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Source  : 8833894
Branch  : main
Author  : Andrew Ghazi <[email protected]>
Time    : 2024-11-11 15:28:31 +0000
Message : comments
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234 changes: 116 additions & 118 deletions cell_type_annotation.md
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Expand Up @@ -98,17 +98,15 @@ which the cells truly reside. After annotation based on marker genes,
the clusters can be treated as proxies for more abstract biological
concepts such as cell types or states.

Popularized by its use in
[Seurat](https://cran.r-project.org/web/packages/Seurat/index.html),
graph-based clustering is a flexible and scalable technique for
clustering large scRNA-seq datasets. We first build a graph where each
node is a cell that is connected to its nearest neighbors in the
high-dimensional space. Edges are weighted based on the similarity
between the cells involved, with higher weight given to cells that are
more closely related. We then apply algorithms to identify "communities"
of cells that are more connected to cells in the same community than
they are to cells of different communities. Each community represents a
cluster that we can use for downstream interpretation.
Graph-based clustering is a flexible and scalable technique for identifying
coherent groups of cells in large scRNA-seq datasets. We first build a graph
where each node is a cell that is connected to its nearest neighbors in the
high-dimensional space. Edges are weighted based on the similarity between the
cells involved, with higher weight given to cells that are more closely related.
We then apply algorithms to identify "communities" of cells that are more
connected to cells in the same community than they are to cells of different
communities. Each community represents a cluster that we can use for downstream
interpretation.

Here, we use the `clusterCells()` function from the
[scran](https://bioconductor.org/packages/scran) package to perform
Expand Down Expand Up @@ -867,113 +865,6 @@ AUCell_exploreThresholds(cell.aucs[10:18], plotHist = TRUE, assign = TRUE)

::::

## Session Info


``` r
sessionInfo()
```

``` output
R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
time zone: UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] GSEABase_1.66.0 graph_1.82.0
[3] annotate_1.82.0 XML_3.99-0.16.1
[5] AnnotationDbi_1.66.0 pheatmap_1.0.12
[7] scran_1.32.0 scater_1.32.0
[9] ggplot2_3.5.1 scuttle_1.14.0
[11] bluster_1.14.0 SingleR_2.6.0
[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_1.40.1 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
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 jsonlite_1.8.8
[3] magrittr_2.0.3 ggbeeswarm_0.7.2
[5] magick_2.8.3 farver_2.1.2
[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_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
[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_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
[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_4.4.1
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
[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_4.4.1
[55] vipor_0.4.7 beeswarm_0.4.0
[57] R.oo_1.26.0 glue_1.7.0
[59] nlme_3.1-164 grid_4.4.1
[61] cluster_2.1.6 generics_0.1.3
[63] gtable_0.3.5 R.methodsS3_1.8.2
[65] tidyr_1.3.1 data.table_1.15.4
[67] BiocSingular_1.20.0 ScaledMatrix_1.12.0
[69] metapod_1.12.0 utf8_1.2.4
[71] XVector_0.44.0 ggrepel_0.9.5
[73] BiocVersion_3.19.1 pillar_1.9.0
[75] limma_3.60.2 BumpyMatrix_1.12.0
[77] splines_4.4.1 dplyr_1.1.4
[79] BiocFileCache_2.12.0 lattice_0.22-6
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.10 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
[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
[95] lazyeval_0.2.2 yaml_2.3.8
[97] evaluate_0.23 codetools_0.2-20
[99] kernlab_0.9-32 tibble_3.2.1
[101] BiocManager_1.30.23 cli_3.6.2
[103] uwot_0.2.2 xtable_1.8-4
[105] segmented_2.1-0 munsell_0.5.1
[107] Rcpp_1.0.12 dbplyr_2.5.0
[109] png_0.1-8 parallel_4.4.1
[111] blob_1.2.4 sparseMatrixStats_1.16.0
[113] viridisLite_0.4.2 scales_1.3.0
[115] purrr_1.0.2 crayon_1.5.2
[117] rlang_1.1.3 cowplot_1.1.3
[119] KEGGREST_1.44.0
```

## Exercises

::: challenge
Expand Down Expand Up @@ -1158,3 +1049,110 @@ Remember, this is an exploratory diagnostic, not the final word! At this point i
- The *[AUCell](https://bioconductor.org/packages/3.19/AUCell)* package provides an enrichment
test to identify curated marker sets that are highly expressed in each cell.
:::

## Session Info


``` r
sessionInfo()
```

``` output
R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
time zone: UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] GSEABase_1.66.0 graph_1.82.0
[3] annotate_1.82.0 XML_3.99-0.16.1
[5] AnnotationDbi_1.66.0 pheatmap_1.0.12
[7] scran_1.32.0 scater_1.32.0
[9] ggplot2_3.5.1 scuttle_1.14.0
[11] bluster_1.14.0 SingleR_2.6.0
[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_1.40.1 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
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 jsonlite_1.8.8
[3] magrittr_2.0.3 ggbeeswarm_0.7.2
[5] magick_2.8.3 farver_2.1.2
[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_1.4.1 AnnotationHub_3.12.0
[15] curl_5.2.1 BiocNeighbors_1.22.0
[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_0.4.1
[33] irlba_2.3.5.1 ExperimentHub_2.12.0
[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_4.4.1
[43] bit64_4.0.5 withr_3.0.0
[45] BiocParallel_1.38.0 viridis_0.6.5
[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_4.4.1
[55] vipor_0.4.7 beeswarm_0.4.0
[57] R.oo_1.26.0 glue_1.7.0
[59] nlme_3.1-164 grid_4.4.1
[61] cluster_2.1.6 generics_0.1.3
[63] gtable_0.3.5 R.methodsS3_1.8.2
[65] tidyr_1.3.1 data.table_1.15.4
[67] BiocSingular_1.20.0 ScaledMatrix_1.12.0
[69] metapod_1.12.0 utf8_1.2.4
[71] XVector_0.44.0 ggrepel_0.9.5
[73] BiocVersion_3.19.1 pillar_1.9.0
[75] limma_3.60.2 BumpyMatrix_1.12.0
[77] splines_4.4.1 dplyr_1.1.4
[79] BiocFileCache_2.12.0 lattice_0.22-6
[81] survival_3.6-4 FNN_1.1.4
[83] renv_1.0.11 bit_4.0.5
[85] tidyselect_1.2.1 locfit_1.5-9.9
[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
[95] lazyeval_0.2.2 yaml_2.3.8
[97] evaluate_0.23 codetools_0.2-20
[99] kernlab_0.9-32 tibble_3.2.1
[101] BiocManager_1.30.23 cli_3.6.2
[103] uwot_0.2.2 xtable_1.8-4
[105] segmented_2.1-0 munsell_0.5.1
[107] Rcpp_1.0.12 dbplyr_2.5.0
[109] png_0.1-8 parallel_4.4.1
[111] blob_1.2.4 sparseMatrixStats_1.16.0
[113] viridisLite_0.4.2 scales_1.3.0
[115] purrr_1.0.2 crayon_1.5.2
[117] rlang_1.1.3 formatR_1.14
[119] cowplot_1.1.3 KEGGREST_1.44.0
```
110 changes: 110 additions & 0 deletions eda_qc.md
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Expand Up @@ -1109,3 +1109,113 @@ Mathematically, this would require the data to fall on a two-dimensional plane (
[^2]: [Vallejos (2017)](learners/reference.md#litref)
[^3]: [Lun (2016)](learners/reference.md#litref)

## Session Info


``` r
sessionInfo()
```

``` output
R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
time zone: UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] scDblFinder_1.18.0 scran_1.32.0
[3] scater_1.32.0 scuttle_1.14.0
[5] EnsDb.Mmusculus.v79_2.99.0 ensembldb_2.28.0
[7] AnnotationFilter_1.28.0 GenomicFeatures_1.56.0
[9] AnnotationDbi_1.66.0 ggplot2_3.5.1
[11] DropletUtils_1.24.0 MouseGastrulationData_1.18.0
[13] SpatialExperiment_1.14.0 SingleCellExperiment_1.26.0
[15] SummarizedExperiment_1.34.0 Biobase_2.64.0
[17] GenomicRanges_1.56.0 GenomeInfoDb_1.40.1
[19] IRanges_2.38.0 S4Vectors_0.42.0
[21] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[23] matrixStats_1.3.0 BiocStyle_2.32.0
loaded via a namespace (and not attached):
[1] jsonlite_1.8.8 magrittr_2.0.3
[3] ggbeeswarm_0.7.2 magick_2.8.3
[5] farver_2.1.2 rmarkdown_2.27
[7] BiocIO_1.14.0 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] Rsamtools_2.20.0 DelayedMatrixStats_1.26.0
[13] RCurl_1.98-1.14 htmltools_0.5.8.1
[15] S4Arrays_1.4.1 AnnotationHub_3.12.0
[17] curl_5.2.1 BiocNeighbors_1.22.0
[19] xgboost_1.7.7.1 Rhdf5lib_1.26.0
[21] SparseArray_1.4.8 rhdf5_2.48.0
[23] cachem_1.1.0 GenomicAlignments_1.40.0
[25] igraph_2.0.3 mime_0.12
[27] lifecycle_1.0.4 pkgconfig_2.0.3
[29] rsvd_1.0.5 Matrix_1.7-0
[31] R6_2.5.1 fastmap_1.2.0
[33] GenomeInfoDbData_1.2.12 digest_0.6.35
[35] colorspace_2.1-0 dqrng_0.4.1
[37] irlba_2.3.5.1 ExperimentHub_2.12.0
[39] RSQLite_2.3.7 beachmat_2.20.0
[41] labeling_0.4.3 filelock_1.0.3
[43] fansi_1.0.6 httr_1.4.7
[45] abind_1.4-5 compiler_4.4.1
[47] bit64_4.0.5 withr_3.0.0
[49] BiocParallel_1.38.0 viridis_0.6.5
[51] DBI_1.2.3 highr_0.11
[53] HDF5Array_1.32.0 R.utils_2.12.3
[55] MASS_7.3-60.2 rappdirs_0.3.3
[57] DelayedArray_0.30.1 bluster_1.14.0
[59] rjson_0.2.21 tools_4.4.1
[61] vipor_0.4.7 beeswarm_0.4.0
[63] R.oo_1.26.0 glue_1.7.0
[65] restfulr_0.0.15 rhdf5filters_1.16.0
[67] grid_4.4.1 Rtsne_0.17
[69] cluster_2.1.6 generics_0.1.3
[71] gtable_0.3.5 R.methodsS3_1.8.2
[73] data.table_1.15.4 metapod_1.12.0
[75] BiocSingular_1.20.0 ScaledMatrix_1.12.0
[77] utf8_1.2.4 XVector_0.44.0
[79] ggrepel_0.9.5 BiocVersion_3.19.1
[81] pillar_1.9.0 limma_3.60.2
[83] BumpyMatrix_1.12.0 dplyr_1.1.4
[85] BiocFileCache_2.12.0 lattice_0.22-6
[87] FNN_1.1.4 renv_1.0.11
[89] rtracklayer_1.64.0 bit_4.0.5
[91] tidyselect_1.2.1 locfit_1.5-9.9
[93] Biostrings_2.72.1 knitr_1.47
[95] gridExtra_2.3 ProtGenerics_1.36.0
[97] edgeR_4.2.0 xfun_0.44
[99] statmod_1.5.0 UCSC.utils_1.0.0
[101] lazyeval_0.2.2 yaml_2.3.8
[103] evaluate_0.23 codetools_0.2-20
[105] tibble_3.2.1 BiocManager_1.30.23
[107] cli_3.6.2 uwot_0.2.2
[109] munsell_0.5.1 Rcpp_1.0.12
[111] dbplyr_2.5.0 png_0.1-8
[113] XML_3.99-0.16.1 parallel_4.4.1
[115] blob_1.2.4 sparseMatrixStats_1.16.0
[117] bitops_1.0-7 viridisLite_0.4.2
[119] scales_1.3.0 purrr_1.0.2
[121] crayon_1.5.2 rlang_1.1.3
[123] formatR_1.14 cowplot_1.1.3
[125] KEGGREST_1.44.0
```


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