.0 [33] BiocStyle_2.32.0 @@ -4959,16 +4964,16 @@
OUTPUT< [17] rmarkdown_2.27 yaml_2.3.8 [19] metapod_1.12.0 httpuv_1.6.15 [21] sctransform_0.4.1 spam_2.10-0 - [23] spatstat.sparse_3.0-3 reticulate_1.36.1 + [23] spatstat.sparse_3.0-3 reticulate_1.37.0 [25] pbapply_1.7-2 cowplot_1.1.3 - [27] DBI_1.2.2 RColorBrewer_1.1-3 + [27] DBI_1.2.3 RColorBrewer_1.1-3 [29] zlibbioc_1.50.0 Rtsne_0.17 [31] purrr_1.0.2 BumpyMatrix_1.12.0 [33] rappdirs_0.3.3 GenomeInfoDbData_1.2.12 [35] ggrepel_0.9.5 irlba_2.3.5.1 [37] spatstat.utils_3.0-4 listenv_0.9.1 [39] goftest_1.2-3 RSpectra_0.16-1 - [41] spatstat.random_3.2-3 dqrng_0.4.0 + [41] spatstat.random_3.2-3 dqrng_0.4.1 [43] fitdistrplus_1.1-11 parallelly_1.37.1 [45] DelayedMatrixStats_1.26.0 leiden_0.4.3.1 [47] codetools_0.2-20 tidyselect_1.2.1 @@ -4987,7 +4992,7 @@
OUTPUT< [73] R6_2.5.1 mime_0.12 [75] colorspace_2.1-0 scattermore_1.2 [77] tensor_1.5 spatstat.data_3.0-4 - [79] RSQLite_2.3.6 tidyr_1.3.1 + [79] RSQLite_2.3.7 tidyr_1.3.1 [81] utf8_1.2.4 generics_0.1.3 [83] data.table_1.15.4 renv_1.0.7 [85] htmlwidgets_1.6.4 httr_1.4.7 @@ -4996,7 +5001,7 @@
OUTPUT< [91] lmtest_0.9-40 XVector_0.44.0 [93] htmltools_0.5.8.1 dotCall64_1.1-1 [95] scales_1.3.0 png_0.1-8 - [97] knitr_1.46 reshape2_1.4.4 + [97] knitr_1.47 reshape2_1.4.4 [99] rjson_0.2.21 nlme_3.1-164 [101] curl_5.2.1 cachem_1.1.0 [103] zoo_1.8-12 stringr_1.5.1 @@ -5015,7 +5020,7 @@
OUTPUT< [129] plyr_1.8.9 ggbeeswarm_0.7.2 [131] stringi_1.8.4 deldir_2.0-4 [133] viridisLite_0.4.2 munsell_0.5.1 -[135] Biostrings_2.72.0 lazyeval_0.2.2 +[135] Biostrings_2.72.1 lazyeval_0.2.2 [137] spatstat.geom_3.2-9 dir.expiry_1.12.0 [139] ExperimentHub_2.12.0 RcppHNSW_0.6.0 [141] patchwork_1.2.0 sparseMatrixStats_1.16.0 @@ -5202,7 +5207,7 @@
Key Points
Content from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 | +
Last updated on 2024-06-04 | Edit this page
Estimated time: 30 minutes
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html index 4db20fd..1bf40c7 100644 --- a/instructor/cell_type_annotation.html +++ b/instructor/cell_type_annotation.html @@ -280,7 +280,7 @@
Cell type annotation
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
@@ -1252,7 +1252,7 @@OUTPUT< [13] MouseGastrulationData_1.18.0 SpatialExperiment_1.14.0 [15] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 [17] Biobase_2.64.0 GenomicRanges_1.56.0 -[19] GenomeInfoDb_1.40.0 IRanges_2.38.0 +[19] GenomeInfoDb_1.40.1 IRanges_2.38.0 [21] S4Vectors_0.42.0 BiocGenerics_0.50.0 [23] MatrixGenerics_1.16.0 matrixStats_1.3.0 [25] AUCell_1.26.0 BiocStyle_2.32.0 @@ -1264,24 +1264,24 @@
OUTPUT< [7] rmarkdown_2.27 zlibbioc_1.50.0 [9] vctrs_0.6.5 memoise_2.0.1 [11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1 - [13] S4Arrays_1.4.0 AnnotationHub_3.12.0 + [13] S4Arrays_1.4.1 AnnotationHub_3.12.0 [15] curl_5.2.1 BiocNeighbors_1.22.0 - [17] SparseArray_1.4.3 htmlwidgets_1.6.4 + [17] SparseArray_1.4.8 htmlwidgets_1.6.4 [19] plotly_4.10.4 cachem_1.1.0 [21] igraph_2.0.3 mime_0.12 [23] lifecycle_1.0.4 pkgconfig_2.0.3 [25] rsvd_1.0.5 Matrix_1.7-0 [27] R6_2.5.1 fastmap_1.2.0 [29] GenomeInfoDbData_1.2.12 digest_0.6.35 - [31] colorspace_2.1-0 dqrng_0.4.0 + [31] colorspace_2.1-0 dqrng_0.4.1 [33] irlba_2.3.5.1 ExperimentHub_2.12.0 - [35] RSQLite_2.3.6 beachmat_2.20.0 + [35] RSQLite_2.3.7 beachmat_2.20.0 [37] labeling_0.4.3 filelock_1.0.3 [39] fansi_1.0.6 httr_1.4.7 [41] abind_1.4-5 compiler_4.4.0 [43] bit64_4.0.5 withr_3.0.0 [45] BiocParallel_1.38.0 viridis_0.6.5 - [47] DBI_1.2.2 highr_0.10 + [47] DBI_1.2.3 highr_0.11 [49] R.utils_2.12.3 MASS_7.3-60.2 [51] rappdirs_0.3.3 DelayedArray_0.30.1 [53] rjson_0.2.21 tools_4.4.0 @@ -1301,7 +1301,7 @@
OUTPUT< [81] survival_3.6-4 FNN_1.1.4 [83] renv_1.0.7 bit_4.0.5 [85] tidyselect_1.2.1 locfit_1.5-9.9 - [87] Biostrings_2.72.0 knitr_1.46 + [87] Biostrings_2.72.1 knitr_1.47 [89] gridExtra_2.3 edgeR_4.2.0 [91] xfun_0.44 mixtools_2.0.0 [93] statmod_1.5.0 UCSC.utils_1.0.0 @@ -1561,7 +1561,7 @@
Key Points
Exploratory data analysis and quality control
-Last updated on 2024-05-29 | +
Last updated on 2024-06-04 | Edit this page
@@ -459,7 +459,7 @@OUTPUT<
-
Setting the Random Seed+Whenever your code involves the generation of random numbers, it’s a good practice to set the random seed in R with @@ -1246,7 +1246,7 @@
Exercise 2: Dimensionality Reduction
Give me a hint
-+First subset the object to include only highly variable genes (
sce2 <- sce[hvg.sce.var,]
) and then apply the @@ -1399,7 +1399,7 @@Further Reading
Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-05-29 | +
Last updated on 2024-06-04 | Edit this page
@@ -977,7 +977,7 @@Key Points
Introduction to Bioconductor and the SingleCellExperiment class
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
@@ -717,7 +717,7 @@Exercise 1
Give me a hint
-+The
@@ -836,7 +836,7 @@SingleCellExperiment
constructor function can be used to create a newSingleCellExperiment
object.Key Points
+Working with large data
-Last updated on 2024-05-23 | +
Last updated on 2024-06-04 | Edit this page
@@ -716,21 +716,26 @@OUTPUT<
+ 10 0 0 0 0 0 0 0 0 0 113 4 10 0 0 0 + 11 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0 + 12 0 0 0 0 1 0 0 0 0 0 0 203 0 0 0 + 13 0 0 0 0 0 0 0 0 0 0 6 0 0 146 0 + 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 + 15 0 0 0 51 0 0 0 0 0 0 0 0 0 0 0 +approx exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 - 1 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1 - 3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0 - 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 1 - 5 2 0 0 0 391 0 0 0 0 1 0 0 0 0 0 - 6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0 - 7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0 + 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0 + 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0 + 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0 + 4 0 0 0 203 0 0 0 0 0 0 0 0 143 0 0 + 5 0 0 0 0 395 0 1 0 0 1 0 1 0 0 0 + 6 0 0 0 0 0 81 127 0 0 0 1 0 0 0 0 + 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0 8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0 9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0 - 10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0 - 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0 - 12 0 0 0 0 3 0 0 0 0 0 0 214 0 0 0 - 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0 - 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 - 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55
+ERROR +
+Error: rand > 0.85 is not TRUE
Note that Annoy writes the NN index to disk prior to performing the search. Thus, it may not actually be faster than the default exact @@ -739,7 +744,7 @@
OUTPUT< also not difficult to find situations where the approximation deteriorates, especially at high dimensions, though this may not have an appreciable impact on the biological conclusions. -
+R
@@ -773,7 +778,7 @@Singular value decomposition +
-R
@@ -799,7 +804,7 @@OUTPUT< .. ..$ : chr [1:500] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919" "ENSMUSG00000048583" ... .. ..$ : chr [1:20] "PC1" "PC2" "PC3" "PC4" ...
+R
@@ -845,7 +850,7 @@Seurat< types of single-cell data. Seurat is developed and maintained by the Satija lab and is released under the MIT license. -
+R
@@ -867,7 +872,7 @@RSeuratData package, which is available from GitHub only. -
+R
@@ -875,7 +880,7 @@R
We then proceed by loading all required packages and installing the PBMC dataset:
-+R
@@ -884,7 +889,7 @@R
We then load the dataset as an
-SeuratObject
and convert it to aSingleCellExperiment
.+R
@@ -898,7 +903,7 @@RSeurat also allows conversion from
SingleCellExperiment
objects to Seurat objects; we demonstrate this here on the wild-type chimera mouse gastrulation dataset. -+R
@@ -909,7 +914,7 @@R
After some processing of the dataset, the actual conversion is carried out with the
-as.Seurat
function.+R
@@ -941,7 +946,7 @@Scanpy< to enable users and developers to easily move data between these frameworks to construct a multi-language analysis pipeline across R/Bioconductor and Python. -
+R
@@ -951,7 +956,7 @@RSingleCellExperiment from an H5AD file. Here, we use an example H5AD file contained in the zellkonverter package. -
+R
@@ -977,7 +982,7 @@OUTPUT<
We can also write a
-SingleCellExperiment
to an H5AD file with thewriteH5AD()
function. This is demonstrated below on the wild-type chimera mouse gastrulation dataset.+R