Last updated: 2020-08-17
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Knit directory: single-cell-topics/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 16dcd2a | Peter Carbonetto | 2020-08-17 | Merge branch ‘master’ of github.com:stephenslab/single-cell-topics. |
| Rmd | a3972f1 | Peter Carbonetto | 2020-08-17 | Improved code in prepare_68k_pbmc analysis. |
| Rmd | 0b0b5a5 | Peter Carbonetto | 2020-08-11 | Working on plots to visualize droplet and 68k pbmc topics. |
| html | dab7448 | Peter Carbonetto | 2020-07-28 | Made a couple minor revisions to prepare_68k_pbmc. |
| Rmd | 4a79355 | Peter Carbonetto | 2020-07-28 | wflow_publish(“prepare_68k_pbmc.Rmd”, view = FALSE, verbose = TRUE) |
| html | 9622db8 | Peter Carbonetto | 2020-07-28 | Built first draft of prepare_68k_pbmc analysis. |
| Rmd | e684eab | Peter Carbonetto | 2020-07-28 | More improvements to prepare_68k_pbmc analysis. |
| Rmd | 29e6a6d | Peter Carbonetto | 2020-07-28 | Working on R Markdown for preparation of 68k pbmc data set. |
These are the steps taken to prepare the unsorted 68k PBMC data from Zheng et al (2017) for topic modeling analysis.
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/pbmc.R")Before loading the data, download the “Gene/cell matrix (filtered)” tar.gz file for the “Fresh 68k PBMCs (Donor A) data set from 10x Genomics website, download 68k_pbmc_barcodes_annotation.tsv from the companion repository on GitHub, and then save and compress these files to the data/pbmc_68k directory.
samples <- read_barcodes_with_annotations(file.path("../data/pbmc_68k",
"68k_pbmc_barcodes_annotation.tsv.gz"))
# Parsed with column specification:
# cols(
# TSNE.1 = col_double(),
# TSNE.2 = col_double(),
# barcodes = col_character(),
# celltype = col_character()
# )
genes <- read_genes("../data/pbmc_68k/genes.tsv.gz")
counts <- create_counts_matrix("../data/pbmc_68k/matrix.mtx.gz",
sample.names = samples$barcode,
gene.names = genes$ensembl)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 68,579 x 20,387 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: 68579
# Number of genes: 20387
# Proportion of counts that are non-zero: 2.7%.Save the processed data.
save(list = c("samples","genes","counts"),
file = "pbmc_68k.RData")
# resaveRdaFiles("pbmc_68k.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
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# [13] htmltools_0.3.6 yaml_2.2.0 rprojroot_1.3-2 digest_0.6.18
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# [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