Gene analysis using topic modeling and DA results (v2) for Cusanovich et al (2018) scATAC-seq data

Last updated: 2022-02-22

Checks: 7 0

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_v2.Rmd) and HTML (docs/gene_analysis_Cusanovich2018_v2.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	a6b27b6	kevinlkx	2022-02-22	added gene volcano plots
html	4b5247c	kevinlkx	2022-02-17	Build site.
Rmd	57ff8a0	kevinlkx	2022-02-17	updated with v2 of DA analysis

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(pathways)
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.

data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
samples <- readRDS(paste0(data.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 <- 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,loadings = rows),
                              grouping = samples[rows, "tissue"],n = Inf,gap = 40,
                              perplexity = 50,colors = colors_topics,
                              num_threads = 4,verbose = FALSE)

print(p.structure)

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

Load the clustering results

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

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

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

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")

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

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")

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

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

Differential accessbility analysis of the ATAC-seq regions for the topics

Load results from differential accessbility analysis for the topics

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2"
cat(sprintf("Load results from %s \n", out.dir))
DA_res <- readRDS(file.path(out.dir, paste0("DAanalysis-Cusanovich2018-k=13/DA_regions_topics_10000iters.rds")))

# Load results from /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2

Gene score analysis

Set output directory

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2"

fig.dir <- "output/plotly/Cusanovich2018_v2"
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.

TSS model
use abs(z) scores
normalized by the sum of weights
Top genes

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

genes <- genescore_res$genes
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$logFC

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/postfit_v2/geneanalysis-Cusanovich2018-k=13-TSS-absZ-sum

Volcano plots

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

Topic 1 (Erythroblasts)

genescore_volcano_plot(genescore_res, k = 1, 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

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)
}

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

Topic 3 (Endothelial cells)

genescore_volcano_plot(genescore_res, k = 3, 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

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)
}

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

Topic 5 (Proximal tubule)

genescore_volcano_plot(genescore_res, k = 5, 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

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)
}

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

Topic 7 (Cardiomyocytes)

genescore_volcano_plot(genescore_res, k = 7, 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

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)
}

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

Topic 8 (Hepatocytes)

genescore_volcano_plot(genescore_res, k = 8, 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

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)
}

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

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-absZ-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_gb_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))
genescore_res <- genescore_gb_res

genes <- genescore_res$genes
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$logFC

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/postfit_v2/geneanalysis-Cusanovich2018-k=13-genebody-absZ-sum

Volcano plots

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

Topic 1 (Erythroblasts)

genescore_volcano_plot(genescore_res, k = 1, 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

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)
}

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

Topic 3 (Endothelial cells)

genescore_volcano_plot(genescore_res, k = 3, 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

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)
}

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

Topic 5 (Proximal tubule)

genescore_volcano_plot(genescore_res, k = 5, 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

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)
}

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

Topic 7 (Cardiomyocytes)

genescore_volcano_plot(genescore_res, k = 7, 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

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)
}

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

Topic 8 (Hepatocytes)

genescore_volcano_plot(genescore_res, k = 8, 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)
}

Version	Author	Date
4b5247c	kevinlkx	2022-02-17

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

m     <- ncol(genescore_gb_res$Z)
plots <- vector("list",m)
names(plots) <- colnames(genescore_gb_res$Z)
for (i in 1:m) {
  dat <- data.frame(genebody = genescore_gb_res$Z[,i], tss = genescore_tss_res$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
4b5247c	kevinlkx	2022-02-17

Gene-set enrichment analysis (GSEA)

Loading gene set data

cat("Loading mouse gene set data.\n")
data(gene_sets_mouse)
gene_sets <- gene_sets_mouse$gene_sets
gene_set_info <- gene_sets_mouse$gene_set_info

# Loading mouse gene set data.

TSS model

Top gene sets/pathways

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

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/postfit_v2/geneanalysis-Cusanovich2018-k=13-TSS-absZ-sum

Gene body model

Top gene sets

gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-absZ-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
gsea_res <- readRDS(file.path(gene.dir, "gsea_result.rds"))

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/postfit_v2/geneanalysis-Cusanovich2018-k=13-genebody-absZ-sum

sessionInfo()

# R version 4.0.4 (2021-02-15)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Scientific Linux 7.4 (Nitrogen)
# 
# Matrix products: default
# BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.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_0.20           htmlwidgets_1.5.4 plotly_4.10.0    
#  [5] cowplot_1.1.1     ggrepel_0.9.1     ggplot2_3.3.5     tidyr_1.1.4      
#  [9] dplyr_1.0.7       pathways_0.1-20   fastTopics_0.6-97 Matrix_1.4-0     
# [13] workflowr_1.7.0  
# 
# loaded via a namespace (and not attached):
#   [1] fgsea_1.21.0         Rtsne_0.15           colorspace_2.0-2    
#   [4] ellipsis_0.3.2       class_7.3-20         rprojroot_2.0.2     
#   [7] fs_1.5.2             rstudioapi_0.13      farver_2.1.0        
#  [10] listenv_0.8.0        MatrixModels_0.5-0   prodlim_2019.11.13  
#  [13] fansi_1.0.2          lubridate_1.8.0      codetools_0.2-18    
#  [16] splines_4.0.4        knitr_1.37           jsonlite_1.7.3      
#  [19] pROC_1.18.0          mcmc_0.9-7           caret_6.0-90        
#  [22] ashr_2.2-47          uwot_0.1.11          compiler_4.0.4      
#  [25] httr_1.4.2           assertthat_0.2.1     fastmap_1.1.0       
#  [28] lazyeval_0.2.2       cli_3.1.1            later_1.3.0         
#  [31] prettyunits_1.1.1    htmltools_0.5.2      quantreg_5.86       
#  [34] tools_4.0.4          coda_0.19-4          gtable_0.3.0        
#  [37] glue_1.6.1           reshape2_1.4.4       fastmatch_1.1-3     
#  [40] Rcpp_1.0.8           jquerylib_0.1.4      vctrs_0.3.8         
#  [43] nlme_3.1-155         conquer_1.2.1        crosstalk_1.2.0     
#  [46] iterators_1.0.13     timeDate_3043.102    gower_0.2.2         
#  [49] xfun_0.29            stringr_1.4.0        globals_0.14.0      
#  [52] ps_1.6.0             lifecycle_1.0.1      irlba_2.3.5         
#  [55] future_1.23.0        getPass_0.2-2        MASS_7.3-55         
#  [58] scales_1.1.1         ipred_0.9-12         hms_1.1.1           
#  [61] promises_1.2.0.1     parallel_4.0.4       SparseM_1.81        
#  [64] yaml_2.2.2           gridExtra_2.3        pbapply_1.5-0       
#  [67] sass_0.4.0           rpart_4.1-15         stringi_1.7.6       
#  [70] SQUAREM_2021.1       highr_0.9            foreach_1.5.1       
#  [73] BiocParallel_1.24.1  lava_1.6.10          truncnorm_1.0-8     
#  [76] rlang_1.0.0          pkgconfig_2.0.3      matrixStats_0.61.0  
#  [79] evaluate_0.14        lattice_0.20-45      invgamma_1.1        
#  [82] purrr_0.3.4          labeling_0.4.2       recipes_0.1.17      
#  [85] processx_3.5.2       tidyselect_1.1.1     parallelly_1.30.0   
#  [88] plyr_1.8.6           magrittr_2.0.2       R6_2.5.1            
#  [91] generics_0.1.1       DBI_1.1.2            pillar_1.6.5        
#  [94] whisker_0.4          withr_2.4.3          survival_3.2-13     
#  [97] mixsqp_0.3-43        nnet_7.3-17          tibble_3.1.6        
# [100] future.apply_1.8.1   crayon_1.4.2         utf8_1.2.2          
# [103] rmarkdown_2.11       progress_1.2.2       grid_4.0.4          
# [106] data.table_1.14.2    callr_3.7.0          git2r_0.29.0        
# [109] ModelMetrics_1.2.2.2 digest_0.6.29        httpuv_1.6.5        
# [112] MCMCpack_1.6-0       RcppParallel_5.1.5   stats4_4.0.4        
# [115] munsell_0.5.0        viridisLite_0.4.0    bslib_0.3.1         
# [118] quadprog_1.5-8

Gene analysis using topic modeling and DA results (v2) for Cusanovich et al (2018) scATAC-seq data

Kaixuan Luo

Load packages and some functions used in this analysis

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

Visualize by Structure plot grouped by tissues

Differential accessbility analysis of the ATAC-seq regions for the topics

Gene score analysis

TSS model

Gene body model

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

Gene-set enrichment analysis (GSEA)

TSS model

Gene body model