Last updated: 2020-07-29

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Rmd 6625ec0 Peter Carbonetto 2020-07-29 wflow_publish(“prepare_pulseseq.Rmd”, view = FALSE, verbose = TRUE)

Here we prepare the “pulse-seq” UMI count data from Montoro et al (2018)—these are gene expression profiles obtained from a novel assay coupling single-cell RNA-seq with in vivo genetic lineage tracing—for topic modeling analyses.

Download file GSE103354_PulseSeq_UMI_counts.rds.gz from the Gene Expression Omnibus (GEO) website, accession GSE103354, and save this file to the “data” folder.

Load a couple packages, and some functions written specifically for loading and preparing the data.

library(Matrix)
library(tools)
source("../code/pulseseq.R")

The single-cell UMI count data are stored as a sparse matrix, with one row per tissue sample and one column per gene.

out     <- read_pulseseq_data("../data/GSE103354_PulseSeq_UMI_counts.rds")
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]

After these processing steps, the data set should contain UMI count data for 21,621 genes and 66,265 samples.

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: 21621
# Number of samples: 66265
# Proportion of counts that are non-zero: 9.3%.

Three mice were profiled at 0, 30 and 60 days of homeostatic turnover:

table(samples$mouse,samples$tp)
#     
#         0   30   60
#   R1 7640 6604 9431
#   R2 4150 7182 7458
#   R3 6273 8175 9352

Krt5-creER/LSL-mT/mG mice and progeny were labeled with membrane-localized EGFP (“GFP”), whereas non-lineage-labeled cells were labeled with membrane-localized tdTomato (“Tom”):

table(samples$lineage)
# 
#   GFP   Tom 
# 36681 29584

In the Montoro et al analysis, the samples were subdivided into 13 cell types:

table(samples$tissue)
# 
#                     basal                  ciliated 
#                     42093                      3016 
#                      club club (hillock-associated) 
#                     13568                      4132 
#                  goblet.1                  goblet.2 
#                        79                         9 
#         goblet.progenitor                  ionocyte 
#                       315                       276 
#            neuroendocrine             proliferating 
#                       630                      1413 
#                    tuft.1                    tuft.2 
#                       413                       259 
#           tuft.progenitor 
#                        62

Save the processed data.

save(list = c("samples","counts"),file = "pulseseq.RData")
resaveRdaFiles("pulseseq.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] Matrix_1.2-15
# 
# loaded via a namespace (and not attached):
#  [1] workflowr_1.6.2 Rcpp_1.0.4.6    lattice_0.20-38 digest_0.6.18  
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# [13] stringi_1.2.4   fs_1.3.1        promises_1.0.1  whisker_0.3-2  
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# [21] yaml_2.2.0      compiler_3.5.1  htmltools_0.3.6 knitr_1.20