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Source  : ef7f591
Branch  : main
Author  : Andrew Ghazi <[email protected]>
Time    : 2024-09-08 03:06:18 +0000
Message : Merge pull request #25 from ccb-hms/add_exercises

minor formatting changes to cell type annotation
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actions-user committed Sep 8, 2024
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128 changes: 74 additions & 54 deletions cell_type_annotation.md
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Expand Up @@ -24,6 +24,8 @@ editor_options:



Again we'll start by loading the libraries we'll be using:


``` r
library(AUCell)
Expand All @@ -32,11 +34,13 @@ library(SingleR)
library(bluster)
library(scater)
library(scran)
library(pheatmap)
library(GSEABase)
```

## Data retrieval

We'll be using the same set of WT chimeric mouse embryo data:
We'll be using the fifth processed sample from the WT chimeric mouse embryo data:


``` r
Expand Down Expand Up @@ -64,7 +68,9 @@ To speed up the computations, we take a random subset of 1,000 cells.

``` r
set.seed(123)

ind <- sample(ncol(sce), 1000)

sce <- sce[,ind]
```

Expand All @@ -75,6 +81,7 @@ The SCE object needs to contain log-normalized expression counts as well as PCA

``` r
sce <- logNormCounts(sce)

sce <- runPCA(sce)
```

Expand Down Expand Up @@ -125,6 +132,7 @@ table(colLabels(sce))
1 2 3 4 5 6 7 8 9 10 11
100 160 99 141 63 93 60 108 44 91 41
```
You can see we ended up with 11 clusters of varying sizes.

We can now overlay the cluster labels as color on a UMAP plot:

Expand Down Expand Up @@ -170,7 +178,9 @@ cluster when compared to another cluster.

``` r
rownames(sce) <- rowData(sce)$SYMBOL

markers <- findMarkers(sce, test.type = "wilcox", direction = "up", lfc = 1)

markers
```

Expand Down Expand Up @@ -329,6 +339,7 @@ most of the examples here are derived.

``` r
ref <- EmbryoAtlasData(samples = 29)

ref
```

Expand All @@ -347,22 +358,14 @@ mainExpName: NULL
altExpNames(0):
```

``` r
table(ref$stage)
```

``` output
E8.5
7569
```

To speed up the computations, we subsample the dataset to 1,000 cells.
In order to reduce the computational load, we subsample the dataset to 1,000 cells.


``` r
set.seed(123)

ind <- sample(ncol(ref), 1000)

ref <- ref[,ind]
```

Expand Down Expand Up @@ -404,17 +407,20 @@ tab
1
```

We need the normalized log counts, so we add those on:


``` r
ref <- logNormCounts(ref)
```

Some cleaning - remove cells of the reference dataset for which the cell
type annotation is missing.
type annotation is missing:


``` r
nna <- !is.na(ref$celltype)

ref <- ref[,nna]
```

Expand All @@ -424,7 +430,9 @@ to remove noise prior to subsequent annotation tasks.

``` r
abu.ct <- names(tab)[tab >= 10]

ind <- ref$celltype %in% abu.ct

ref <- ref[,ind]
```

Expand All @@ -433,17 +441,21 @@ Restrict to genes shared between query and reference dataset.

``` r
rownames(ref) <- rowData(ref)$SYMBOL
isect <- intersect(rownames(sce), rownames(ref))
sce <- sce[isect,]
ref <- ref[isect,]

shared_genes <- intersect(rownames(sce), rownames(ref))

sce <- sce[shared_genes,]

ref <- ref[shared_genes,]
```

Convert sparse assay matrices to regular dense matrices for input to
SingleR.
SingleR:


``` r
sce.mat <- as.matrix(assay(sce, "logcounts"))

ref.mat <- as.matrix(assay(ref, "logcounts"))
```

Expand Down Expand Up @@ -513,8 +525,8 @@ cell types.


``` r
library(pheatmap)
tab <- table(anno = res$pruned.labels, cluster = colLabels(sce))

pheatmap(log2(tab + 10), color = colorRampPalette(c("white", "blue"))(101))
```

Expand All @@ -529,6 +541,7 @@ experts.

``` r
tab <- table(res$pruned.labels, sce$celltype.mapped)

pheatmap(log2(tab + 10), color = colorRampPalette(c("white", "blue"))(101))
```

Expand Down Expand Up @@ -571,10 +584,11 @@ markers derived from the mouse embryo atlas dataset.


``` r
library(scran)
wilcox.z <- pairwiseWilcox(ref, ref$celltype, lfc = 1, direction = "up")

markers.z <- getTopMarkers(wilcox.z$statistics, wilcox.z$pairs,
pairwise = FALSE, n = 50)

lengths(markers.z)
```

Expand Down Expand Up @@ -635,10 +649,11 @@ cell.


``` r
library(GSEABase)
all.sets <- lapply(names(markers.z),
function(x) GeneSet(markers.z[[x]], setName = x))

all.sets <- GeneSetCollection(all.sets)

all.sets
```

Expand All @@ -653,55 +668,57 @@ GeneSetCollection


``` r
library(AUCell)
rankings <- AUCell_buildRankings(as.matrix(counts(sce)),
plotStats = FALSE, verbose = FALSE)

cell.aucs <- AUCell_calcAUC(all.sets, rankings)

results <- t(assay(cell.aucs))

head(results)
```

``` output
gene sets
cells Allantois Cardiomyocytes Endothelium Erythroid2 Erythroid3
cell_11995 0.21994609 0.2336827 0.1947563 0.12228508 0.1639749
cell_10294 0.10066221 0.1246414 0.1104634 0.60732017 0.6081721
cell_9963 0.34273069 0.3158431 0.5407815 0.13588372 0.1797007
cell_11610 0.08244651 0.1340388 0.1048735 0.57892981 0.5706619
cell_10910 0.31763336 0.2784001 0.2445795 0.08175859 0.1354818
cell_11021 0.20549732 0.2320122 0.1869839 0.11351012 0.1769588
cells Allantois Cardiomyocytes Endothelium Erythroid2 Erythroid3
cell_11995 0.2199 0.234 0.195 0.1223 0.164
cell_10294 0.1007 0.125 0.110 0.6073 0.608
cell_9963 0.3427 0.316 0.541 0.1359 0.180
cell_11610 0.0824 0.134 0.105 0.5789 0.571
cell_10910 0.3176 0.278 0.245 0.0818 0.135
cell_11021 0.2055 0.232 0.187 0.1135 0.177
gene sets
cells ExE endoderm ExE mesoderm Forebrain/Midbrain/Hindbrain Gut
cell_11995 0.06053637 0.4371076 0.6319109 0.3779960
cell_10294 0.06369959 0.1900184 0.2670988 0.1482988
cell_9963 0.09466189 0.4165978 0.4972886 0.3805059
cell_11610 0.05720738 0.2060333 0.2785028 0.1466698
cell_10910 0.11787498 0.5770948 0.5721069 0.4450836
cell_11021 0.09285926 0.4736976 0.6011933 0.3993608
cells ExE endoderm ExE mesoderm Forebrain/Midbrain/Hindbrain Gut
cell_11995 0.0605 0.437 0.632 0.378
cell_10294 0.0637 0.190 0.267 0.148
cell_9963 0.0947 0.417 0.497 0.381
cell_11610 0.0572 0.206 0.279 0.147
cell_10910 0.1179 0.577 0.572 0.445
cell_11021 0.0929 0.474 0.601 0.399
gene sets
cells Haematoendothelial progenitors Intermediate mesoderm Mesenchyme
cell_11995 0.3329538 0.5396029 0.2456757
cell_10294 0.1621829 0.1781659 0.1078670
cell_9963 0.6010306 0.4623277 0.3566887
cell_11610 0.1526329 0.1968055 0.1118536
cell_10910 0.4333710 0.5481614 0.3512731
cell_11021 0.3336212 0.5197592 0.2403682
cell_11995 0.333 0.540 0.246
cell_10294 0.162 0.178 0.108
cell_9963 0.601 0.462 0.357
cell_11610 0.153 0.197 0.112
cell_10910 0.433 0.548 0.351
cell_11021 0.334 0.520 0.240
gene sets
cells Neural crest NMP Paraxial mesoderm Pharyngeal mesoderm
cell_11995 0.6535231 0.4841026 0.5510547 0.5076289
cell_10294 0.2840264 0.2113283 0.2192031 0.2049349
cell_9963 0.5374756 0.3740042 0.5007010 0.4618718
cell_11610 0.2949987 0.2235049 0.2348724 0.2134701
cell_10910 0.5472885 0.5225294 0.5506325 0.5564014
cell_11021 0.5863399 0.5731637 0.4992671 0.4707530
cells Neural crest NMP Paraxial mesoderm Pharyngeal mesoderm
cell_11995 0.654 0.484 0.551 0.508
cell_10294 0.284 0.211 0.219 0.205
cell_9963 0.537 0.374 0.501 0.462
cell_11610 0.295 0.224 0.235 0.213
cell_10910 0.547 0.523 0.551 0.556
cell_11021 0.586 0.573 0.499 0.471
gene sets
cells Somitic mesoderm Spinal cord Surface ectoderm
cell_11995 0.4654095 0.5807133 0.5251957
cell_10294 0.2141816 0.2290110 0.1874113
cell_9963 0.4307335 0.4373858 0.4173470
cell_11610 0.2339673 0.2564642 0.2054802
cell_10910 0.5179489 0.5167149 0.5049140
cell_11021 0.5186006 0.5526277 0.5052879
cell_11995 0.465 0.581 0.525
cell_10294 0.214 0.229 0.187
cell_9963 0.431 0.437 0.417
cell_11610 0.234 0.256 0.205
cell_10910 0.518 0.517 0.505
cell_11021 0.519 0.553 0.505
```

We assign cell type identity to each cell in the test dataset by taking
Expand All @@ -718,7 +735,9 @@ they should at least represent closely related cell states).

``` r
new.labels <- colnames(results)[max.col(results)]

tab <- table(new.labels, sce$celltype.mapped)

tab
```

Expand Down Expand Up @@ -959,6 +978,7 @@ may indicate that its gene set is not sufficiently informative.

``` r
par(mfrow = c(3,3))

AUCell_exploreThresholds(cell.aucs[1:9], plotHist = TRUE, assign = TRUE)
```

Expand Down
2 changes: 1 addition & 1 deletion md5sum.txt
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Expand Up @@ -6,7 +6,7 @@
"links.md" "8184cf4149eafbf03ce8da8ff0778c14" "site/built/links.md" "2024-09-06"
"episodes/intro-sce.Rmd" "538e5f4aa0dde4a6cb09a342d8dc867d" "site/built/intro-sce.md" "2024-09-06"
"episodes/eda_qc.Rmd" "1e88f395d30778f4526532deea43eb03" "site/built/eda_qc.md" "2024-09-06"
"episodes/cell_type_annotation.Rmd" "fa2333e4c8aa876394327610eb650e8b" "site/built/cell_type_annotation.md" "2024-09-06"
"episodes/cell_type_annotation.Rmd" "a586bd88d56159adebaf315580fa2285" "site/built/cell_type_annotation.md" "2024-09-08"
"episodes/multi-sample.Rmd" "e33ec8bf5cfd26471d17fce4d6af9281" "site/built/multi-sample.md" "2024-09-06"
"episodes/large_data.Rmd" "bbe443f474a0823122658effa2beb57e" "site/built/large_data.md" "2024-09-06"
"episodes/hca.Rmd" "6db220495ae4ae56d33e4ca5b5f9b8ae" "site/built/hca.md" "2024-09-06"
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