Last updated: 2021-03-11

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Rmd 6f16f8e Peter Carbonetto 2021-03-11 Added code and R Markdown for preparing trachea droplet data.

Here we prepare the “droplet” UMI count data from Montoro et al (2018)—these are gene expression profiles of trachea epithelial cells in C57BL/6 mice obtained using droplet-based 3’ single-cell RNA-seq—for topic modeling analysis.

Download file GSE103354_Trachea_droplet_UMIcounts.txt.gz from the Gene Expression Omnibus (GEO) website, accession GSE103354, and save this file to the “data” folder. Edit the first line of this file so that the column names are “gene”, “M1_GCTTGAGAAGTCGT_Club”, “M1_GGAACACTTTCGTT_Club”, etc.

Load the readr package, and a couple others, and load some functions written specifically for loading and preparing the data.

library(Matrix)
library(readr)
library(tools)
source("../code/droplet.R")

The UMI count data are imported as a matrix, with one row per tissue sample and one column per gene.

out <- read_droplet_data("../data/GSE103354_Trachea_droplet_UMIcounts.txt.gz")
samples <- out$samples
counts <- out$counts
rm(out)

Remove genes that are not expressed in any of the cells.

j      <- which(colSums(counts > 0) >= 1)
counts <- counts[,j]

This data set contains UMI count data for 7,193 samples and 18,388 genes expressed in at least one cell. The count data are sparse; over 90% of the counts are zero.

cat(sprintf("Number of genes: %d\n",ncol(counts)))
cat(sprintf("Number of samples: %d\n",nrow(counts)))
cat(sprintf("Proportion of counts that are non-zero: %0.1f%%.\n",
            100*mean(counts > 0)))
# Number of genes: 18388
# Number of samples: 7193
# Proportion of counts that are non-zero: 9.3%.

Gene expression was profiled in cells from 6 different mice:

table(samples$mouse.id)
# 
#   M1   M2   M3   M4   M5   M6 
#  212 1679 2211 2222  327  542

In the Montoro et al analysis, the samples were subdivided into 7 cell types:

table(samples$tissue)
# 
#          Basal       Ciliated           Club         Goblet       Ionocyte 
#           3845            425           2578             65             26 
# Neuroendocrine           Tuft 
#             96            158

Convert the count data to a sparse matrix.

counts <- as(counts,"dgCMatrix")

Save the processed data.

save(list = c("samples","counts"),file = "droplet.RData")
resaveRdaFiles("droplet.RData")

sessionInfo()
# R version 3.5.1 (2018-07-02)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Scientific Linux 7.4 (Nitrogen)
# 
# Matrix products: default
# BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
# 
# locale:
#  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
# 
# attached base packages:
# [1] tools     stats     graphics  grDevices utils     datasets  methods  
# [8] base     
# 
# other attached packages:
# [1] readr_1.3.1   Matrix_1.2-15
# 
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#  [1] Rcpp_1.0.4.6     knitr_1.20       whisker_0.3-2    magrittr_1.5    
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