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Rmd 9089601 kevinlkx 2022-03-09 update link to the HOMER motif enrichment script
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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)

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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.rds

Top 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  790

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)

Version Author Date
3551232 kevinlkx 2022-03-09
# 133 out of 439 motifs included the heatmap

Top 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)

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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.rds

Top 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 1191

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)

Version Author Date
3551232 kevinlkx 2022-03-09
# 140 out of 439 motifs included the heatmap

Top 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)

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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.rds

Top 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 4656

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)

Version Author Date
3551232 kevinlkx 2022-03-09
# 181 out of 439 motifs included the heatmap

Top 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)

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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.rds

Top 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 2000

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)

Version Author Date
3551232 kevinlkx 2022-03-09
# 142 out of 439 motifs included the heatmap

Top 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)

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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      
#   [4] class_7.3-20         rprojroot_2.0.2      fs_1.5.2            
#   [7] rstudioapi_0.13      farver_2.1.0         listenv_0.8.0       
#  [10] MatrixModels_0.5-0   prodlim_2019.11.13   fansi_1.0.2         
#  [13] lubridate_1.8.0      codetools_0.2-18     splines_4.0.4       
#  [16] knitr_1.37           jsonlite_1.7.3       pROC_1.18.0         
#  [19] mcmc_0.9-7           caret_6.0-90         ashr_2.2-47         
#  [22] uwot_0.1.11          compiler_4.0.4       httr_1.4.2          
#  [25] assertthat_0.2.1     fastmap_1.1.0        lazyeval_0.2.2      
#  [28] cli_3.2.0            later_1.3.0          prettyunits_1.1.1   
#  [31] htmltools_0.5.2      quantreg_5.86        tools_4.0.4         
#  [34] coda_0.19-4          gtable_0.3.0         glue_1.6.2          
#  [37] Rcpp_1.0.8           jquerylib_0.1.4      vctrs_0.3.8         
#  [40] nlme_3.1-155         conquer_1.2.1        crosstalk_1.2.0     
#  [43] iterators_1.0.13     timeDate_3043.102    gower_0.2.2         
#  [46] xfun_0.29            stringr_1.4.0        globals_0.14.0      
#  [49] ps_1.6.0             lifecycle_1.0.1      irlba_2.3.5         
#  [52] future_1.23.0        getPass_0.2-2        MASS_7.3-55         
#  [55] scales_1.1.1         ipred_0.9-12         hms_1.1.1           
#  [58] promises_1.2.0.1     parallel_4.0.4       SparseM_1.81        
#  [61] yaml_2.2.2           pbapply_1.5-0        sass_0.4.0          
#  [64] rpart_4.1-15         stringi_1.7.6        SQUAREM_2021.1      
#  [67] highr_0.9            foreach_1.5.1        lava_1.6.10         
#  [70] truncnorm_1.0-8      rlang_1.0.1          pkgconfig_2.0.3     
#  [73] matrixStats_0.61.0   evaluate_0.14        lattice_0.20-45     
#  [76] invgamma_1.1         purrr_0.3.4          labeling_0.4.2      
#  [79] recipes_0.1.17       htmlwidgets_1.5.4    processx_3.5.2      
#  [82] tidyselect_1.1.2     parallelly_1.30.0    plyr_1.8.6          
#  [85] magrittr_2.0.2       R6_2.5.1             generics_0.1.2      
#  [88] DBI_1.1.2            pillar_1.7.0         whisker_0.4         
#  [91] withr_2.4.3          survival_3.2-13      mixsqp_0.3-43       
#  [94] nnet_7.3-17          tibble_3.1.6         future.apply_1.8.1  
#  [97] crayon_1.5.0         utf8_1.2.2           rmarkdown_2.11      
# [100] progress_1.2.2       grid_4.0.4           data.table_1.14.2   
# [103] callr_3.7.0          git2r_0.29.0         ModelMetrics_1.2.2.2
# [106] digest_0.6.29        httpuv_1.6.5         MCMCpack_1.6-0      
# [109] RcppParallel_5.1.5   stats4_4.0.4         munsell_0.5.0       
# [112] viridisLite_0.4.0    bslib_0.3.1          quadprog_1.5-8