Last updated: 2021-02-04

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Here we perform gene analysis for the Cusanovich et al (2018) scATAC-seq result inferred from the multinomial topic model with \(k = 13\).

Load packages and some functions used in this analysis

library(Matrix)
library(fastTopics)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
library(cowplot)
library(plotly)
library(htmlwidgets)
library(DT)
library(reshape)
source("code/plots.R")

Load samples and topic model (\(k = 13\)) results.

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/"
samples <- readRDS(paste0(out.dir, "/samples-clustering-Cusanovich2018.rds"))
fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/"
fit <- readRDS(file.path(fit.dir, "/fit-Cusanovich2018-scd-ex-k=13.rds"))$fit
fit_multinom <- poisson2multinom(fit)

Visualize by Structure plot grouped by tissues

set.seed(10)
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
                   "#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
                   "gray")
rows <- sample(nrow(fit$L),4000)
samples$tissue <- as.factor(samples$tissue)

p.structure <- structure_plot(select(fit_multinom,loadings = rows),
                              grouping = samples[rows, "tissue"],n = Inf,gap = 40,
                              perplexity = 50,topics = 1:13,colors = colors_topics,
                              num_threads = 4,verbose = FALSE)

print(p.structure)

Version Author Date
ca6ea6f kevinlkx 2021-01-22

Load the clustering results

set.seed(10)
rows <- sample(nrow(fit$L),4000)

p.structure.kmeans <- structure_plot(select(fit_multinom,loadings = rows),
                                     grouping = samples$cluster_kmeans[rows],n = Inf,gap = 40,
                                     perplexity = 50,topics = 1:13,colors = colors_topics,
                                     num_threads = 4,verbose = FALSE)
print(p.structure.kmeans)

Distribution of tissue labels by cluster.

freq_table_cluster_tissue <- with(samples,table(tissue,cluster_kmeans))

freq_table_cluster_tissue <- as.data.frame.matrix(freq_table_cluster_tissue)
DT::datatable(freq_table_cluster_tissue, 
              options = list(pageLength = nrow(freq_table_cluster_tissue)), 
              rownames = T, caption = "Number of cells")

create_celllabel_cluster_heatmap(samples$tissue, samples$cluster_kmeans, normalize_by = "column")

Distribution of cell labels by cluster.

freq_table_cluster_celllabel <- with(samples,table(cell_label,cluster_kmeans))

freq_table_cluster_celllabel <- as.data.frame.matrix(freq_table_cluster_celllabel)
DT::datatable(freq_table_cluster_celllabel, 
              options = list(pageLength = nrow(freq_table_cluster_celllabel)), 
              rownames = T, caption = "Number of cells")

create_celllabel_cluster_heatmap(samples$cell_label, samples$cluster_kmeans, normalize_by = "column")

Top 5 cell types

top_celltypes_table <- data.frame(matrix(nrow=5, ncol = ncol(freq_table_cluster_celllabel)))
colnames(top_celltypes_table) <- colnames(freq_table_cluster_celllabel)
for (k in 1:ncol(freq_table_cluster_celllabel)){
  top_celltypes <- rownames(freq_table_cluster_celllabel)[head(order(freq_table_cluster_celllabel[,k], decreasing=TRUE), 5)]
  freq_top_celltypes <- freq_table_cluster_celllabel[top_celltypes, k]
  percent_top_celltypes <- freq_table_cluster_celllabel[top_celltypes, k]/sum(freq_table_cluster_celllabel[,k])
  top_celltypes_table[,k] <- sprintf("%s (%.1f%%)", top_celltypes, percent_top_celltypes*100)
}

DT::datatable(top_celltypes_table, rownames = T, caption = "Top 5 cell types in each cluster")

We can see the major cell types in the clusters (topics): * cluster 1 (topic 1): Erythroblasts * cluster 2 (topic 3): Endothelial cells * cluster 3 (topic 7): Cardiomyocytes cells * cluster 4 (topic 6): B cells, Monocytes, Dendritic cells * cluster 5 (topic 9): Astrocytes, Oligodendrocytes * cluster 8 (topic 5): Proximal tubule * cluster 10 (topic 4): a mixture of pneumocytes, Loop of henle, Enterocytes, DCT/CD * cluster 11 (topic 12): T cells * cluster 13 (topic 2): Cerebellar granule cells * cluster 14 (topic 11): Ex. neurons and Inhibitory neurons * cluster 15 (topic 8): Hepatocytes

Gene score analysis

Set output directorry

fig.dir <- "output/plotly/Cusanovich2018"
dir.create(fig.dir, showWarnings = F, recursive = T)

TSS model

Gene scores were computed using TSS based method as in Lareau et al Nature Biotech, 2019 as well as the model 21 in archR paper. This model weights chromatin accessibility around gene promoters by using bi-directional exponential decays from the TSS.

  • Top genes
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res_tss <- readRDS(file.path(gene.dir, "genescore_result_topics.rds"))
genescore_res <- genescore_res_tss

genes <- genescore_res$genes
gene_mean_acc <- genescore_res$colmeans
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$beta

topics <- colnames(gene_scores)
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- topics

for (k in topics){
  top_genes[,k] <- genes$SYMBOL[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}

DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F, caption = "Top 10 genes by abs(gene z-scores)")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018//geneanalysis-Cusanovich2018-k=13-TSS-sum
  • Volcano plots

Explore the volcano plots [interactively][volcano-plotly-tss-k]

for ( k in 1:ncol(genescore_res$beta) ){
  p.volcano.plotly <- genescore_volcano_plotly(genescore_res,k,
                                             file = sprintf("%s/volcano_topic_%s_%s.html", fig.dir, k, "tss-sum"),
                                             labels = genescore_res$genes$SYMBOL)
}

Topic 1

k <- 1
genescore_volcano_plot(genescore_res, k, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

Version Author Date
ca6ea6f kevinlkx 2021-01-22

Check some known marker genes for endothelial cells

marker_genes <- c("Hbb-b1", "Hbb-b2", "Gypa")

gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 3 (Endothelial cells)

k <- 3
genescore_volcano_plot(genescore_res, k, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

Check some known marker genes for erythrocyte

marker_genes <- c("PECAM1", "CD106", "CD62E", "Sele", "Kdr", "ENG")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 5 (Proximal tubule)

k <- 5
genescore_volcano_plot(genescore_res, k, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

Check some known marker genes for Proximal tubule

marker_genes <- c("PALDOB", "CUBN", "LRP2", "SLC34A1")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 7 (Cardiomyocytes)

k <- 7
genescore_volcano_plot(genescore_res, k, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

Check some known marker genes for Cardiomyocytes

marker_genes <- c("Nppa", "Myl4", "SLN", "PITX2", "Myl7", "Gja5", "Myl2", "Myl3", "IRX4", "HAND1", "HEY2")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/3),3))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 8 (Hepatocytes)

k <- 8
genescore_volcano_plot(genescore_res, k, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

Check some known marker genes for Hepatocytes

marker_genes <- c("SERPINA1", "TTR", "ALB","AFP","CYP3A4","CYP7A1","FABP1","ALR","Glut1","MET","FoxA1","FoxA2","CD29","PTP4A2","Prox1", "HNF1B")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/3),3))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Gene body model

Gene scores were computed using the gene score model (model 42) in the archR paper with some modifications. This model uses bi-directional exponential decays from the gene TSS (extended upstream by 5 kb by default) and the gene transcription termination site (TTS). Note: the current version of the function does not account for neighboring gene boundaries.

  • Top genes
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res_gb <- readRDS(file.path(gene.dir, "genescore_result_topics.rds"))
genescore_res <- genescore_res_gb

genes <- genescore_res$genes
gene_mean_acc <- genescore_res$colmeans
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$beta

topics <- colnames(gene_scores)
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- topics

for (k in topics){
  top_genes[,k] <- genes$SYMBOL[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}

DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F, caption = "Top 10 genes by abs(gene z-scores)")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018//geneanalysis-Cusanovich2018-k=13-genebody-sum
  • Volcano plots

Explore the volcano plot interactively

for ( k in 1:ncol(genescore_res$beta) ){
  p.volcano.plotly <- genescore_volcano_plotly(genescore_res,k,
                                             file = sprintf("%s/volcano_topic_%s_%s.html", fig.dir, k, "genebody-sum"),
                                             labels = genescore_res$genes$SYMBOL)
}

Topic 1

k <- 1
genescore_volcano_plot(genescore_res, k, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

Check some known marker genes for endothelial cells

marker_genes <- c("Hbb-b1", "Hbb-b2", "Gypa")

gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 3 (Endothelial cells)

k <- 3
genescore_volcano_plot(genescore_res, k, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

Check some known marker genes for erythrocyte

marker_genes <- c("PECAM1", "CD106", "CD62E", "Sele", "Kdr", "ENG")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 5 (Proximal tubule)

k <- 5
genescore_volcano_plot(genescore_res, k, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

Check some known marker genes for Proximal tubule

marker_genes <- c("PALDOB", "CUBN", "LRP2", "SLC34A1")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 7 (Cardiomyocytes)

k <- 7
genescore_volcano_plot(genescore_res, k, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

Check some known marker genes for Cardiomyocytes

marker_genes <- c("Nppa", "Myl4", "SLN", "PITX2", "Myl7", "Gja5", "Myl2", "Myl3", "IRX4", "HAND1", "HEY2")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/3),3))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 8 (Hepatocytes)

k <- 8
genescore_volcano_plot(genescore_res, k, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

Check some known marker genes for Hepatocytes

marker_genes <- c("SERPINA1", "TTR", "ALB","AFP","CYP3A4","CYP7A1","FABP1","ALR","Glut1","MET","FoxA1","FoxA2","CD29","PTP4A2","Prox1", "HNF1B")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/3),3))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Compare gene scores from the gene-body model vs. the TSS model.

m     <- ncol(genescore_res_gb$Z)
plots <- vector("list",m)
names(plots) <- colnames(genescore_res_gb$Z)
for (i in 1:m) {
  dat <- data.frame(genebody = genescore_res_gb$Z[,i], tss = genescore_res_tss$Z[,i])
  plots[[i]] <- 
    ggplot(dat,aes_string(x = "genebody",y = "tss")) +
    geom_point(shape = 21, na.rm = TRUE, size = 1, alpha = 1/10) +
    geom_abline(intercept = 0, slope = 1, color="blue") + 
    labs(x = "gene body model",y = "TSS model", 
         title = paste("topic",i)) +
    theme_cowplot(9)
}

do.call(plot_grid,plots)

Version Author Date
ca6ea6f kevinlkx 2021-01-22

Gene-set enrichment analysis (GSEA)

TSS model

Top gene sets/pathways

gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
load(file.path(gene.dir, "genescores_gsea.Rdata"))

top_pathways_up <- top_pathways_down <- data.frame(matrix(nrow=10, ncol = ncol(gsea_res$pval)))
colnames(top_pathways_up) <- colnames(top_pathways_down) <- colnames(gsea_res$pval)

for (k in 1:ncol(gsea_res$pval)){
  gsea_topic <- data.frame(pathway = rownames(gsea_res$pval),  
                           pval = gsea_res$pval[,k],
                           log2err = gsea_res$log2err[,k],
                           ES = gsea_res$ES[,k],
                           NES = gsea_res$NES[,k])
  gsea_up <- gsea_topic[gsea_topic$ES > 0,]
  top_IDs_up <- as.character(gsea_up[head(order(gsea_up$pval), 10), "pathway"])
  top_IDs_up <- gene_set_info[match(top_IDs_up, gene_set_info$id),c("name", "id")]
  top_pathways_up[,k] <- paste0(top_IDs_up$name, "(", top_IDs_up$id, ")")
  
  gsea_down <- gsea_topic[gsea_topic$ES < 0,]
  top_IDs_down <- as.character(gsea_down[head(order(gsea_down$pval), 10), "pathway"])
  top_IDs_down <- gene_set_info[match(top_IDs_down, gene_set_info$id),c("name", "id")]
  top_pathways_down[,k] <- paste0(top_IDs_down$name, "(", top_IDs_down$id, ")")
  
}

DT::datatable(data.frame(rank = 1:10, top_pathways_up), rownames = F,
              caption = "Top 10 pathways enriched at the top of the gene rank list.")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018//geneanalysis-Cusanovich2018-k=13-TSS-sum
  • Scatterplot of GSEA results Topic 1 (highlighting "Erythroblast" cells)
k   <- 1
name_interest <- "erythroblast"
label_geneset_ids <- gene_set_info[gsea_res$pval[,k] < 1e-6 & grepl(name_interest, gene_set_info$name, ignore.case = T), "id"]
cat(sprintf("highlighting %d %s gene sets \n", length(label_geneset_ids), name_interest))

create_genescore_gsea_plot(gene_set_info, gsea_res, k, label_geneset_ids, 
                                     title = sprintf("topic %d GSEA plot",k))

Version Author Date
5e832d2 kevinlkx 2021-01-22
ca6ea6f kevinlkx 2021-01-22 
if(length(label_geneset_ids) > 0)
   DT::datatable(gene_set_info[gene_set_info$id %in% label_geneset_ids, 
                               c("name", "id", "database", "category_code", "description_brief")],  
                 rownames = F)
# highlighting 3 erythroblast gene sets

Explore the GSEA plot interactively

for ( k in 1:ncol(gsea_res$pval)){
  p.gsea.plotly <- create_genescore_gsea_plotly(gene_set_info, gsea_res, k)
  saveWidget(p.gsea.plotly,file = sprintf("%s/gsea_topic_%s_%s.html", fig.dir, k, "tss-sum"),
             selfcontained = TRUE,title = "GSEA")
}

Gene body model

  • Top gene sets
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
load(file.path(gene.dir, "genescores_gsea.Rdata"))

top_pathways_up <- top_pathways_down <- data.frame(matrix(nrow=10, ncol = ncol(gsea_res$pval)))
colnames(top_pathways_up) <- colnames(top_pathways_down) <- colnames(gsea_res$pval)

for (k in 1:ncol(gsea_res$pval)){
  gsea_topic <- data.frame(pathway = rownames(gsea_res$pval),  
                           pval = gsea_res$pval[,k],
                           log2err = gsea_res$log2err[,k],
                           ES = gsea_res$ES[,k],
                           NES = gsea_res$NES[,k])
  gsea_up <- gsea_topic[gsea_topic$ES > 0,]
  top_IDs_up <- as.character(gsea_up[head(order(gsea_up$pval), 10), "pathway"])
  top_pathways_up[,k] <- gene_set_info[match(top_IDs_up, gene_set_info$id),c("name", "id")] %>% 
    unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
  
  gsea_down <- gsea_topic[gsea_topic$ES < 0,]
  top_IDs_down <- as.character(gsea_down[head(order(gsea_down$pval), 10), "pathway"])
  top_pathways_down[,k] <- gene_set_info[match(top_IDs_down, gene_set_info$id),c("name", "id")] %>% 
    unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
}

DT::datatable(data.frame(rank = 1:10, top_pathways_up), rownames = F,
              caption = "Top 10 pathways enriched at the top of the gene rank list.")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018//geneanalysis-Cusanovich2018-k=13-genebody-sum
  • Scatterplot of GSEA results Topic 1 (highlighting "Erythroblast" cells)
k   <- 1
name_interest <- "erythroblast"
label_geneset_ids <- gene_set_info[gsea_res$pval[,k] < 1e-6 & grepl(name_interest, gene_set_info$name, ignore.case = T), "id"]
cat(sprintf("highlighting %d %s gene sets \n", length(label_geneset_ids), name_interest))

create_genescore_gsea_plot(gene_set_info, gsea_res, k, label_geneset_ids, 
                                     title = sprintf("topic %d GSEA plot",k))

Version Author Date
5e832d2 kevinlkx 2021-01-22
ca6ea6f kevinlkx 2021-01-22 
if(length(label_geneset_ids) > 0)
   DT::datatable(gene_set_info[gene_set_info$id %in% label_geneset_ids, 
                               c("name", "id", "database", "category_code", "description_brief")],  
                 rownames = F)
# highlighting 0 erythroblast gene sets

Explore the GSEA plot interactively

for ( k in 1:ncol(gsea_res$pval)){
  p.gsea.plotly <- create_genescore_gsea_plotly(gene_set_info, gsea_res, k)
saveWidget(p.gsea.plotly,file = sprintf("%s/gsea_topic_%s_%s.html", fig.dir, k, "genebody-sum"),
           selfcontained = TRUE,title = "GSEA")
}

sessionInfo()
# R version 3.6.1 (2019-07-05)
# 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] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
#  [1] reshape_0.8.8     DT_0.16           htmlwidgets_1.5.3 plotly_4.9.3     
#  [5] cowplot_1.1.1     ggrepel_0.9.1     ggplot2_3.3.3     tidyr_1.1.2      
#  [9] dplyr_1.0.3       fastTopics_0.4-29 Matrix_1.2-18     workflowr_1.6.2  
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.6         lattice_0.20-41    prettyunits_1.1.1  rprojroot_2.0.2   
#  [5] digest_0.6.27      plyr_1.8.6         R6_2.5.0           MatrixModels_0.4-1
#  [9] evaluate_0.14      coda_0.19-4        httr_1.4.2         pillar_1.4.7      
# [13] rlang_0.4.10       progress_1.2.2     lazyeval_0.2.2     data.table_1.13.6 
# [17] irlba_2.3.3        SparseM_1.78       whisker_0.4        rmarkdown_2.6     
# [21] labeling_0.4.2     Rtsne_0.15         stringr_1.4.0      munsell_0.5.0     
# [25] compiler_3.6.1     httpuv_1.5.4       xfun_0.19          pkgconfig_2.0.3   
# [29] mcmc_0.9-7         htmltools_0.5.1.1  tidyselect_1.1.0   tibble_3.0.6      
# [33] quadprog_1.5-8     matrixStats_0.58.0 viridisLite_0.3.0  withr_2.4.1       
# [37] crayon_1.4.0       conquer_1.0.2      later_1.1.0.1      MASS_7.3-53       
# [41] grid_3.6.1         jsonlite_1.7.2     gtable_0.3.0       lifecycle_0.2.0   
# [45] DBI_1.1.0          git2r_0.27.1       magrittr_2.0.1     scales_1.1.1      
# [49] RcppParallel_5.0.2 stringi_1.5.3      farver_2.0.3       fs_1.3.1          
# [53] promises_1.1.1     ellipsis_0.3.1     generics_0.1.0     vctrs_0.3.6       
# [57] tools_3.6.1        glue_1.4.2         purrr_0.3.4        crosstalk_1.1.1   
# [61] hms_1.0.0          yaml_2.2.1         colorspace_2.0-0   knitr_1.30        
# [65] quantreg_5.83      MCMCpack_1.5-0