Last updated: 2022-02-24

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Knit directory: scATACseq-topics/

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    Modified:   scripts/fit_all_models_Cusanovich2018.sh
    Modified:   scripts/postfit_Buenrostro2018_Chen2019pipeline.sh
    Modified:   scripts/postfit_Cusanovich2018.sh
    Modified:   scripts/postfit_gene_analysis.sbatch

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


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 0ffb5c6 kevinlkx 2022-02-24 wflow_publish("analysis/gene_analysis_Cusanovich2018_v2.Rmd")
html c010fd7 kevinlkx 2022-02-22 Build site.
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)

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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)

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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")

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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")

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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):

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 ver1

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)

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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)

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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)
}

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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)

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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)
}

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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)

Version Author Date
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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
c010fd7 kevinlkx 2022-02-22
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)

Version Author Date
c010fd7 kevinlkx 2022-02-22

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
c010fd7 kevinlkx 2022-02-22
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)

Version Author Date
c010fd7 kevinlkx 2022-02-22

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
c010fd7 kevinlkx 2022-02-22
4b5247c kevinlkx 2022-02-17

TSS model ver2

  • use Z scores
  • normalized by the sum of weights

  • Top genes

gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-Z-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-Z-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)
}

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)
}

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)
}

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)
}

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)
}

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)

Version Author Date
c010fd7 kevinlkx 2022-02-22

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)

Version Author Date
c010fd7 kevinlkx 2022-02-22

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
c010fd7 kevinlkx 2022-02-22
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)

Version Author Date
c010fd7 kevinlkx 2022-02-22

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
c010fd7 kevinlkx 2022-02-22
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)

Version Author Date
c010fd7 kevinlkx 2022-02-22

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
c010fd7 kevinlkx 2022-02-22
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)

Version Author Date
c010fd7 kevinlkx 2022-02-22

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
c010fd7 kevinlkx 2022-02-22
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)

Version Author Date
c010fd7 kevinlkx 2022-02-22

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
c010fd7 kevinlkx 2022-02-22
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
c010fd7 kevinlkx 2022-02-22
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_1.0.8       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_2.0-3    
#   [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_3.2.0            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_1.6.2           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_1.0.1          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_1.1.2     parallelly_1.30.0   
#  [88] plyr_1.8.6           magrittr_2.0.2       R6_2.5.1            
#  [91] generics_0.1.2       DBI_1.1.2            pillar_1.7.0        
#  [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_1.5.0         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