Last updated: 2020-07-29
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Knit directory: single-cell-topics/analysis/
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| File | Version | Author | Date | Message |
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| Rmd | 3a11444 | Peter Carbonetto | 2020-07-29 | wflow_publish(“prepare_droplet.Rmd”, view = FALSE, verbose = TRUE) |
| html | 8c1812d | Peter Carbonetto | 2020-07-29 | Completed first build of prepare_droplet analysis. |
| Rmd | 5fecfb1 | Peter Carbonetto | 2020-07-28 | wflow_publish(“prepare_droplet.Rmd”, view = FALSE, verbose = TRUE) |
| Rmd | dec0e66 | Peter Carbonetto | 2020-07-28 | Added R and R Markdown code for preparation of 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.
Before running these steps on your computer, 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 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$mouse.id)
#
# M1 M2 M3 M4 M5 M6
# 212 1679 2211 2222 327 542In the Montoro et al analysis, the samples were subdivided into 7 cell types:
table(samples$tissue)
#
# Basal Ciliated Club Goblet Ionocyte
# 3845 425 2578 65 26
# Neuroendocrine Tuft
# 96 158Convert 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
#
# loaded via a namespace (and not attached):
# [1] Rcpp_1.0.4.6 knitr_1.20 whisker_0.3-2 magrittr_1.5
# [5] workflowr_1.6.2 hms_0.4.2 lattice_0.20-38 R6_2.3.0
# [9] rlang_0.4.0 stringr_1.3.1 grid_3.5.1 git2r_0.26.1
# [13] htmltools_0.3.6 yaml_2.2.0 rprojroot_1.3-2 digest_0.6.18
# [17] tibble_2.1.1 crayon_1.3.4 later_0.7.5 promises_1.0.1
# [21] fs_1.3.1 glue_1.3.0 evaluate_0.12 rmarkdown_1.10
# [25] stringi_1.2.4 pillar_1.3.1 compiler_3.5.1 backports_1.1.2
# [29] httpuv_1.4.5 pkgconfig_2.0.2