Last updated: 2019-08-31

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File Version Author Date Message
html 9435a68 Jason Willwerscheid 2019-08-30 Build site.
Rmd 3202fa7 Jason Willwerscheid 2019-08-30 wflow_publish(“analysis/droplet.Rmd”)
html f63d86b Jason Willwerscheid 2019-08-17 Build site.
Rmd 6978559 Jason Willwerscheid 2019-08-17 wflow_publish(“analysis/droplet.Rmd”)
html 4e8e813 Jason Willwerscheid 2019-08-12 Build site.
Rmd cbc50e1 Jason Willwerscheid 2019-08-12 wflow_publish(“analysis/droplet.Rmd”) 
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")

Version Author Date
9435a68 Jason Willwerscheid 2019-08-30
4e8e813 Jason Willwerscheid 2019-08-12 
mouse <- as.factor(sapply(strsplit(colnames(droplet), "_"), `[`, 1))
plot.category(mouse, title = "Number of cells per mouse")

Version Author Date
9435a68 Jason Willwerscheid 2019-08-30
4e8e813 Jason Willwerscheid 2019-08-12

Library size is distributed as follows.

plot.libsize(droplet)

Version Author Date
4e8e813 Jason Willwerscheid 2019-08-12

Note the presence of extreme outliers. There are two of them, and both turn out to be goblet cells. I think that it would be a good idea 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              0

Mean expression is distributed as follows.

plot.meanexp(droplet)

Version Author Date
4e8e813 Jason Willwerscheid 2019-08-12

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 


sessionInfo()
#> R version 3.5.3 (2019-03-11)
#> Platform: x86_64-apple-darwin15.6.0 (64-bit)
#> Running under: macOS Mojave 10.14.6
#> 
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] flashier_0.1.15 ggplot2_3.2.0   Matrix_1.2-15  
#> 
#> loaded via a namespace (and not attached):
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#> [13] pkgconfig_2.0.2   rlang_0.3.1       foreach_1.4.4    
#> [16] parallel_3.5.3    yaml_2.2.0        ebnm_0.1-24      
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