Motif analysis using topic modeling results for Buenrostro et al (2018) scATAC-seq result (data processed using Chen 2019 pipeline)

Last updated: 2021-01-28

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Knit directory: scATACseq-topics/

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Rmd	c1f6331	kevinlkx	2021-01-28	change the example volcano plot to topic 4
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Rmd	3608c72	kevinlkx	2021-01-28	motif analysis of Buenrostro2018 data with topic 4 examples

Here we perform TF motif analysis for the Buenrostro et al (2018) scATAC-seq result inferred from the multinomial topic model with \(k = 11\).

We use binarized scPeaks and scATAC-seq data was processed using Chen et al (2019) pipeline.

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(reshape2)
library(Logolas)
library(grid)
source("code/motif_analysis.R")
source("code/plots.R")

Load data and topic model results

Load the binarized data and the \(k = 11\) Poisson NMF fit results

data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data_Chen2019pipeline/"
load(file.path(data.dir, "Buenrostro_2018_binarized_counts.RData"))
cat(sprintf("%d x %d counts matrix.\n",nrow(counts),ncol(counts)))

# 2034 x 101172 counts matrix.

fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/"
fit <- readRDS(file.path(fit.dir, "/fit-Buenrostro2018-binarized-scd-ex-k=11.rds"))$fit
fit_multinom <- poisson2multinom(fit)

Visualize by Structure plot grouped by cell labels.

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

p.structure <- structure_plot(fit_multinom,
                     grouping = samples[, "label"],n = Inf,gap = 40,
                     perplexity = 50,topics = 1:11,colors = colors_topics,
                     num_threads = 4,verbose = FALSE)

print(p.structure)

Version	Author	Date
c7742f0	kevinlkx	2021-01-28

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/Buenrostro_2018_Chen2019pipeline/binarized/"
cat(sprintf("Load results from %s \n", out.dir))

# Load results from /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/

diff_count_topics <- readRDS(file.path(out.dir, "diffcount-Buenrostro2018-11topics.rds"))

Distribution of z-scores

zscore_topics <-  melt(diff_count_topics$Z)
colnames(zscore_topics) <- c("region", "topic", "zscore")
levels(zscore_topics$topic) <- colnames(diff_count_topics$Z)

z.quantile.99 <- apply(abs(diff_count_topics$Z), 2, quantile, 0.99)
cat("z-score 99% quantile: \n")
print(z.quantile.99)

p.hist.zscores <- ggplot(zscore_topics, aes(x=zscore)) + 
  geom_histogram(binwidth=1, color="black", fill="white") + 
  coord_cartesian(xlim = c(-10, 30)) + theme_cowplot(font_size = 10) +
  facet_wrap(~ topic, ncol=4)

print(p.hist.zscores)

Version	Author	Date
c7742f0	kevinlkx	2021-01-28

# z-score 99% quantile: 
#        k1        k2        k3        k4        k5        k6        k7        k8 
#  7.680613  7.226323  5.954049 10.059326  9.203917  5.821598  7.215842  8.156498 
#        k9       k10       k11 
#  5.978363  6.684635  8.191629

Volcano plot of the regions

topic 4 example

volcano_plot(diff_count_topics,k = 4,label_above_quantile = Inf, 
             subsample_below_quantile = 0.7, subsample_rate = 0.1)

# 37434 out of 101172 data points will be included in plot

Version	Author	Date
c7742f0	kevinlkx	2021-01-28

Motif enrichment analysis using HOMER

Details about HOMER motif analysis:
- http://homer.ucsd.edu/homer/motif/
- http://homer.ucsd.edu/homer/ngs/peakMotifs.html
Motif enrichment result using regions with z-score above 99% quantile.

Compile Homer results across topics

homer.dir <- paste0(out.dir, "/motifanalysis-Buenrostro2018-k=11-quantile/HOMER/")
cat(sprintf("Directory of motif analysis result: %s \n", homer.dir))
homer_res_topics <- readRDS(file.path(homer.dir, "/homer_knownResults.rds"))
selected_regions <- readRDS(file.path(homer.dir, "/selected_regions.rds"))

# Compile Homer results (pvalue and ranking) across topics
motif_res <- compile_homer_motif_res(homer_res_topics)
saveRDS(motif_res, paste0(homer.dir, "/homer_motif_enrichment_results.rds"))
cat("compiled homer motif results are saved in", paste0(homer.dir, "/homer_motif_enrichment_results.rds"))

motif_table <- data.frame(motif = gsub("/.*", "", rownames(motif_res$mlog10P)),
                          round(motif_res$mlog10P,2))
DT::datatable(motif_table, rownames = F, caption = "Motif enrichment (-log10P)")

# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized//motifanalysis-Buenrostro2018-k=11-quantile/HOMER/ 
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized//motifanalysis-Buenrostro2018-k=11-quantile/HOMER//homer_motif_enrichment_results.rds

Heatmap of motif enrichment across topics

Heatmap of motif enrichment -log10(p-value). Order motifs by hierarchical clustering.

create_motif_enrichment_heatmap(motif_res, enrichment = "-log10(p-value)", 
                                cluster_motifs = TRUE, cluster_topics = FALSE, motif_filter = 50, 
                                enrichment_range = c(0,100), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)

Version	Author	Date
c7742f0	kevinlkx	2021-01-28

# 72 out of 439 motifs included the heatmap

Heatmap of motif enrichment z-score. Order motifs by hierarchical clustering.

create_motif_enrichment_heatmap(motif_res, enrichment = "z-score",
                                cluster_motifs = TRUE, cluster_topics = FALSE, motif_filter = 10, 
                                enrichment_range = c(-20,20), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)

Version	Author	Date
c7742f0	kevinlkx	2021-01-28

# 163 out of 439 motifs included the heatmap

Scatterplots of motif enrichment

# Plot enrichment (-log10 p-value) and ranking of the motifs

plots <- vector("list", ncol(motif_res$mlog10P))
names(plots) <- colnames(motif_res$mlog10P)

for( i in 1:length(plots)){
  plots[[i]] <- create_motif_enrichment_ranking_plot(motif_res, k = i, 
                                                     max.overlaps = 20, subsample = FALSE)
}

# do.call(plot_grid,plots)

Plot motif enrichment (-log10 p-value) and the ranking

Topic 4 example

print(plots[[4]])

Version	Author	Date
c7742f0	kevinlkx	2021-01-28

# Plot motif enrichment (-log10 p-value) in each topic vs other topics

plots <- vector("list", ncol(motif_res$mlog10P))
names(plots) <- colnames(motif_res$mlog10P)

for( i in 1:length(homer_res_topics)){
  plots[[i]] <- create_motif_enrichment_plot(motif_res, k = i, 
                                             max.overlaps = 20, subsample = TRUE)
}

# do.call(plot_grid,plots)

Plot motif enrichment (-log10 p-value) in topic 4 vs other topics

print(plots[[4]])

Version	Author	Date
c7742f0	kevinlkx	2021-01-28

Motif enrichment vs gene score

Load pre-computed gene scores

gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-TSS-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res <- readRDS(file.path(gene.dir, "genescore_result_topics.rds"))

genes <- genescore_res$genes
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genes$SYMBOL
gene_logFC <- genescore_res$beta
rownames(gene_logFC) <- genes$SYMBOL

# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized//geneanalysis-Buenrostro2018-k=11-TSS-l2

Get TF genes

motif_names <- gsub("\\s*\\(.*", "", motif_res$motifs$motif)
gene_names <- genes$SYMBOL

TF_genes <- intersect(toupper(motif_names), toupper(gene_names))
cat(sprintf("%s TF genes mapped between motif names and gene symbol. \n", length(TF_genes)))

# 266 TF genes mapped between motif names and gene symbol.

Compute correlation between motif enrichment z-score and gene score: