Gene analysis using topic modeling results for Cusanovich et al (2018) scATAC-seq data

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Last updated: 2021-02-04

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Knit directory: scATACseq-topics/

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---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/gene_analysis_Cusanovich2018.Rmd) and HTML (docs/gene_analysis_Cusanovich2018.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	dd6949d	kevinlkx	2021-02-04	removed the DT tables of the tissue or cell composition in topics
html	027dab2	kevinlkx	2021-02-04	Build site.
Rmd	2edcd80	kevinlkx	2021-02-04	added marker gene plots
html	5e832d2	kevinlkx	2021-01-22	Build site.
Rmd	8a44752	kevinlkx	2021-01-22	minor update on highlighting the gene sets of GSEA results
html	ca6ea6f	kevinlkx	2021-01-22	Build site.
Rmd	8f3685f	kevinlkx	2021-01-22	update figure directory and links to plotly html files

---

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)

Past versions of structure-plot-tissue-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04
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)

Past versions of structure-plot-kmeans-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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")

Past versions of freq-table-cluster-tissue-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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")

Past versions of freq-table-cluster-celllabel-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of gene-volcano-plot-tss-1-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04
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)
}

Past versions of marker-gene-scores-tss-1-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of gene-volcano-plot-tss-3-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)
}

Past versions of marker-gene-scores-tss-3-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of gene-volcano-plot-tss-5-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)
}

Past versions of marker-gene-scores-tss-5-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of gene-volcano-plot-tss-7-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)
}

Past versions of marker-gene-scores-tss-7-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of gene-volcano-plot-tss-8-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)
}

Past versions of marker-gene-scores-8-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of gene-volcano-plot-genebody-1-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)
}

Past versions of marker-gene-scores-genebody-1-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of gene-volcano-plot-genebody-3-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)
}

Past versions of marker-gene-scores-genebody-3-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of gene-volcano-plot-genebody-5-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)
}

Past versions of marker-gene-scores-genebody-5-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of gene-volcano-plot-genebody-7-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)
}

Past versions of marker-gene-scores-genebody-7-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of gene-volcano-plot-genebody-8-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)
}

Past versions of marker-gene-scores-genebody-8-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04

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)

Past versions of compare-genebody-tss-1.png

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))

Past versions of gsea-plot-tss-1-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04
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))

Past versions of gsea-plot-genebody-1-1.png

Version	Author	Date
027dab2	kevinlkx	2021-02-04
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")
}

Session information

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