Last updated: 2021-02-24

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Rmd 3d7de41 Peter Carbonetto 2021-02-24 workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE)
Rmd 0163047 Peter Carbonetto 2021-02-24 Updated volcano plots using K=6 topic model in plots_purified_pbmc analysis.
Rmd a5c70b4 Peter Carbonetto 2021-02-24 Revised the FACS-based and cluster-based volcano plots in the plots_pbmc_purified analysis.
Rmd 0748309 Peter Carbonetto 2021-02-19 Revised volcano plots in plots_purified_pbmc analysis; working on examine_de_calculations.R.
Rmd 65dbb1e Peter Carbonetto 2021-02-17 Made improvements to volcano plots in plots_purified_pbmc analysis after fixing DE calculations.
Rmd 14401d5 Peter Carbonetto 2021-02-10 Working on GSEA in plots_purified_pbmc analysis.
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html f8e1d99 Peter Carbonetto 2021-02-10 Re-built plots_purified_pbmc page.
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Rmd e437ddf Peter Carbonetto 2021-01-04 Working on volcano plots in plots_purified_pbmc analysis.

Here we perform a differential expression analysis using the topic model fit to the mixture of FACS-purified data, as well as the clusters identified from this topic model.

Load the packages used in the analysis below, as well as additional functions that we will use to generate some of the plots.

library(Matrix)
library(fastTopics)
library(fgsea)
library(ggplot2)
library(ggrepel)
library(cowplot)
library(plotly)
library(htmlwidgets)
source("../code/gsea.R")
source("../code/functions_for_plots_purified_pbmc.R")

Load the count data, the \(K = 6\) topic model fit, and the 7 clusters identified in the clustering analysis.

load("../data/pbmc_purified.RData")
fit <- readRDS(file.path("../output/pbmc-purified/rds",
                         "fit-pbmc-purified-scd-ex-k=6.rds"))$fit
fit <- poisson2multinom(fit)
samples <- readRDS("../output/pbmc-purified/clustering-pbmc-purified.rds")

Load the gene set data.

load("../data/gene_sets_human.RData")
rownames(gene_sets) <- gene_info$Ensembl

Perform differential expression analysis using the FACS labeling:

celltype <- as.character(samples$celltype)
celltype[celltype == "CD4+/CD45RA+/CD25- Naive T" |
         celltype == "CD4+/CD45RO+ Memory" |
         celltype == "CD8+/CD45RA+ Naive Cytotoxic" |
         celltype == "CD4+ T Helper2" |
         celltype == "CD4+/CD25 T Reg"] <- "T cell"
celltype <- factor(celltype)
table(celltype)
diff_count_facs <- diff_count_clusters(celltype,counts)
# celltype
#   CD14+ Monocyte          CD19+ B            CD34+         CD56+ NK 
#             2612            10085             9232             8385 
# CD8+ Cytotoxic T           T cell 
#            10209            54132 
# Fitting 21952 x 6 = 131712 univariate Poisson models.
# Computing log-fold change statistics.
# Stabilizing log-fold change estimates using adaptive shrinkage.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

These volcano plots summarize the results of the differential expression analysis using the FACS labeling:

p1 <- volcano_plot(diff_count_facs,"CD19+ B",genes$symbol,
                   betamax = 10,label_above_quantile = 0.995,
                   subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("B cells")
p2 <- volcano_plot(diff_count_facs,"CD14+ Monocyte",genes$symbol,
                   betamax = 10,label_above_quantile = 0.995,
                   subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("CD14+ cells")
p3 <- volcano_plot(diff_count_facs,"CD34+",genes$symbol,
                   betamax = 10,label_above_quantile = 0.995,
                   subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("CD34+ cells")
p4 <- volcano_plot(diff_count_facs,"CD56+ NK",genes$symbol,
                   betamax = 10,label_above_quantile = 0.995,
                   subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("NK cells")
p5 <- volcano_plot(diff_count_facs,"CD8+ Cytotoxic T",genes$symbol,
                   betamax = 10,label_above_quantile = 0.995,
                   subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("CD8+ T cells")
p6 <- volcano_plot(diff_count_facs,"T cell",genes$symbol,
                   betamax = 10,label_above_quantile = 0.998,
                   subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("T cells")
plot_grid(p1,p2,p3,p4,p5,p6,nrow = 2,ncol = 3)

Version Author Date
6e2b53a Peter Carbonetto 2021-02-10
9afb462 Peter Carbonetto 2021-02-10
f8e1d99 Peter Carbonetto 2021-02-10
d1d4185 Peter Carbonetto 2021-02-10
f596d8a Peter Carbonetto 2021-02-09
d0775d2 Peter Carbonetto 2021-02-09
ab3eeb8 Peter Carbonetto 2021-01-29
42ebc62 Peter Carbonetto 2021-01-29
a4bc59b Peter Carbonetto 2021-01-06
a0b6c2b Peter Carbonetto 2021-01-06
0411340 Peter Carbonetto 2021-01-06
fad8e3d Peter Carbonetto 2021-01-05

Perform differential expression analysis using the clusters:

table(samples$cluster)
diff_count_clusters <- diff_count_clusters(samples$cluster,counts)
# 
#         B     CD14+     CD34+      CD8+ dendritic        NK         T 
#     10439      2956      8237      3757       308      8380     60578 
# Fitting 21952 x 7 = 153664 univariate Poisson models.
# Computing log-fold change statistics.
# Stabilizing log-fold change estimates using adaptive shrinkage.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

These volcano plots summarize the results of the differential expression analysis using the clusters:

p7 <- volcano_plot(diff_count_clusters,"B",genes$symbol,betamax = 10,
                   label_above_quantile = 0.995,
                   subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("B cells")
# 12074 out of 21952 data points will be included in plot
p8 <- volcano_plot(diff_count_clusters,"CD14+",genes$symbol,betamax = 10,
                   label_above_quantile = 0.993,
                   subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("CD14+ cells")
# 12074 out of 21952 data points will be included in plot
p9 <- volcano_plot(diff_count_clusters,"CD34+",genes$symbol,betamax = 10,
                   label_above_lfc = 4,label_above_quantile = 0.98,
                   subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("CD34+ cells")
# 12074 out of 21952 data points will be included in plot
p10 <- volcano_plot(diff_count_clusters,"dendritic",genes$symbol,
                    label_above_lfc = 4,label_above_quantile = 0.95,
                    subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("dendritic cells")
# 12074 out of 21952 data points will be included in plot
p11 <- volcano_plot(diff_count_clusters,"NK",genes$symbol,
                    label_above_quantile = 0.995,
                    subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("NK cells")
# 12074 out of 21952 data points will be included in plot
p12 <- volcano_plot(diff_count_clusters,"CD8+",genes$symbol,
                    label_above_lfc = 2,label_above_quantile = 0.98,
                    subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("CD8+ T cells")
# 12091 out of 21952 data points will be included in plot
p13 <- volcano_plot(diff_count_clusters,"T",genes$symbol,
                    label_above_lfc = 2,label_above_quantile = 0.995,
                    subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("T cells")
# 12074 out of 21952 data points will be included in plot
plot_grid(p7,p8,p9,p10,p11,p12,p13,nrow = 3,ncol = 3)

Version Author Date
6e2b53a Peter Carbonetto 2021-02-10
9afb462 Peter Carbonetto 2021-02-10
f8e1d99 Peter Carbonetto 2021-02-10
d1d4185 Peter Carbonetto 2021-02-10
f596d8a Peter Carbonetto 2021-02-09
d0775d2 Peter Carbonetto 2021-02-09
ab3eeb8 Peter Carbonetto 2021-01-29
42ebc62 Peter Carbonetto 2021-01-29
a4bc59b Peter Carbonetto 2021-01-06
a0b6c2b Peter Carbonetto 2021-01-06
0411340 Peter Carbonetto 2021-01-06
fad8e3d Peter Carbonetto 2021-01-05

Perform differential expression analysis using the multinomial topic model, after removing the dendritic cells cluster:

rows <- which(samples$cluster != "dendritic")
fit_no_dendritic <- select_loadings(fit,loadings = rows)
diff_count_topics <- diff_count_analysis(fit_no_dendritic,counts[rows,])
# Fitting 21952 x 6 = 131712 univariate Poisson models.
# Computing log-fold change statistics.
# Stabilizing log-fold change estimates using adaptive shrinkage.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

These volcano plots summarize the results of the differential expression analysis using the topic model:

p14 <- volcano_plot(diff_count_topics,"k3",genes$symbol,
                    label_above_quantile = 0.995,
                    subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("topic 3 (B cells)")
p15 <- volcano_plot(diff_count_topics,"k2",genes$symbol,
                    label_above_quantile = 0.994,
                    subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("topic 2 (CD14+ cells)")
p16 <- volcano_plot(diff_count_topics,"k5",genes$symbol,
                    label_above_lfc = 2,label_above_quantile = 0.995,
                    subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("topic 5 (CD34+ cells)")
p17 <- volcano_plot(diff_count_topics,"k4",genes$symbol,
                    label_above_quantile = 0.995,
                    subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("topic 4 (NK cells)")
p18 <- volcano_plot(diff_count_topics,"k1",genes$symbol,
                    label_above_quantile = 0.998,
                    subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("topic 1 (T cells)")
p19 <- volcano_plot(diff_count_topics,"k6",genes$symbol,
                    label_above_quantile = 0.998,
                    subsample_below_quantile = 0.5) +
  guides(fill = "none") +
  ggtitle("topic 6 (ribosomal proteins)")
plot_grid(p14,p15,p16,p17,p18,p19,nrow = 2,ncol = 3)

Version Author Date
6e2b53a Peter Carbonetto 2021-02-10
9afb462 Peter Carbonetto 2021-02-10
f8e1d99 Peter Carbonetto 2021-02-10
d1d4185 Peter Carbonetto 2021-02-10
f596d8a Peter Carbonetto 2021-02-09
d0775d2 Peter Carbonetto 2021-02-09

The results of the differential expression analysis can also be browsed in interactive volcano plots:

volcano_plotly(diff_count_topics,"k3",
               "volcano_plot_purified_pbmc_bcells.html",
               genes$symbol,title = "topic 3 (B cells)",
               subsample_below_quantile = 0.5,
               width = 450,height = 500)
# 12049 out of 21952 data points will be included in plot
volcano_plotly(diff_count_topics,"k2",
               "volcano_plot_purified_pbmc_cd14.html",
               genes$symbol,title = "topic 2 (CD14+)",
               subsample_below_quantile = 0.5,
               width = 450,height = 500)
# 12049 out of 21952 data points will be included in plot
volcano_plotly(diff_count_topics,"k5",
               "volcano_plot_purified_pbmc_cd34.html",
               genes$symbol,title = "topic 5 (CD34+)",
               subsample_below_quantile = 0.5,
               width = 450,height = 500)
# 12049 out of 21952 data points will be included in plot
volcano_plotly(diff_count_topics,"k4",
               "volcano_plot_purified_pbmc_nk.html",
               genes$symbol,title = "topic 4 (NK cells)",
               subsample_below_quantile = 0.5,
               width = 450,height = 500)
# 12049 out of 21952 data points will be included in plot
volcano_plotly(diff_count_topics,"k1",
               "volcano_plot_purified_pbmc_tcells.html",
               genes$symbol,title = "topic 1 (T cells)",
               subsample_below_quantile = 0.5,
               width = 450,height = 500)
# 12049 out of 21952 data points will be included in plot
volcano_plotly(diff_count_topics,"k6",
               "volcano_plot_purified_pbmc_ribosomal_proteins.html",
               genes$symbol,title = "topic 6 (ribosomal proteins)",
               subsample_below_quantile = 0.5,
               width = 450,height = 500)
# 12049 out of 21952 data points will be included in plot
volcano_plotly(diff_count_clusters,"CD8+",
               "volcano_plot_purified_pbmc_cd8.html",
               genes$symbol,title = "CD8+ cluster",
               subsample_below_quantile = 0.5,
               width = 450,height = 500)
# 12091 out of 21952 data points will be included in plot
volcano_plotly(diff_count_clusters,"dendritic",
               "volcano_plot_purified_pbmc_dendritic.html",
               genes$symbol,title = "dendritic cells cluster",
               subsample_below_quantile = 0.5,
               width = 450,height = 500)
# 12074 out of 21952 data points will be included in plot

The interactive volcano plots may also be viewed by clicking these links: topic 3 (B cells), topic 4 (NK cells), topic 2 (CD14+), topic 5 (CD34+), topics 1 (T cells), topics 6 (T cells), CD8+ cluster and dendritic cells cluster.

The volcano plot for topic 6 suggests an enrichment of ribosomal protein genes. Indeed, there is a very strong correlation between the topic 6 mixture proportion and the fraction of total expression attributed to ribosomal genes:

rpgenes <- c("RPS2","RPS3","RPS3A","RPS4X","RPS6","RPS7","RPS8","RPS9",
             "RPS10","RPS11","RPS12","RPS13","RPS14","RPS15","RPS15A",
             "RPS16","RPS17","RPS18","RPS19","RPS20","RPS21","RPS23",
             "RPS24","RPS25","RPS26","RPS27","RPS27A","RPS28","RPS29",
             "RPL3","RPL4","RPL5","RPL6","RPL7A","RPL8","RPL9","RPL10",
             "RPL10A","RPL12","RPL13A","RPL14","RPL15","RPL17","RPL18",
             "RPL18A","RPL19","RPL21","RPL22","RPL23","RPL23A","RPL24",
             "RPL26","RPL27A","RPL30","RPL31","RPL32","RPL34","RPL35",
             "RPL36","RPL36A","RPL37","RPL39","RPL41")
rgscatterplot <- function (i, title = NULL) {
  j    <- which(is.element(genes$symbol,rpgenes))
  pdat <- data.frame(x = fit$L[i,6],
                     y = rowSums(counts[i,j])/rowSums(counts[i,]))
  return(ggplot(pdat,aes(x = x,y = y)) +
         geom_point() +
         geom_smooth(method = "lm",se = FALSE,color = "dodgerblue",
                     size = 0.5,linetype = "dashed") +
         ylim(0,0.6) +
         labs(x     = "topic 6 proportion",
              y     = "ribosomal expression",
              title = title) + 
         theme_cowplot(font_size = 10) +
         theme(plot.title = element_text(size = 10,face = "plain")))
}
p20 <- rgscatterplot(which(celltype == "CD19+ B"),"B cells")
p21 <- rgscatterplot(which(celltype == "CD34+"),"CD34+ cells")
p22 <- rgscatterplot(which(!(celltype == "CD19+ B" | celltype == "CD34+")),
                     "all other cells")
plot_grid(p20,p21,p22,nrow = 1,ncol = 3)

Version Author Date
6e2b53a Peter Carbonetto 2021-02-10
9afb462 Peter Carbonetto 2021-02-10

The list of ribosomal protein genes comes from Yoshihama et al (2002).

p10 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD19+ B","k3",
                       genes$symbol,label_above_quantile = 0.998,
                       xlab = "B cells FACS subpopulation",ylab = "topic 3")
p11 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD56+ NK","k4",
                       genes$symbol,label_above_quantile = 0.998,
                       xlab = "NK cells FACS subpopulation",ylab = "topic 4")
p12 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD14+ Monocyte","k2",
                       genes$symbol,label_above_quantile = 0.998,
                       xlab = "CD14+ FACS subpopulation",ylab = "topic 2")
p13 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"CD34+","k5",
                       genes$symbol,label_above_quantile = 0.998,
                       xlab = "CD34+ FACS subpopulation",ylab = "topic 4")
p14 <- lfc_scatterplot(diff_count_facs,diff_count_topics,"T cell","k1+k6",
                       genes$symbol,label_above_quantile = 0.998,
                       xlab = "T cells FACS subpopulation",
                       ylab = "topics 1 + 6")
plot_grid(p10,p11,p12,p13,p14,nrow = 3,ncol = 2)

Prepare the gene set data and differential expression results for the gene set enrichment analysis. First, align the gene-set data with the gene-wise statistics.

de        <- diff_count_topics
out       <- align_gene_data(gene_sets,de)
gene_sets <- out$gene_sets
de        <- out$diff_count_res
ids       <- rownames(gene_sets)
gene_info <- gene_info[match(ids,gene_info$Ensembl),]
genes     <- genes[match(ids,genes$ensembl),]

Next, remove gene sets with fewer than 4 genes, and with more than 400 genes.

i <- which(colSums(gene_sets) >= 4 & colSums(gene_sets) <= 400)
gene_set_info <- gene_set_info[i,]
gene_sets     <- gene_sets[,i]

For each topic, perform a gene-set enrichment analysis using fgsea.

gsea_res <- perform_gsea_all_topics(gene_sets,de,nproc = 4)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Add text here.

gsea_res$ES[is.na(gsea_res$ES)] <- 0
p <- create_gsea_plotly(gene_set_info,gsea_res,1,title = "")
saveWidget(p,"gsea_plot.html",selfcontained = TRUE)

sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
# 
# 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] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] htmlwidgets_1.5.1 plotly_4.9.2      cowplot_1.0.0     ggrepel_0.9.0    
# [5] ggplot2_3.3.0     fgsea_1.15.1      fastTopics_0.5-18 Matrix_1.2-18    
# 
# loaded via a namespace (and not attached):
#  [1] httr_1.4.2           tidyr_1.0.0          splines_3.6.2       
#  [4] jsonlite_1.6         viridisLite_0.3.0    RcppParallel_4.4.2  
#  [7] shiny_1.4.0          assertthat_0.2.1     mixsqp_0.3-44       
# [10] yaml_2.2.0           progress_1.2.2       pillar_1.4.3        
# [13] backports_1.1.5      lattice_0.20-38      quantreg_5.54       
# [16] glue_1.3.1           quadprog_1.5-8       digest_0.6.23       
# [19] promises_1.1.0       colorspace_1.4-1     htmltools_0.4.0     
# [22] httpuv_1.5.2         pkgconfig_2.0.3      invgamma_1.1        
# [25] SparseM_1.78         xtable_1.8-4         purrr_0.3.3         
# [28] scales_1.1.0         whisker_0.4          later_1.0.0         
# [31] Rtsne_0.15           BiocParallel_1.18.1  MatrixModels_0.4-1  
# [34] git2r_0.26.1         tibble_2.1.3         mgcv_1.8-31         
# [37] farver_2.0.1         withr_2.1.2          ashr_2.2-51         
# [40] lazyeval_0.2.2       mime_0.8             magrittr_1.5        
# [43] crayon_1.3.4         mcmc_0.9-6           evaluate_0.14       
# [46] fs_1.3.1             nlme_3.1-142         MASS_7.3-51.4       
# [49] truncnorm_1.0-8      tools_3.6.2          data.table_1.12.8   
# [52] prettyunits_1.1.1    hms_0.5.2            lifecycle_0.1.0     
# [55] stringr_1.4.0        MCMCpack_1.4-5       munsell_0.5.0       
# [58] irlba_2.3.3          compiler_3.6.2       rlang_0.4.5         
# [61] grid_3.6.2           crosstalk_1.0.0      labeling_0.3        
# [64] rmarkdown_2.3        gtable_0.3.0         R6_2.4.1            
# [67] gridExtra_2.3        knitr_1.26           dplyr_0.8.3         
# [70] fastmap_1.0.1        zeallot_0.1.0        fastmatch_1.1-0     
# [73] workflowr_1.6.2.9000 rprojroot_1.3-2      stringi_1.4.3       
# [76] parallel_3.6.2       SQUAREM_2017.10-1    Rcpp_1.0.5          
# [79] vctrs_0.2.1          tidyselect_0.2.5     xfun_0.11           
# [82] coda_0.19-3