Last updated: 2022-02-16

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Knit directory: scATACseq-topics/

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Rmd c1e1d7e kevinlkx 2022-02-16 updated with new DA test result

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 <- 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$label <- factor(samples$label, levels = c("HSC", "MPP", "CMP", "GMP", "mono", "MEP", "LMPP", "CLP", "pDC", "UNK"))

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

print(p.structure)

Focus on myeloid differentiation (HSC, MPP, CMP, GMP, Momo)

set.seed(10)
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
                   "#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
                   "gray")
myeloid_samples <- factor(samples$label, levels = c("HSC", "MPP", "CMP", "GMP", "mono"))

p.structure.myeloid <- structure_plot(fit,
                     grouping = myeloid_samples, n = Inf,gap = 40,
                     perplexity = 50, colors = colors_topics,
                     num_threads = 4,verbose = FALSE)

print(p.structure.myeloid)

Focus on erythroid differentiation (HSC, MPP, CMP, MEP)

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

p.structure.erythroid <- structure_plot(fit,
                     grouping = myeloid_samples, n = Inf,gap = 40,
                     perplexity = 50,colors = colors_topics,
                     num_threads = 4,verbose = FALSE)

print(p.structure.erythroid)

Focus on lymphoid differentiation (HSC, MPP, LMPP, CLP)

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

p.structure.lymphoid <- structure_plot(fit,
                     grouping = myeloid_samples, n = Inf,gap = 40,
                     perplexity = 50,colors = colors_topics,
                     num_threads = 4,verbose = FALSE)

print(p.structure.lymphoid)

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/postfit_v2"
cat(sprintf("Load results from %s \n", out.dir))
DA_res <- readRDS(file.path(out.dir, paste0("DAanalysis-Buenrostro2018-k=11-quantile/DA_regions_topics_10000iters.rds")))
# Load results from /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2

Volcano plot of the regions

topic 1 and topic 4 examples

p.volcano.1 <- volcano_plot(DA_res,k = 1, labels = rep("",nrow(DA_res$z)))

p.volcano.4 <- volcano_plot(DA_res,k = 4, labels = rep("",nrow(DA_res$z)))

plot_grid(p.volcano.1, p.volcano.4)

Motif enrichment analysis using HOMER

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 \n"))

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/postfit_v2/motifanalysis-Buenrostro2018-k=11-quantile/HOMER/ 
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2/motifanalysis-Buenrostro2018-k=11-quantile/HOMER//homer_motif_enrichment_results.rds

Top 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 
# 1012 1012 1012 1012 1012 1012 1012 1012 1012 1012 1012

Heatmap 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)

# 137 out of 439 motifs included the heatmap
# Heatmap of motif enrichment z-score.
create_motif_enrichment_heatmap(motif_res, enrichment = "z-score",
                                cluster_motifs = TRUE, cluster_topics = FALSE, motif_filter = 10, horizontal = FALSE,
                                enrichment_range = c(-20,20), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)

Heatmap of motif enrichment for selected TF motifs

toMatch <- c("^GATA\\d*$", "^CEBP.?$", "^SPI.?$", "^IRF\\d*$", "^STAT\\d*$", "^TCF\\d*$", "^BCL\\d*$", "^CTCF$", "^ERG$")
selected_motifs <- grep(paste(toMatch,collapse="|"), motif_res$motifs$motif, ignore.case = T, value = T)
rows <- match(selected_motifs, motif_res$motifs$motif)
selected_motif_res <- lapply(motif_res, FUN = function(x) {x[rows, ]})

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

create_motif_enrichment_heatmap(selected_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 = 8, font.size.topics = 9)

# 17 out of 26 motifs included the heatmap

Scatterplots 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)

Examples Topic 1 is mainly shown in GMP. The enrichment of CEBP motif in GMP is also highlighted in Figure 2F of the Buenrostro et al paper.

Topic 4 is mainly shown in MEP especially and also CMP. The enrichment of GATA motif in MEP and CMP is also highlighted in Figure 2E of the Buenrostro et al paper.

do.call(plot_grid,plots[c(1,4)])

# 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)

Motif enrichment vs gene score

Load pre-computed gene scores

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, normalized by the l2 norm of weights, as in Stouffer's z-score method.
  • use abs(z) scores
gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-TSS-absZ-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2/geneanalysis-Buenrostro2018-k=11-TSS-absZ-l2

Get 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))])
# 263 TF genes mapped between motif names and gene symbol.

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

Topic 4 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     Gata1(Zf)    221.490186 0.093355787  0.8026633
# 2     Gata6(Zf)    272.042063 0.014304687  0.1222153
# 3       Sp2(Zf)      8.846579 0.009008670  0.8859293
# 4      Klf4(Zf)      7.808615 0.018467354  0.7009582
# 5      MYB(HTH)     12.451223 0.033500629  0.3270266
# 6      KLF6(Zf)     10.540327 0.007717778  0.3080052
# 7  Twist2(bHLH)      8.764063 0.002548089  0.3316900
# 8      KLF3(Zf)      4.798954 0.046606706  0.5499308
# 9      KLF1(Zf)      8.277653 0.292057556  0.2978747
# 10      Maz(Zf)      2.858961 0.003280518  0.7933922

GATA 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 GATA6
# Plot GATA motifs in topic 4
k = 4
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 = 4, 
                                       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 = 4, 
                                       y = "z-score", 
                                       colors = colors_topics,
                                       max.overlaps = 10)

do.call(plot_grid,plots)

CEBP family

motif_names <- motif_res$motifs$motif
gene_names <- genescore_res$genes$SYMBOL
TF_motifs <- sort(unique(grep("^CEBP.?$", motif_names, ignore.case=T, value=T)))
TF_genes <- sort(unique(grep("^CEBP.?$", gene_names, ignore.case=T, value=T)))
motif_gene_table <- unique(data.frame(motif = c("CEBP"), gene = TF_genes))
print(motif_gene_table)
#   motif  gene
# 1  CEBP CEBPA
# 2  CEBP CEBPB
# 3  CEBP CEBPD
# 4  CEBP CEBPE
# 5  CEBP CEBPG
# 6  CEBP CEBPZ
# Plot GATA motifs in topic 4
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 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] grid      stats     graphics  grDevices utils     datasets  methods  
# [8] base     
# 
# other attached packages:
#  [1] Logolas_1.3.1     reshape2_1.4.4    DT_0.20           htmlwidgets_1.5.4
#  [5] plotly_4.10.0     cowplot_1.1.1     ggrepel_0.9.1     ggplot2_3.3.5    
#  [9] tidyr_1.1.4       dplyr_1.0.7       fastTopics_0.6-97 Matrix_1.4-0     
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# 
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#  [13] prodlim_2019.11.13   fansi_1.0.2          lubridate_1.8.0     
#  [16] codetools_0.2-18     splines_4.0.4        knitr_1.37          
#  [19] ade4_1.7-18          jsonlite_1.7.3       pROC_1.18.0         
#  [22] mcmc_0.9-7           caret_6.0-90         gridBase_0.4-7      
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#  [28] compiler_4.0.4       httr_1.4.2           assertthat_0.2.1    
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