From add5ddce63db870d8499080a4a889d3428a25559 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Mon, 7 Oct 2024 19:25:08 +0000 Subject: [PATCH] site deploy Auto-generated via {sandpaper} Source : 465803e8e8d894af4c7ce6b2121f0577eae3d5b3 Branch : md-outputs Author : GitHub Actions Time : 2024-10-07 19:23:59 +0000 Message : markdown source builds Auto-generated via {sandpaper} Source : 2fa80f3351595c803ba1740e2827868e5de1b882 Branch : main Author : Andrew Ghazi <6763470+andrewGhazi@users.noreply.github.com> Time : 2024-10-07 18:41:12 +0000 Message : Merge pull request #56 from ccb-hms/chimera_bg background on chimera --- aio.html | 123 +++++++++++++++------------ cell_type_annotation.html | 20 ++--- hca.html | 8 +- instructor/aio.html | 123 +++++++++++++++------------ instructor/cell_type_annotation.html | 20 ++--- instructor/hca.html | 8 +- instructor/intro-sce.html | 27 ++++-- instructor/large_data.html | 56 ++++++------ instructor/multi-sample.html | 16 ++-- intro-sce.html | 27 ++++-- large_data.html | 56 ++++++------ md5sum.txt | 4 +- multi-sample.html | 16 ++-- pkgdown.yml | 2 +- 14 files changed, 273 insertions(+), 233 deletions(-) diff --git a/aio.html b/aio.html index f8325a9..9aa787d 100644 --- a/aio.html +++ b/aio.html @@ -368,7 +368,7 @@

Content from Introduction to Bioconductor and the SingleCellExperiment class

-

Last updated on 2024-10-04 | +

Last updated on 2024-10-07 | Edit this page

@@ -570,9 +570,14 @@

Let’s look at an example dataset. WTChimeraData comes -from a study on mouse development. We can assign one sample to a -SingleCellExperiment object named sce like -so:

+from a study on mouse development Pijuan-Sala et +al.. The study profiles the effect of a transcription factor TAL1 +and its influence on mouse development. Because mutations in this gene +can cause severe developmental issues, Tal1-/- cells (positive for +tdTomato, a fluorescent protein) were injected into wild-type +blastocysts (tdTomato-), forming chimeric embryos.

+

We can assign one sample to a SingleCellExperiment +object named sce like so:

R

@@ -984,12 +989,19 @@

Key Points

+ +

References +

+
+
    +
  1. Pijuan-Sala B, Griffiths JA, Guibentif C et al. (2019). A +single-cell molecular map of mouse gastrulation and early organogenesis. +Nature 566, 7745:490-495.
    2. +
- -

Content from Exploratory data analysis and quality control

+ -->

Content from Exploratory data analysis and quality control

Last updated on 2024-10-03 | @@ -2744,7 +2756,7 @@

Challenge

-
+

We see in the help documentation for ?clusterCells that all of the clustering algorithm details are handled through the @@ -2941,7 +2953,7 @@

Challenge

-
+

You can see that at least among the top markers, cluster 6 (pale green) tends to have the least separation from cluster 1.

@@ -3239,7 +3251,7 @@

Challenge

-
+

R @@ -3503,7 +3515,7 @@

Challenge

-
+

R @@ -3653,7 +3665,7 @@

Exercise 1: Clustering

-
+

The NNGraphParam constructor has an argument cluster.args. This allows to specify arguments passed on to @@ -3670,7 +3682,7 @@

Give me a hint

-
+

R @@ -3709,7 +3721,7 @@

Exercise 2: Reference marker genes

-
+

R @@ -3782,7 +3794,7 @@

Extension Challenge 1: Group pair comparisons

-
+

One important reason why is because averages over all other clusters can be sensitive to the cell type composition. If a rare cell type shows @@ -3816,7 +3828,7 @@

Extension Challenge 2: Parallelizing SingleR

-
+

Use BiocParallel and the BPPARAM argument! This example will set it to use four cores on your laptop, but you can @@ -3858,7 +3870,7 @@

Extension Challenge 3: Critical inspection of diagnost -
+

The example that jumps out most strongly to the eye is ExE endoderm, which doesn’t show clear separate modes. Simultaneously, Endothelium @@ -4151,7 +4163,7 @@

Challenge

-
+

Samples 5 and 6 were from the same “pool” of cells. Looking at the documentation for the dataset under ?WTChimeraData we see @@ -4216,7 +4228,7 @@

Challenge

-
+

False. Batch-level data can be retained through confounding with experimental factors or poor ability to distinguish experimental effects @@ -4669,7 +4681,7 @@

Challenge

-
+

“logFC” stands for log fold-change. edgeR uses a log2 convention. Rather than reporting e.g. a 5-fold increase, it’s better to @@ -4995,7 +5007,7 @@

Exercise 1: Heatmaps

-
+

You can simply hand pheatmap() a matrix as its only argument. pheatmap() has a million options you can tweak, @@ -5009,7 +5021,7 @@

Give me a hint

-
+

R @@ -5044,7 +5056,7 @@

Exercise 2: Model specification and comparison

-
+

After running the second pseudobulk DGE, you can join the two DataFrames of Erythroid3 statistics using the @@ -5059,7 +5071,7 @@

Give me a hint

-
+

R @@ -5142,7 +5154,7 @@

Extension challenge 1: Group effects

-
+

It’s important to have multiple samples within each experimental group because it helps the batch effect correction algorithm distinguish @@ -5219,7 +5231,7 @@

Key Points

-->

Content from Working with large data

-

Last updated on 2024-10-04 | +

Last updated on 2024-10-07 | Edit this page

@@ -5637,7 +5649,7 @@

Challenge

-
+

From ?MulticoreParam :

@@ -5710,21 +5722,22 @@

ROUTPUT 

     approx
-exact   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15
-   1   90   0   0   0   1   0   0   0   1   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  75   0   2   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  56
-   5    0   0   0   0 392   0   0   1   0   1   0   0   0   0   0
-   6    0   0   0   0   0  79 131   0   0   0   0   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   2   0   0   0 106   0   0   0   0   0   0
-   10   0   0   0   0   0   0   0   0   0 105   0   0   0   0   0
-   11   0   0   0   0   0   0   1   0   0   5 147   0   0   0   0
-   12   0   0   0   0   1   0   0   0   0  23   0 199   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
+exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 + 1 88 0 0 0 0 0 0 2 0 0 0 0 0 0 + 2 0 86 0 0 0 0 0 0 0 0 0 0 0 0 + 3 0 57 75 1 0 0 0 0 0 0 0 1 0 0 + 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 + 5 0 0 0 0 176 0 0 0 0 0 0 11 0 0 + 6 0 0 0 0 0 73 128 0 0 1 0 0 0 0 + 7 0 0 0 0 0 253 0 0 1 0 0 0 0 0 + 8 1 0 0 0 0 0 0 106 0 0 0 1 0 0 + 9 0 0 0 0 0 0 0 0 113 0 10 0 0 0 + 10 0 0 0 0 0 0 0 0 0 153 0 0 0 0 + 11 0 0 0 0 0 0 0 0 0 0 198 0 0 0 + 12 0 0 0 0 0 0 0 0 1 0 0 312 0 0 + 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 + 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 + 15 0 0 0 56 0 0 0 0 0 0 0 0 0 0

The similarity of the two clusterings can be quantified by calculating the pairwise Rand index:

@@ -5869,7 +5882,7 @@

Challenge

-
+

This code block calculates the exact PCA coordinates. Another thing to note: PC vectors are only identified up to a sign flip. We can see @@ -6247,7 +6260,7 @@

OUTPUT