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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)
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)

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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_2018
diff_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)

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# 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 20.71928

Volcano plot of the regions

topic 1 examples

volcano_plot(diff_count_topics,k = 1,label_above_quantile = Inf, 
             subsample_below_quantile = 0.7, subsample_rate = 0.1)
# 161397 out of 436206 data points will be included in plot

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Motif enrichment analysis using HOMER

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$mlog10P)),
                          motif_res$mlog10P)
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.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  k12  k13 
# 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363

Heatmap of motif enrichment across topics

Clustering motifs by hierarchical clustering (motifs with similar enrichment across topics are plotted together)

create_motif_enrichment_heatmap(motif_res, cluster_motifs = TRUE, cluster_topics = FALSE, filter_motifs = TRUE, min_enrichment = 50, 
                                max_enrichment = 100, method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)

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# 114 out of 439 motifs included the heatmap

Cluster both motifs and topics by hierarchical clustering

create_motif_enrichment_heatmap(motif_res, cluster_motifs = TRUE, cluster_topics = TRUE, filter_motifs = TRUE, min_enrichment = 50, 
                                max_enrichment = 100, method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)

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# 114 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 = 15, subsample = FALSE)
}

# do.call(plot_grid,plots)
  • Plot motif enrichment (-log10 p-value) and the ranking

Topic 1 example

print(plots[[1]])

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# 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 = 15, subsample = TRUE)
}

# do.call(plot_grid,plots)
  • Plot motif enrichment (-log10 p-value) in topic 1 vs other topics
print(plots[[1]])

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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-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)))
# 250 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, gene_scores, motif_names, gene_names, TF_genes, 
                                                        k = 1, cor.motif = "z-score")

motif_gene_mapping <- motif_gene_mapping[with(motif_gene_mapping, order(motif_mlog10P*cor.zscore, decreasing = T)),]

cat("Top motifs by motif enrichment (-log10 p-value) and correlation to gene scores: \n")
rownames(motif_gene_mapping) <- 1:nrow(motif_gene_mapping)
print(head(motif_gene_mapping, 10))
# Top motifs by motif enrichment (-log10 p-value) and correlation to gene scores: 
#          motif  gene motif_mlog10P motif_zscore gene_score cor.zscore
# 1    Gata2(Zf) Gata2     460.35215     45.94025 27.8830209  0.5920248
# 2    Gata6(Zf) Gata6     465.56368     46.20060 -0.6075940  0.2660333
# 3    Gata4(Zf) Gata4     452.96914     45.56888 -0.9032233  0.2450204
# 4     KLF5(Zf)  Klf5      63.18985     16.83769 20.2932849  0.4208190
# 5     KLF1(Zf)  Klf1      66.14305     17.23547 51.7778237  0.3657063
# 6     KLF3(Zf)  Klf3      58.76004     16.22288 23.3799000  0.3629908
# 7     KLF6(Zf)  Klf6      56.02399     15.83131 14.6687082  0.3115715
# 8     Klf4(Zf)  Klf4      57.06629     15.98160 -3.7795042  0.2882401
# 9      Sp2(Zf)   Sp2      59.80235     16.36960 28.4662015  0.2650298
# 10 Bach1(bZIP) Bach1      23.76459     10.14637 26.3022816  0.3642947
#     cor.mlog10P
# 1   0.629963388
# 2   0.006659885
# 3   0.051105474
# 4   0.385521594
# 5   0.562941174
# 6   0.106165968
# 7   0.098692064
# 8  -0.055373875
# 9   0.398822257
# 10  0.373610276
  • Plot motif enrichment (-log10 p-value) and correlation between motif enrichment (-log10 p-value) and gene scores
  • Rank motifs by motif enrichment (-log10 p-value) and correlation between motif enrichment (-log10 p-value) and gene scores
motif_gene_mapping <- create_motif_gene_cor_scatterplot(motif_res, gene_scores, motif_names, gene_names, TF_genes, 
                                                        k = 1, cor.motif = "-log10(p-value)")

motif_gene_mapping <- motif_gene_mapping[with(motif_gene_mapping, order(motif_mlog10P*cor.mlog10P, decreasing = T)),]

cat("Top motifs by motif enrichment (-log10 p-value) and correlation to gene scores: \n")
rownames(motif_gene_mapping) <- 1:nrow(motif_gene_mapping)
print(head(motif_gene_mapping, 10))
# Top motifs by motif enrichment (-log10 p-value) and correlation to gene scores: 
#          motif  gene motif_mlog10P motif_zscore gene_score  cor.zscore
# 1    Gata2(Zf) Gata2    460.352151    45.940249 27.8830209  0.59202478
# 2    Gata1(Zf) Gata1    447.757611    45.304904 26.7126627 -0.06272824
# 3     KLF1(Zf)  Klf1     66.143050    17.235472 51.7778237  0.36570627
# 4     KLF5(Zf)  Klf5     63.189847    16.837693 20.2932849  0.42081896
# 5      Sp2(Zf)   Sp2     59.802350    16.369602 28.4662015  0.26502977
# 6    Gata4(Zf) Gata4    452.969145    45.568879 -0.9032233  0.24502036
# 7  Bach1(bZIP) Bach1     23.764594    10.146374 26.3022816  0.36429470
# 8    KLF14(Zf) Klf14     31.538465    11.766791  0.4537105  0.11910799
# 9    SpiB(ETS)  Spib      7.452493     5.389748 -3.0070640  0.95370507
# 10 Tcf21(bHLH) Tcf21      8.234223     5.704582 -0.5385452  0.73531571
#    cor.mlog10P
# 1   0.62996339
# 2   0.56449139
# 3   0.56294117
# 4   0.38552159
# 5   0.39882226
# 6   0.05110547
# 7   0.37361028
# 8   0.25921375
# 9   0.99145723
# 10  0.89029474

GATA genes

  • Plot motif enrichment (-log10 p-value) and gene scores
GATA_genes <- grep("^GATA\\d*$", TF_genes, ignore.case=T, value=T)

plots <- create_motif_gene_scatterplot(motif_res, gene_scores, 
                                       motif_names, gene_names, 
                                       selected_genes = GATA_genes,
                                       y = "-log10(p-value)", 
                                       colors = colors_topics,
                                       max.overlaps = 10)

do.call(plot_grid,plots)

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  • Plot motif enrichment (zscore) and gene scores
plots <- create_motif_gene_scatterplot(motif_res, gene_scores, 
                                       motif_names, gene_names, 
                                       selected_genes = GATA_genes,
                                       y = "z-score", 
                                       colors = colors_topics,
                                       max.overlaps = 10)

do.call(plot_grid,plots)

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KLF/SP genes

  • Plot motif enrichment (-log10 p-value) and gene scores
KLF_genes <- grep("^KLF\\d*$", TF_genes, ignore.case=T, value=T)
SP_genes <- grep("^SP\\d*$", TF_genes, ignore.case=T, value=T)

plots <- create_motif_gene_scatterplot(motif_res, gene_scores, 
                                       motif_names, gene_names, 
                                       selected_genes = c(KLF_genes, SP_genes),
                                       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, gene_scores, 
                                       motif_names, gene_names, 
                                       selected_genes = c(KLF_genes, SP_genes),
                                       y = "z-score", 
                                       colors = colors_topics,
                                       max.overlaps = 10)

do.call(plot_grid,plots)


sessionInfo()
# R version 3.6.1 (2019-07-05)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Scientific Linux 7.4 (Nitrogen)
# 
# Matrix products: default
# BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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_1.4.3    DT_0.16           htmlwidgets_1.5.3 plotly_4.9.2.1   
#  [5] cowplot_1.1.0     ggrepel_0.9.0     ggplot2_3.3.2     tidyr_1.1.2      
#  [9] dplyr_1.0.2       fastTopics_0.4-6  Matrix_1.2-18     workflowr_1.6.2  
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.5         lattice_0.20-41    prettyunits_1.1.1  rprojroot_2.0.2   
#  [5] digest_0.6.27      plyr_1.8.6         R6_2.5.0           MatrixModels_0.4-1
#  [9] evaluate_0.14      coda_0.19-4        httr_1.4.2         pillar_1.4.7      
# [13] rlang_0.4.9        progress_1.2.2     lazyeval_0.2.2     data.table_1.13.4 
# [17] irlba_2.3.3        SparseM_1.78       whisker_0.4        rmarkdown_2.6     
# [21] labeling_0.4.2     Rtsne_0.15         stringr_1.4.0      munsell_0.5.0     
# [25] compiler_3.6.1     httpuv_1.5.4       xfun_0.19          pkgconfig_2.0.3   
# [29] mcmc_0.9-7         htmltools_0.5.0    tidyselect_1.1.0   tibble_3.0.4      
# [33] quadprog_1.5-8     matrixStats_0.57.0 viridisLite_0.3.0  withr_2.3.0       
# [37] crayon_1.3.4       conquer_1.0.2      later_1.1.0.1      MASS_7.3-53       
# [41] grid_3.6.1         jsonlite_1.7.2     gtable_0.3.0       lifecycle_0.2.0   
# [45] git2r_0.27.1       magrittr_2.0.1     scales_1.1.1       RcppParallel_5.0.2
# [49] stringi_1.5.3      farver_2.0.3       fs_1.3.1           promises_1.1.1    
# [53] ellipsis_0.3.1     generics_0.1.0     vctrs_0.3.6        tools_3.6.1       
# [57] glue_1.4.2         purrr_0.3.4        crosstalk_1.1.0.1  hms_0.5.3         
# [61] yaml_2.2.1         colorspace_2.0-0   knitr_1.30         quantreg_5.75     
# [65] MCMCpack_1.4-9