Last updated: 2020-09-25
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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 droplet data.
Load the packages used in the analysis below.
library(Matrix)
library(dplyr)
library(fastTopics)
library(tools)Load the droplet data.
load("../data/droplet.RData")Load the \(k = 7\) Poisson NMF model fit.
fit <- readRDS("../output/droplet/rds/fit-droplet-scd-ex-k=7.rds")$fitLoad the clusters identified in the clustering analysis.
samples <- readRDS("../output/droplet/clustering-droplet.rds")Perform differential expression analysis using the multinomial topic model.
timing <- system.time(
diff_count_topics <- diff_count_analysis(fit,counts))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 18388 x 7 = 128716 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 23.76 seconds.Next, calculate differential expression statistics using the clusters that were identified from the topic proportion PCs.
fit_clusters <- init_poisson_nmf_from_clustering(counts,samples$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 18388 x 8 = 147104 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 25.88 seconds.Finally, perform one more differential expression analysis after first merging the two topics for club cells.
fit_merge_club <- merge_topics(poisson2multinom(fit),c(5,7))
timing <- system.time(
diff_count_merge_club <- diff_count_analysis(fit_merge_club,counts))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 18388 x 6 = 110328 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 17.14 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_club"),
file = "diff-count-droplet.RData")
resaveRdaFiles("diff-count-droplet.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
# [7] colorspace_1.4-1 viridisLite_0.3.0 htmltools_0.4.0
# [10] yaml_2.2.0 MCMCpack_1.4-5 plotly_4.9.2
# [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
# [22] workflowr_1.6.2.9000 htmlwidgets_1.5.1 coda_0.19-3
# [25] evaluate_0.14 knitr_1.26 SparseM_1.78
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# [31] Rcpp_1.0.5 promises_1.1.0 backports_1.1.5
# [34] scales_1.1.0 jsonlite_1.6 RcppParallel_4.4.2
# [37] fs_1.3.1 mcmc_0.9-6 hms_0.5.2
# [40] ggplot2_3.3.0 digest_0.6.23 stringi_1.4.3
# [43] Rtsne_0.15 ggrepel_0.9.0 grid_3.6.2
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# [55] whisker_0.4 pkgconfig_2.0.3 MASS_7.3-51.4
# [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