Motif analysis using topic modeling results for Cusanovich et al (2018) scATAC-seq data
Kaixuan Luo
Last updated: 2021-02-05
Checks: 7 0
Knit directory: scATACseq-topics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8e3f641 | kevinlkx | 2021-02-05 | cleaned results |
| html | 40ea1f1 | kevinlkx | 2021-01-21 | Build site. |
| Rmd | 780af9e | kevinlkx | 2021-01-21 | added motif logo plots |
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| Rmd | c7428cd | kevinlkx | 2021-01-20 | minor adjustment on the figures |
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| Rmd | 4458ed5 | kevinlkx | 2021-01-20 | added KLF/SP genes and added an option to compute correlation using -log10P |
| html | 1fb78db | kevinlkx | 2021-01-20 | Build site. |
| Rmd | c9d6742 | kevinlkx | 2021-01-20 | updated the correlations between motif enrichment (using z-score) and gene z-score |
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| Rmd | 0134bdf | kevinlkx | 2021-01-19 | fix colors in scatter plots of create_motif_gene_scatterplot |
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| Rmd | f51b56e | kevinlkx | 2021-01-19 | added colors to scatterplots for motif enrichment and gene scores |
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| Rmd | 5f18411 | kevinlkx | 2021-01-19 | Plot motif enrichment and correlate with gene scores |
Here we perform TF motif 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(reshape2)
library(Logolas)
library(grid)
source("code/motif_analysis.R")
source("code/plots.R")Load data and topic model results
Load the data and the \(k = 13\) Poisson NMF fit results.
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Cusanovich_2018/processed_data/"
load(file.path(data.dir, "Cusanovich_2018.RData"))
rm(counts)fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(fit.dir, "/fit-Cusanovich2018-scd-ex-k=13.rds"))$fit
fit_multinom <- poisson2multinom(fit)Visualize by Structure plot grouped by tissues
set.seed(10)
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
rows <- sample(nrow(fit$L),4000)
samples$tissue <- as.factor(samples$tissue)
p.structure <- structure_plot(select(fit_multinom,loadings = rows),
grouping = samples[rows, "tissue"],n = Inf,gap = 40,
perplexity = 50,topics = 1:13,colors = colors_topics,
num_threads = 4,verbose = FALSE)
print(p.structure)
| Version | Author | Date |
|---|---|---|
| 14eac34 | kevinlkx | 2021-01-19 |
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"
cat(sprintf("Load results from %s \n", out.dir))# Load results from /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018diff_count_topics <- readRDS(file.path(out.dir, "/diffcount-Cusanovich2018-13topics.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 |
|---|---|---|
| 14eac34 | kevinlkx | 2021-01-19 |
# z-score 99% quantile:
# k1 k2 k3 k4 k5 k6 k7 k8
# 21.42992 31.48751 25.46082 25.97670 34.64418 37.09098 32.07250 39.65746
# k9 k10 k11 k12 k13
# 25.93102 15.88394 34.29782 39.80147 20.71928Motif enrichment analysis using HOMER
- Details about HOMER motif analysis:
- http://homer.ucsd.edu/homer/motif/
Motif enrichment result using regions with z-score above 99% quantile.
Compile Homer results across topics
homer.dir <- paste0(out.dir, "/motifanalysis-Cusanovich2018-k=13-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/Cusanovich_2018/motifanalysis-Cusanovich2018-k=13-quantile/HOMER
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/motifanalysis-Cusanovich2018-k=13-quantile/HOMER/homer_motif_enrichment_results.rdsTop 10 motifs in each topic
cat("Number of regions selected for each topic: \n")
print(mapply(nrow, selected_regions[1:(length(selected_regions)-1)]))
colnames_homer <- c("motif_name", "consensus", "P", "log10P", "Padj", "num_target", "percent_target", "num_bg", "percent_bg")
top_motifs <- data.frame(matrix(nrow=10, ncol = length(homer_res_topics)))
colnames(top_motifs) <- names(homer_res_topics)
for (k in 1:length(homer_res_topics)){
homer_res <- homer_res_topics[[k]]
colnames(homer_res) <- colnames_homer
homer_res <- homer_res %>% separate(motif_name, c("motif", "origin", "database"), "/")
top_motifs[,k] <- head(homer_res$motif, 10)
}
DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F, caption = "Top 10 motifs enriched in each topic.")# Number of regions selected for each topic:
# k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 k11 k12 k13
# 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363Heatmap of motif enrichment across topics
Heatmap of motif enrichment -log10(p-value).
create_motif_enrichment_heatmap(motif_res, enrichment = "-log10(p-value)",
cluster_motifs = TRUE, cluster_topics = TRUE, motif_filter = 10, horizontal = FALSE,
enrichment_range = c(0,100), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)
# 243 out of 439 motifs included the heatmapScatterplots of motif enrichment
Plot motif enrichment (-log10 p-value) and the ranking
# Plot enrichment (-log10 p-value) and ranking of the motifs
plots <- vector("list", ncol(motif_res$mlog10P))
names(plots) <- colnames(motif_res$mlog10P)
for( i in 1:length(plots)){
plots[[i]] <- create_motif_enrichment_ranking_plot(motif_res, k = i,
max.overlaps = 20, subsample = FALSE)
}
do.call(plot_grid,plots)
Topic 1 example
print(plots[[1]])
Motif enrichment vs gene score
Load pre-computed gene scores
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res <- readRDS(file.path(gene.dir, "genescore_result_topics.rds"))
genes <- genescore_res$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/Cusanovich_2018/geneanalysis-Cusanovich2018-k=13-TSS-l2Get TF genes
motif_names <- motif_res$motifs$motif
gene_names <- genescore_res$genes$SYMBOL
common_genes <- intersect(toupper(motif_names), toupper(gene_names))
cat(sprintf("%s TF genes mapped between motif names and gene symbol. \n", length(common_genes)))
motif_gene_table <- data.frame(motif = motif_names[match(common_genes, toupper(motif_names))],
gene = gene_names[match(common_genes, toupper(gene_names))])# 247 TF genes mapped between motif names and gene symbol.Compute correlation between motif enrichment z-score and gene score:
Topic 1 example
- Compute motif enrichment z-scores from the motif enrichment p-values
- Plot motif enrichment (-log10 p-value) and correlation between motif enrichment z-scores and gene scores
- Rank motifs by motif enrichment (-log10 p-value) and correlation between motif enrichment z-score and gene scores
motif_gene_mapping <- create_motif_gene_cor_scatterplot(motif_res, genescore_res, motif_gene_table,
k = 4, cor.motif = "z-score")
motif_gene_mapping <- motif_gene_mapping[with(motif_gene_mapping, order(motif_mlog10P*cor_zscore, decreasing = T)),]
rownames(motif_gene_mapping) <- 1:nrow(motif_gene_mapping)
cat("Top 10 motifs by motif enrichment (-log10 p-value) and correlation to gene scores: \n")
print(head(motif_gene_mapping[,c("motif","motif_mlog10P", "gene_score", "cor_zscore")], 10))# Top 10 motifs by motif enrichment (-log10 p-value) and correlation to gene scores:
# motif motif_mlog10P gene_score cor_zscore
# 1 TEAD3(TEA) 89.89896 33.96213 0.8305369
# 2 TEAD1(TEAD) 97.19511 37.87814 0.6162972
# 3 TEAD4(TEA) 93.63389 43.41048 0.6033918
# 4 HNF1b(Homeobox) 53.67880 55.95734 0.9691212
# 5 Foxa2(Forkhead) 29.36699 46.10279 0.8435864
# 6 GRHL2(CP2) 26.10110 88.26210 0.9316662
# 7 FOXA1(Forkhead) 25.61469 67.55695 0.8544556
# 8 HOXB13(Homeobox) 24.12940 35.16238 0.8467272
# 9 Hoxd13(Homeobox) 28.61132 11.97401 0.6732085
# 10 Cdx2(Homeobox) 20.24247 22.57429 0.9163834GATA family
motif_names <- motif_res$motifs$motif
gene_names <- genescore_res$genes$SYMBOL
TF_motifs <- sort(unique(grep("^GATA\\d*$", motif_names, ignore.case=T, value=T)))
TF_genes <- sort(unique(grep("^GATA\\d*$", gene_names, ignore.case=T, value=T)))
common_genes <- intersect(toupper(TF_motifs), toupper(TF_genes))
motif_gene_table <- data.frame(motif = TF_motifs[match(common_genes, toupper(TF_motifs))],
gene = TF_genes[match(common_genes, toupper(TF_genes))])
print(motif_gene_table)# motif gene
# 1 Gata1 Gata1
# 2 Gata2 Gata2
# 3 GATA3 Gata3
# 4 Gata4 Gata4
# 5 Gata6 Gata6Plot GATA motifs in topic 1
# Plot GATA motifs in topic 1
k = 1
motif_order <- order(motif_res$mlog10P[,k], decreasing = T)
motifs <- rownames(motif_res$motifs[motif_order,])
motif_names <- motif_res$motifs[motif_order, "motif"]
selected_motifs <- unique(motifs[match(toupper(motif_gene_table$motif), toupper(motif_names))])
motif.dir <- paste0(homer.dir, "/homer_result_topic_", k, "/knownResults/")
for (i in 1:length(selected_motifs)){
plot_motif_logo(homer_res_topics, selected_motifs[i], k, motif.dir, type = "both")
}
- Plot motif enrichment (-log10 p-value) and gene scores
plots <- create_motif_gene_scatterplot(motif_res, genescore_res,
motif_gene_table,
k = 1,
y = "-log10(p-value)",
colors = colors_topics,
max.overlaps = 10)
do.call(plot_grid,plots)
- Plot motif enrichment (zscore) and gene scores
plots <- create_motif_gene_scatterplot(motif_res, genescore_res,
motif_gene_table,
k = 1,
y = "z-score",
colors = colors_topics,
max.overlaps = 10)
do.call(plot_grid,plots)
sessionInfo()# R version 3.6.1 (2019-07-05)
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