Last updated: 2020-10-16

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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, 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)
library(ggplot2)
library(ggrepel)
library(cowplot)
source("../code/plots.R")

Load data and results

Load the droplet data, the \(k = 7\) Poisson NMF model fit, and the clusters identified in the clustering analysis.

load("../data/droplet.RData")
fit <- readRDS("../output/droplet/rds/fit-droplet-scd-ex-k=7.rds")$fit
samples <- readRDS("../output/droplet/clustering-droplet.rds")

Perform differential expression analysis

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 21.36 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 25.22 seconds.

Perform a differential expression analysis after merging the two club cell topics.

fit_club <- merge_topics(poisson2multinom(fit),c("k5","k7"))
timing <- system.time(
  diff_count_club <- diff_count_analysis(fit_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.

Finally, to refine differential expression analysis of the basal cells topic (\(k = 2\)), perform a differential expression analysis after removing the “T+N” cluster.

rows <- which(samples$cluster != "T+N")
fit_basal <- select(poisson2multinom(fit),loadings = rows)
timing <- system.time(
  diff_count_basal <- diff_count_analysis(fit_basal,counts[rows,]))
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 20.59 seconds.

Topics capture shared gene expression patterns

In some cases, the topics capture individual cell-types, and in other cases they capture gene expression patterns common to multiple cell-types.

basal_genes    <- c("Aqp3","Krt5","Dapl1","Hspa1a","Trp63")
ciliated_genes <- c("Ccdc113","Ccdc153","Cdhr3","Foxj1","Lztfl1","Mlf1")
club_genes     <- c("Bpifa1","Cbr2","Cyp2a5","Cyp2f2","Krt15",
                    "Lypd2","Muc5b","Nfia","Scgb1a1","Scgb3a2")
goblet_genes   <- "Gp2"
hillock_genes  <- c("Anxa1","Cldn3","Ecm1","Krt13","Krt4","Lgals3","S100a11")
ionocyte_genes <- c("Ascl3","Asgr1","Atp6v0d2","Atp6v1c2","Cftr","Foxi1",
                    "Moxd1","P2ry14","Stap1")
tuft_neuroendocrine_genes <- c("Ascl1","Ascl2","Ascl3","Chga","Dclk1",
                               "Gnat3","Rgs13")
p1 <- volcano_plot_with_highlighted_genes(diff_count_club,"k1",
        goblet_genes,label_above_quantile = Inf) +
        ylim(c(0,225)) +
        guides(fill = "none") +
        ggtitle("topic 1 (goblet)")
p2 <- volcano_plot_with_highlighted_genes(diff_count_club,"k2",
        c(basal_genes,tuft_neuroendocrine_genes),label_above_quantile = Inf) +
        guides(fill = "none") +
        ggtitle("topic 2 (basal + tuft + PNEC)")
p3 <- volcano_plot_with_highlighted_genes(diff_count_club,"k5+k7",
        club_genes,label_above_quantile = Inf) +
        ylim(c(0,800)) +
        guides(fill = "none") +
        ggtitle("topic 5 + 7 (club)")
p4 <- volcano_plot_with_highlighted_genes(diff_count_club,"k4",
        hillock_genes,label_above_quantile = Inf) +
        guides(fill = "none") +
        ggtitle("topic 4 (hillock)")
p5 <- volcano_plot_with_highlighted_genes(diff_count_club,"k6",
        c(ciliated_genes,tuft_neuroendocrine_genes),
        label_above_quantile = Inf) +
        guides(fill = "none") +
        ggtitle("topic 6 (ciliated + tuft + PNEC)")
plot_grid(p1,p2,p3,p4,p5,nrow = 2,ncol = 3)

Save results

Save the results of the differential expression analyses to an RData file.

save(list = c("diff_count_topics","diff_count_clusters",
              "diff_count_club","diff_count_basal"),
     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] cowplot_1.0.0      ggrepel_0.9.0      ggplot2_3.3.0      fastTopics_0.3-183
# [5] dplyr_0.8.3        Matrix_1.2-18     
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.5           lattice_0.20-38      tidyr_1.0.0         
#  [4] prettyunits_1.1.1    assertthat_0.2.1     zeallot_0.1.0       
#  [7] rprojroot_1.3-2      digest_0.6.23        R6_2.4.1            
# [10] backports_1.1.5      MatrixModels_0.4-1   evaluate_0.14       
# [13] coda_0.19-3          httr_1.4.2           pillar_1.4.3        
# [16] rlang_0.4.5          progress_1.2.2       lazyeval_0.2.2      
# [19] data.table_1.12.8    irlba_2.3.3          SparseM_1.78        
# [22] whisker_0.4          rmarkdown_2.3        labeling_0.3        
# [25] Rtsne_0.15           stringr_1.4.0        htmlwidgets_1.5.1   
# [28] munsell_0.5.0        compiler_3.6.2       httpuv_1.5.2        
# [31] xfun_0.11            pkgconfig_2.0.3      mcmc_0.9-6          
# [34] htmltools_0.4.0      tidyselect_0.2.5     tibble_2.1.3        
# [37] workflowr_1.6.2.9000 quadprog_1.5-8       viridisLite_0.3.0   
# [40] crayon_1.3.4         withr_2.1.2          later_1.0.0         
# [43] MASS_7.3-51.4        grid_3.6.2           jsonlite_1.6        
# [46] gtable_0.3.0         lifecycle_0.1.0      git2r_0.26.1        
# [49] magrittr_1.5         scales_1.1.0         RcppParallel_4.4.2  
# [52] stringi_1.4.3        farver_2.0.1         fs_1.3.1            
# [55] promises_1.1.0       vctrs_0.2.1          glue_1.3.1          
# [58] purrr_0.3.3          hms_0.5.2            yaml_2.2.0          
# [61] colorspace_1.4-1     plotly_4.9.2         knitr_1.26          
# [64] quantreg_5.54        MCMCpack_1.4-5