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Rmd | c1f6331 | kevinlkx | 2021-01-28 | change the example volcano plot to topic 4 |
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Rmd | 3608c72 | kevinlkx | 2021-01-28 | motif analysis of Buenrostro2018 data with topic 4 examples |
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.
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 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"))$fit
fit_multinom <- poisson2multinom(fit)
set.seed(10)
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
samples$label <- as.factor(samples$label)
p.structure <- structure_plot(fit_multinom,
grouping = samples[, "label"],n = Inf,gap = 40,
perplexity = 50,topics = 1:11,colors = colors_topics,
num_threads = 4,verbose = FALSE)
print(p.structure)
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
Load results from differential accessbility analysis for the topics
out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/"
cat(sprintf("Load results from %s \n", out.dir))
# Load results from /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/
diff_count_topics <- readRDS(file.path(out.dir, "diffcount-Buenrostro2018-11topics.rds"))
Distribution of z-scores
zscore_topics <- melt(diff_count_topics$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$Z), 2, quantile, 0.99)
cat("z-score 99% quantile: \n")
print(z.quantile.99)
p.hist.zscores <- ggplot(zscore_topics, aes(x=zscore)) +
geom_histogram(binwidth=1, color="black", fill="white") +
coord_cartesian(xlim = c(-10, 30)) + theme_cowplot(font_size = 10) +
facet_wrap(~ topic, ncol=4)
print(p.hist.zscores)
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
# z-score 99% quantile:
# k1 k2 k3 k4 k5 k6 k7 k8
# 7.680613 7.226323 5.954049 10.059326 9.203917 5.821598 7.215842 8.156498
# k9 k10 k11
# 5.978363 6.684635 8.191629
Volcano plot of the regions
topic 4 example
volcano_plot(diff_count_topics,k = 4,label_above_quantile = Inf,
subsample_below_quantile = 0.7, subsample_rate = 0.1)
# 37434 out of 101172 data points will be included in plot
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
Compile Homer results across topics
homer.dir <- paste0(out.dir, "/motifanalysis-Buenrostro2018-k=11-quantile/HOMER/")
cat(sprintf("Directory of motif analysis result: %s \n", homer.dir))
homer_res_topics <- readRDS(file.path(homer.dir, "/homer_knownResults.rds"))
selected_regions <- readRDS(file.path(homer.dir, "/selected_regions.rds"))
# Compile Homer results (pvalue and ranking) across topics
motif_res <- compile_homer_motif_res(homer_res_topics)
saveRDS(motif_res, paste0(homer.dir, "/homer_motif_enrichment_results.rds"))
cat("compiled homer motif results are saved in", paste0(homer.dir, "/homer_motif_enrichment_results.rds"))
motif_table <- data.frame(motif = gsub("/.*", "", rownames(motif_res$mlog10P)),
round(motif_res$mlog10P,2))
DT::datatable(motif_table, rownames = F, caption = "Motif enrichment (-log10P)")
# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized//motifanalysis-Buenrostro2018-k=11-quantile/HOMER/
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized//motifanalysis-Buenrostro2018-k=11-quantile/HOMER//homer_motif_enrichment_results.rds
cat("Number of regions selected for each topic: \n")
print(mapply(nrow, selected_regions[1:(length(selected_regions)-1)]))
colnames_homer <- c("motif_name", "consensus", "P", "log10P", "Padj", "num_target", "percent_target", "num_bg", "percent_bg")
top_motifs <- data.frame(matrix(nrow=10, ncol = length(homer_res_topics)))
colnames(top_motifs) <- names(homer_res_topics)
for (k in 1:length(homer_res_topics)){
homer_res <- homer_res_topics[[k]]
colnames(homer_res) <- colnames_homer
homer_res <- homer_res %>% separate(motif_name, c("motif", "origin", "database"), "/")
top_motifs[,k] <- head(homer_res$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 -log10(p-value). Order motifs by hierarchical clustering.
create_motif_enrichment_heatmap(motif_res, enrichment = "-log10(p-value)",
cluster_motifs = TRUE, cluster_topics = FALSE, motif_filter = 50,
enrichment_range = c(0,100), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
# 72 out of 439 motifs included the heatmap
Heatmap of motif enrichment z-score. Order motifs by hierarchical clustering.
create_motif_enrichment_heatmap(motif_res, enrichment = "z-score",
cluster_motifs = TRUE, cluster_topics = FALSE, motif_filter = 10,
enrichment_range = c(-20,20), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
# 163 out of 439 motifs included the heatmap
# 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 4 example
print(plots[[4]])
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
# 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)
print(plots[[4]])
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
Load pre-computed gene scores
gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-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$SYMBOL
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized//geneanalysis-Buenrostro2018-k=11-TSS-l2
Get TF genes
motif_names <- gsub("\\s*\\(.*", "", motif_res$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)))
# 266 TF genes mapped between motif names and gene symbol.
Compute correlation between motif enrichment z-score and gene score:
Topic 4 example
motif_gene_mapping <- create_motif_gene_cor_scatterplot(motif_res, gene_scores, motif_names, gene_names, TF_genes,
k = 4, cor.motif = "z-score")
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
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) 268.611137 10.58850478 0.82417385
# 2 Gata2(Zf) 283.246861 3.50532676 0.71486568
# 3 Gata6(Zf) 308.045076 0.28569100 0.39652411
# 4 TRPS1(Zf) 262.313867 0.18883601 0.08901704
# 5 Fli1(ETS) 13.293754 -0.02447725 0.71178359
# 6 ETV1(ETS) 14.944073 -0.85762399 0.58726309
# 7 RUNX2(Runt) 24.572382 -1.08635191 0.33730209
# 8 Foxo1(Forkhead) 5.706629 3.14525125 0.81941673
# 9 RUNX1(Runt) 27.712331 5.37659767 0.15396168
# 10 NFE2L2(bZIP) 6.349385 0.62828389 0.63787097
motif_gene_mapping <- create_motif_gene_cor_scatterplot(motif_res, gene_scores, motif_names, gene_names, TF_genes,
k = 4, cor.motif = "-log10(p-value)")
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
motif_gene_mapping <- motif_gene_mapping[with(motif_gene_mapping, order(motif_mlog10P*cor_mlog10P, 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_mlog10P")], 10))
# Top 10 motifs by motif enrichment (-log10 p-value) and correlation to gene scores:
# motif motif_mlog10P gene_score cor_mlog10P
# 1 Gata1(Zf) 268.611137 10.58850478 0.8966339
# 2 Gata2(Zf) 283.246861 3.50532676 0.5747579
# 3 Gata6(Zf) 308.045076 0.28569100 0.1974081
# 4 ETV1(ETS) 14.944073 -0.85762399 0.6996988
# 5 Fli1(ETS) 13.293754 -0.02447725 0.7454046
# 6 RUNX2(Runt) 24.572382 -1.08635191 0.3249036
# 7 Foxo1(Forkhead) 5.706629 3.14525125 0.8169112
# 8 NFE2L2(bZIP) 6.349385 0.62828389 0.6854138
# 9 Sp2(Zf) 4.351631 0.40903017 0.9621119
# 10 Fosl2(bZIP) 7.539352 -1.18725365 0.5367656
GATA family
GATA_genes <- grep("^GATA\\d*$", TF_genes, ignore.case=T, value=T)
Plot GATA motifs in topic 4
k = 4
selected_motifs <- rownames(motif_res$motifs)[match(toupper(GATA_genes), 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")
}
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c7742f0 | kevinlkx | 2021-01-28 |
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c7742f0 | kevinlkx | 2021-01-28 |
# The PWM of the motif (GATA3(Zf),DR4/iTreg-Gata3-ChIP-Seq(GSE20898)/Homer) was not in HOMER output of enriched motifs.
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)
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
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)
Version | Author | Date |
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c7742f0 | kevinlkx | 2021-01-28 |
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] grid stats graphics grDevices utils datasets methods
# [8] base
#
# other attached packages:
# [1] Logolas_1.3.1 reshape2_1.4.3 DT_0.16 htmlwidgets_1.5.3
# [5] plotly_4.9.2.1 cowplot_1.1.0 ggrepel_0.9.0 ggplot2_3.3.3
# [9] tidyr_1.1.2 dplyr_1.0.2 fastTopics_0.4-6 Matrix_1.2-18
# [13] workflowr_1.6.2
#
# loaded via a namespace (and not attached):
# [1] nlme_3.1-140 mcmc_0.9-7 matrixStats_0.57.0 fs_1.3.1
# [5] bit64_4.0.5 progress_1.2.2 httr_1.4.2 rprojroot_2.0.2
# [9] tools_3.6.1 R6_2.5.0 irlba_2.3.3 lazyeval_0.2.2
# [13] colorspace_2.0-0 ade4_1.7-16 withr_2.4.0 tidyselect_1.1.0
# [17] prettyunits_1.1.1 bit_4.0.4 compiler_3.6.1 git2r_0.27.1
# [21] quantreg_5.75 SparseM_1.78 labeling_0.4.2 scales_1.1.1
# [25] SQUAREM_2021.1 quadprog_1.5-8 mixsqp_0.3-43 stringr_1.4.0
# [29] digest_0.6.27 rmarkdown_2.6 MCMCpack_1.4-9 pkgconfig_2.0.3
# [33] htmltools_0.5.0 invgamma_1.1 rlang_0.4.10 generics_0.1.0
# [37] farver_2.0.3 jsonlite_1.7.2 crosstalk_1.1.0.1 magrittr_2.0.1
# [41] Rcpp_1.0.6 munsell_0.5.0 ape_5.4-1 lifecycle_0.2.0
# [45] CVXR_1.0-9 stringi_1.5.3 whisker_0.4 yaml_2.2.1
# [49] MASS_7.3-53 Rtsne_0.15 plyr_1.8.6 parallel_3.6.1
# [53] promises_1.1.1 crayon_1.3.4 lattice_0.20-41 hms_1.0.0
# [57] knitr_1.30 pillar_1.4.7 seqinr_4.2-5 glue_1.4.2
# [61] evaluate_0.14 data.table_1.13.6 RcppParallel_5.0.2 vctrs_0.3.6
# [65] httpuv_1.5.4 MatrixModels_0.4-1 gtable_0.3.0 purrr_0.3.4
# [69] ashr_2.2-47 xfun_0.19 gridBase_0.4-7 Rmpfr_0.8-2
# [73] coda_0.19-4 later_1.1.0.1 viridisLite_0.3.0 truncnorm_1.0-8
# [77] tibble_3.0.5 conquer_1.0.2 gmp_0.6-2 ellipsis_0.3.1