Last updated: 2020-09-26
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Knit directory: single-cell-topics/analysis/
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Here we perform a differential expression analysis using the topic model fit to the pulse-seq data, as well as the clusters identified from this topic model.
Load the packages used in the analysis below.
library(Matrix)
library(dplyr)
library(fastTopics)
library(tools)Load the pulse-seq data, the \(k = 11\) Poisson NMF model fit, and the clusters identified in the clustering analysis.
load("../data/pulseseq.RData")
fit <- readRDS("../output/pulseseq/rds/fit-pulseseq-scd-ex-k=11.rds")$fit
samples <- readRDS("../output/pulseseq/clustering-pulseseq.rds")Perform differential expression analysis using the multinomial topic model.
timing <- system.time(
diff_count_topics <- diff_count_analysis(fit,counts))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21621 x 11 = 237831 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 841.75 seconds.Next, calculate differential expression statistics using the clusters that were identified from the topic proportion PCs.
fit_clusters <- init_poisson_nmf_from_clustering(counts,samples$cluster)
timing <- system.time(
diff_count_clusters <- diff_count_analysis(fit_clusters,counts))
# All topic proportions are either zero or one; using simpler single-topic calculations for model parameter estimates
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21621 x 7 = 151347 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 495.92 seconds.Finally, perform one more differential expression analysis after merging the 5 club cell topics and the 3 basal cell topics.
fit_merge_bc <- merge_topics(poisson2multinom(fit),
c("k4","k5","k6","k8","k10"))
fit_merge_bc <- merge_topics(fit_merge_bc,c("k1","k3","k9"))
timing <- system.time(
diff_count_merge_bc <- diff_count_analysis(fit_merge_bc,counts))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21621 x 5 = 108105 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 351.25 seconds.Save the results of the differential expression analyses to an RData file.
save(list = c("diff_count_topics",
"diff_count_clusters",
"diff_count_merge_bc"),
file = "diff-count-pulseseq.RData")
resaveRdaFiles("diff-count-pulseseq.RData")
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.6
#
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
#
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#
# attached base packages:
# [1] tools stats graphics grDevices utils datasets methods
# [8] base
#
# other attached packages:
# [1] fastTopics_0.3-177 dplyr_0.8.3 Matrix_1.2-18
#
# loaded via a namespace (and not attached):
# [1] progress_1.2.2 tidyselect_0.2.5 xfun_0.11
# [4] purrr_0.3.3 lattice_0.20-38 vctrs_0.2.1
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# [13] rlang_0.4.5 later_1.0.0 pillar_1.4.3
# [16] glue_1.3.1 lifecycle_0.1.0 stringr_1.4.0
# [19] MatrixModels_0.4-1 munsell_0.5.0 gtable_0.3.0
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# [58] prettyunits_1.1.1 data.table_1.12.8 assertthat_0.2.1
# [61] rmarkdown_2.3 httr_1.4.1 R6_2.4.1
# [64] git2r_0.26.1 compiler_3.6.2