Last updated: 2020-07-27

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Rmd a75eba1 Peter Carbonetto 2020-07-27 Added code and workflowr analysis for preparing FACS-purified PBMC data sets.

These are the steps taken to prepare the mixture of FACS-purified PBMC data for topic modeling analysis. These data are the “reference transcriptome” profiles generated via single-cell RNA-seq of 10 bead-enriched subpopulations as described in Zheng et al (2017).

Note: In Zheng et al (2017), the clustering analysis suggests that the subpopulations are mostly “homogenous”, with a couple exceptions, the CD14+ and CD34+ subpopulations. For the CD14+ monocytes, two clusters were observed, and they were identified as “CD14+ monocytes” and “dendritic cells”. See Supplementary Figures 6 and 7 in that paper.

Download the “Gene/cell matrix (filtered)” tar.gz file from the 10x Genomics website for each of the following 10 data sets: CD14+ Monocytes, CD19+ B Cells, CD34+ Cells, CD4+ Helper T Cells, CD4+/CD25+ Regulatory T Cells, CD4+/CD45RA+/CD25- Naive T cells, CD4+/CD45RO+ Memory T Cells, CD56+ Natural Killer Cells, CD8+ Cytotoxic T cells and CD8+/CD45RA+ Naive Cytotoxic T Cells. Unpack the individual tar archives and re-organize the files into 10 subdirectories, each containing three (compressed) files, barcodes.tsv.gz, genes.tsv.gz and matrix.mtx.gz. The “datasets” variable lists the names of all subdirectories used, along with the “cell type” labels that Zheng et al (2017) assigned to each of the 10 data sets:

datasets <- list("CD19+ B"                      = "b_cells",
                 "CD14+ Monocyte"               = "cd14_monocytes",
                 "CD34+"                        = "cd34_filtered",
                 "CD4+ T Helper2"               = "cd4_t_helper",
                 "CD56+ NK"                     = "cd56_nk",
                 "CD8+ Cytotoxic T"             = "cytotoxic_t",
                 "CD4+/CD45RO+ Memory"          = "memory_t",
                 "CD8+/CD45RA+ Naive Cytotoxic" = "naive_cytotoxic",
                 "CD4+/CD45RA+/CD25- Naive T"   = "naive_t",
                 "CD4+/CD25 T Reg"              = "regulatory_t")

Load the packages used in the analysis below, and some functions written specifically for loading and preparing these data.

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

Call read_purified_pbmc_data, which imports the individual data sets into R, and combines them:

out     <- read_purified_pbmc_data("../data",datasets)
samples <- out$samples
genes   <- out$genes
counts  <- out$counts
rm(out)
# Importing data from these files:
# ../data/b_cells/genes.tsv.gz 
# ../data/b_cells/barcodes.tsv.gz 
# ../data/b_cells/matrix.mtx.gz 
# ../data/cd14_monocytes/genes.tsv.gz 
# ../data/cd14_monocytes/barcodes.tsv.gz 
# ../data/cd14_monocytes/matrix.mtx.gz 
# ../data/cd34_filtered/genes.tsv.gz 
# ../data/cd34_filtered/barcodes.tsv.gz 
# ../data/cd34_filtered/matrix.mtx.gz 
# ../data/cd4_t_helper/genes.tsv.gz 
# ../data/cd4_t_helper/barcodes.tsv.gz 
# ../data/cd4_t_helper/matrix.mtx.gz 
# ../data/cd56_nk/genes.tsv.gz 
# ../data/cd56_nk/barcodes.tsv.gz 
# ../data/cd56_nk/matrix.mtx.gz 
# ../data/cytotoxic_t/genes.tsv.gz 
# ../data/cytotoxic_t/barcodes.tsv.gz 
# ../data/cytotoxic_t/matrix.mtx.gz 
# ../data/memory_t/genes.tsv.gz 
# ../data/memory_t/barcodes.tsv.gz 
# ../data/memory_t/matrix.mtx.gz 
# ../data/naive_cytotoxic/genes.tsv.gz 
# ../data/naive_cytotoxic/barcodes.tsv.gz 
# ../data/naive_cytotoxic/matrix.mtx.gz 
# ../data/naive_t/genes.tsv.gz 
# ../data/naive_t/barcodes.tsv.gz 
# ../data/naive_t/matrix.mtx.gz 
# ../data/regulatory_t/genes.tsv.gz 
# ../data/regulatory_t/barcodes.tsv.gz 
# ../data/regulatory_t/matrix.mtx.gz

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

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

After filtering out genes with no expression, the count data are stored in a 94,655 x 21,952 sparse matrix, with rows corresponding to samples, and columns corresponding to genes. This matrix is very sparse—less than 3% of the counts are greater than zero:

cat(sprintf("Number of samples: %d\n",nrow(counts)))
cat(sprintf("Number of genes: %d\n",ncol(counts)))
cat(sprintf("Proportion of counts that are non-zero: %0.1f%%.\n",
            100*mean(counts > 0)))
# Number of samples: 94655
# Number of genes: 21952
# Proportion of counts that are non-zero: 2.9%.

This is the breakdown of samples by data set:

summary(samples["celltype"],maxsum = 10)
#                          celltype    
#  CD19+ B                     :10085  
#  CD14+ Monocyte              : 2612  
#  CD34+                       : 9232  
#  CD4+ T Helper2              :11213  
#  CD56+ NK                    : 8385  
#  CD8+ Cytotoxic T            :10209  
#  CD4+/CD45RO+ Memory         :10224  
#  CD8+/CD45RA+ Naive Cytotoxic:11953  
#  CD4+/CD45RA+/CD25- Naive T  :10479  
#  CD4+/CD25 T Reg             :10263

Save the processed data.

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