Motif analysis using topic modeling and DA results (v2) for Buenrostro et al (2018) scATAC-seq result
Kaixuan Luo
Last updated: 2022-03-09
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Knit directory: scATACseq-topics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 9089601 | kevinlkx | 2022-03-09 | update link to the HOMER motif enrichment script |
| html | ec9c5dd | kevinlkx | 2022-03-09 | Build site. |
| Rmd | 6baae37 | kevinlkx | 2022-03-09 | corrected a typo of k |
| html | bdd0a99 | kevinlkx | 2022-03-09 | Build site. |
| Rmd | 1b7f106 | kevinlkx | 2022-03-09 | do not show all motif enrichment results |
| html | 38627c5 | kevinlkx | 2022-03-09 | Build site. |
| Rmd | e17fb16 | kevinlkx | 2022-03-09 | switched the order of showing motif enrichment results |
| html | 3551232 | kevinlkx | 2022-03-09 | Build site. |
| Rmd | 6954b87 | kevinlkx | 2022-03-09 | updated motif enrichment results with four different methods |
Here we perform TF motif analysis for the Buenrostro et al (2018) scATAC-seq result inferred from the multinomial topic model with \(k = 10\).
We use binarized data downloaded from original paper.
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(DT)
library(reshape2)
source("code/motif_analysis.R")
source("code/plots.R")Load data and topic model results
Data downloaded from original paper. Load the binarized data and the \(k = 10\) Poisson NMF fit results
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data/"
load(file.path(data.dir, "Buenrostro_2018_binarized_counts.RData"))
cat(sprintf("%d x %d counts matrix.\n",nrow(counts),ncol(counts)))# 2953 x 491437 counts matrix.fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/"
fit <- readRDS(file.path(fit.dir, "/fit-Buenrostro2018-binarized-scd-ex-k=10.rds"))$fit
fit <- poisson2multinom(fit)Structure plot
topic_colors <- c("darkorange","limegreen","magenta","gold","skyblue",
"darkblue","dodgerblue","darkmagenta","red","olivedrab")
set.seed(1)
# labels <- factor(samples$label, levels = c("HSC", "MPP", "CMP", "GMP", "mono", "MEP", "LMPP", "CLP", "pDC", "UNK"))
labels <- factor(samples$label, c("mono","pDC","MEP","HSC","MPP","CLP",
"LMPP","CMP","GMP","UNK"))
structure_plot(fit,grouping = labels,colors = topic_colors,
# topics = 1:10,
gap = 20,perplexity = 50,verbose = FALSE)
| Version | Author | Date |
|---|---|---|
| 3551232 | kevinlkx | 2022-03-09 |
Motif enrichment analysis using HOMER
- About HOMER motif analysis
- Script for motif enrichment analysis using HOMER
Motif enrichment result using regions with DA p-value < 0.05
Load and compile HOMER results across topics
postfit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2"
homer.dir <- paste0(postfit.dir, "/motifanalysis-Buenrostro2018-k=10/HOMER/DA_pval_0.05_regions")
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 \n"))# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2/motifanalysis-Buenrostro2018-k=10/HOMER/DA_pval_0.05_regions
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2/motifanalysis-Buenrostro2018-k=10/HOMER/DA_pval_0.05_regions/homer_motif_enrichment_results.rdsTop 10 motifs for 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
# 40 19 1322 3223 245 73 1385 1882 252 790Heatmap 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)
| Version | Author | Date |
|---|---|---|
| 3551232 | kevinlkx | 2022-03-09 |
# 133 out of 439 motifs included the heatmapTop enriched 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)
Motif enrichment result using regions with DA p-value < 0.1
Load and compile HOMER results across topics
postfit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2"
homer.dir <- paste0(postfit.dir, "/motifanalysis-Buenrostro2018-k=10/HOMER/DA_pval_0.1_regions")
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 \n"))# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2/motifanalysis-Buenrostro2018-k=10/HOMER/DA_pval_0.1_regions
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2/motifanalysis-Buenrostro2018-k=10/HOMER/DA_pval_0.1_regions/homer_motif_enrichment_results.rdsTop 10 motifs for 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$motif, 10)
}
DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F, caption = "Top 10 motifs enriched in each topic.")# Number of regions selected for each topic:
# k1 k2 k3 k4 k5 k6 k7 k8 k9 k10
# 163 45 1793 4250 416 125 2091 2772 418 1191Heatmap of motif enrichment -log10(p-value).
create_motif_enrichment_heatmap(motif_res, enrichment = "-log10(p-value)",
cluster_motifs = TRUE, cluster_topics = TRUE, motif_filter = 10, horizontal = FALSE,
enrichment_range = c(0,100), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)
| Version | Author | Date |
|---|---|---|
| 3551232 | kevinlkx | 2022-03-09 |
# 140 out of 439 motifs included the heatmapTop enriched motifs
plots <- vector("list", ncol(motif_res$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)
Motif enrichment result using top 1% regions with largest logFC
Load and compile HOMER results across topics
postfit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2"
homer.dir <- paste0(postfit.dir, "/motifanalysis-Buenrostro2018-k=10/HOMER/DA_top1percent_regions")
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 \n"))# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2/motifanalysis-Buenrostro2018-k=10/HOMER/DA_top1percent_regions
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2/motifanalysis-Buenrostro2018-k=10/HOMER/DA_top1percent_regions/homer_motif_enrichment_results.rdsTop 10 motifs for 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
# 4656 4656 4656 4656 4656 4656 4656 4656 4656 4656Heatmap 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)
| Version | Author | Date |
|---|---|---|
| 3551232 | kevinlkx | 2022-03-09 |
# 181 out of 439 motifs included the heatmapTop enriched 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)
Motif enrichment result using top 2000 regions with largest logFC
Load and compile HOMER results across topics
postfit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2"
homer.dir <- paste0(postfit.dir, "/motifanalysis-Buenrostro2018-k=10/HOMER/DA_top2000_regions")
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 \n"))# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2/motifanalysis-Buenrostro2018-k=10/HOMER/DA_top2000_regions
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/binarized/postfit_v2/motifanalysis-Buenrostro2018-k=10/HOMER/DA_top2000_regions/homer_motif_enrichment_results.rdsTop 10 motifs for 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$motif, 10)
}
DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F, caption = "Top 10 motifs enriched in each topic.")# Number of regions selected for each topic:
# k1 k2 k3 k4 k5 k6 k7 k8 k9 k10
# 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000Heatmap of motif enrichment -log10(p-value).
create_motif_enrichment_heatmap(motif_res, enrichment = "-log10(p-value)",
cluster_motifs = TRUE, cluster_topics = TRUE, motif_filter = 10, horizontal = FALSE,
enrichment_range = c(0,100), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)
| Version | Author | Date |
|---|---|---|
| 3551232 | kevinlkx | 2022-03-09 |
# 142 out of 439 motifs included the heatmapTop enriched motifs
plots <- vector("list", ncol(motif_res$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)
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] reshape2_1.4.4 DT_0.20 plotly_4.10.0 cowplot_1.1.1
# [5] ggrepel_0.9.1 ggplot2_3.3.5 tidyr_1.1.4 dplyr_1.0.8
# [9] fastTopics_0.6-97 Matrix_1.4-0 workflowr_1.7.0
#
# loaded via a namespace (and not attached):
# [1] Rtsne_0.15 colorspace_2.0-3 ellipsis_0.3.2
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