Montoro et al. droplet dataset
Jason Willwerscheid
8/12/2019
Last updated: 2019-08-31
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Knit directory: scFLASH/
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Overview
droplet <- readRDS("./data/droplet.rds")
source("./code/utils.R")The droplet-based 3’ scRNA-seq dataset analyzed in Montoro et al. (2018) can be obtained here. It includes counts for 18,388 genes and 7,193 cells. Only 9.3% of all counts are nonzero. The data takes up 143 MB when loaded into memory as a sparse Matrix object.
The authors assign each cell to one of seven cell types. The cells are also labelled according to the mice they were taken from.
cell.type <- as.factor(sapply(strsplit(colnames(droplet), "_"), `[`, 3))
plot.category(cell.type, title = "Number of cells per cell type")
mouse <- as.factor(sapply(strsplit(colnames(droplet), "_"), `[`, 1))
plot.category(mouse, title = "Number of cells per mouse")
Library size is distributed as follows.
plot.libsize(droplet)
Note the presence of extreme outliers. There are two of them, and both turn out to be goblet cells. I prefer to remove them from the dataset before analysis.
table(cell.type[which(colSums(droplet) > 30000)])#>
#> Basal Ciliated Club Goblet Ionocyte
#> 0 0 0 2 0
#> Neuroendocrine Tuft
#> 0 0Mean expression is distributed as follows.
plot.meanexp(droplet)
There are two genes that are on average much more highly expressed than any other gene: Bpifa1 and Scgb1a1. Both have bimodal distributions.
high.exp <- names(which(rowMeans(droplet) > 200))
for (gene in high.exp) {
plot(plot.gene(droplet, gene))
}
| Version | Author | Date |
|---|---|---|
| 4e8e813 | Jason Willwerscheid | 2019-08-12 |
| Version | Author | Date |
|---|---|---|
| 4e8e813 | Jason Willwerscheid | 2019-08-12 |
Preprocessing
I remove the two cells with outlying library sizes, then I remove all genes with nonzero counts in less than ten of the remaining 7191 cells. This leaves a total of 14481 genes.
Next, I normalize and transform the data. My default approach is to use library-size normalization followed by alog1p transformation. In future analyses, I will consider alternative normalization strategies and smaller or larger pseudocounts.
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