Motif analysis using topic modeling and DA results (v2) for Cusanovich et al (2018) scATAC-seq data
Kaixuan Luo
Last updated: 2022-02-24
Checks: 7 0
Knit directory: scATACseq-topics/
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Modified: scripts/fit_all_models_Cusanovich2018.sh
Modified: scripts/postfit_Buenrostro2018_Chen2019pipeline.sh
Modified: scripts/postfit_Cusanovich2018.sh
Modified: scripts/postfit_gene_analysis.sbatch
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/motif_analysis_Cusanovich2018_v2.Rmd) and HTML (docs/motif_analysis_Cusanovich2018_v2.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2810d52 | kevinlkx | 2022-02-24 | wflow_publish("analysis/motif_analysis_Cusanovich2018_v2.Rmd") |
| html | 0083668 | kevinlkx | 2022-02-16 | Build site. |
| Rmd | 33840df | kevinlkx | 2022-02-16 | updated with v2 of the DA analysis |
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)
library(Logolas)
library(grid)
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"))
cat(sprintf("%d x %d counts matrix.\n",nrow(counts),ncol(counts)))
rm(counts)# 81173 x 436206 counts matrix.fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(fit.dir, "/fit-Cusanovich2018-scd-ex-k=13.rds"))$fit
fit <- poisson2multinom(fit)Visualize by Structure plot grouped by tissues
set.seed(10)
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
rows <- sample(nrow(fit$L),4000)
samples$tissue <- as.factor(samples$tissue)
p.structure <- structure_plot(select(fit,loadings = rows),
grouping = samples[rows, "tissue"], n = Inf,gap = 40,
perplexity = 50,colors = colors_topics,
num_threads = 4,verbose = FALSE)
print(p.structure)
| Version | Author | Date |
|---|---|---|
| 0083668 | kevinlkx | 2022-02-16 |
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/postfit_v2"
cat(sprintf("Load results from %s \n", out.dir))
DA_res <- readRDS(file.path(out.dir, paste0("DAanalysis-Cusanovich2018-k=13/DA_regions_topics_10000iters.rds")))# Load results from /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2Volcano 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)
| Version | Author | Date |
|---|---|---|
| 0083668 | kevinlkx | 2022-02-16 |
Motif enrichment analysis using HOMER
- Details about HOMER motif analysis:
- http://homer.ucsd.edu/homer/motif/
Motif enrichment result using regions with z-score above 99% quantile.
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$mlog10P)),
round(motif_res$mlog10P,2))
DT::datatable(motif_table, rownames = F, caption = "Motif enrichment (-log10P)")# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2/motifanalysis-Cusanovich2018-k=13-quantile/HOMER
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2/motifanalysis-Cusanovich2018-k=13-quantile/HOMER/homer_motif_enrichment_results.rdsTop 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$motif, 10)
}
DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F, caption = "Top 10 motifs enriched in each topic.")# Number of regions selected for each topic:
# k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 k11 k12 k13
# 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137Heatmap of motif enrichment across topics
Heatmap of motif enrichment -log10(p-value).
create_motif_enrichment_heatmap(motif_res, enrichment = "-log10(p-value)",
cluster_motifs = TRUE, cluster_topics = TRUE, motif_filter = 10, horizontal = FALSE,
enrichment_range = c(0,100), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)
| Version | Author | Date |
|---|---|---|
| 0083668 | kevinlkx | 2022-02-16 |
# 250 out of 439 motifs included the heatmapScatterplots of motif enrichment
Plot motif enrichment (-log10 p-value) and the ranking
# Plot enrichment (-log10 p-value) and ranking of the motifs
plots <- vector("list", ncol(motif_res$mlog10P))
names(plots) <- colnames(motif_res$mlog10P)
for( i in 1:length(plots)){
plots[[i]] <- create_motif_enrichment_ranking_plot(motif_res, k = i,
max.overlaps = 20, subsample = FALSE)
}
do.call(plot_grid,plots)
| Version | Author | Date |
|---|---|---|
| 0083668 | kevinlkx | 2022-02-16 |
Topic 1 example
print(plots[[1]])
| Version | Author | Date |
|---|---|---|
| 0083668 | kevinlkx | 2022-02-16 |
Motif enrichment vs gene score
Load pre-computed gene scores
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-absZ-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2/geneanalysis-Cusanovich2018-k=13-TSS-absZ-l2Get TF genes
motif_names <- motif_res$motifs$motif
gene_names <- genescore_res$genes$SYMBOL
common_genes <- intersect(toupper(motif_names), toupper(gene_names))
cat(sprintf("%s TF genes mapped between motif names and gene symbol. \n", length(common_genes)))
motif_gene_table <- data.frame(motif = motif_names[match(common_genes, toupper(motif_names))],
gene = gene_names[match(common_genes, toupper(gene_names))])# 247 TF genes mapped between motif names and gene symbol.Compute correlation between motif enrichment z-score and gene score:
Topic 1 example
- Compute motif enrichment z-scores from the motif enrichment p-values
- Plot motif enrichment (-log10 p-value) and correlation between motif enrichment z-scores and gene scores
- Rank motifs by motif enrichment (-log10 p-value) and correlation between motif enrichment z-score and gene scores
motif_gene_mapping <- create_motif_gene_cor_scatterplot(motif_res, genescore_res, motif_gene_table,
k = 1, cor.motif = "z-score")
| Version | Author | Date |
|---|---|---|
| 0083668 | kevinlkx | 2022-02-16 |
motif_gene_mapping <- motif_gene_mapping[with(motif_gene_mapping, order(motif_mlog10P*cor_zscore, decreasing = T)),]
rownames(motif_gene_mapping) <- 1:nrow(motif_gene_mapping)
cat("Top 10 motifs by motif enrichment (-log10 p-value) and correlation to gene scores: \n")
print(head(motif_gene_mapping[,c("motif","motif_mlog10P", "gene_score", "cor_zscore")], 10))# Top 10 motifs by motif enrichment (-log10 p-value) and correlation to gene scores:
# motif motif_mlog10P gene_score cor_zscore
# 1 Gata2(Zf) 405.97848 2.6620405 0.6191297
# 2 Gata4(Zf) 402.37384 0.1782368 0.1705583
# 3 Gata1(Zf) 405.76133 13.0433214 0.1630853
# 4 Gata6(Zf) 415.88040 0.3492756 0.1087562
# 5 Sp2(Zf) 45.34034 6.2994971 0.4073351
# 6 KLF3(Zf) 43.77688 6.4292462 0.4064971
# 7 KLF1(Zf) 53.93937 20.9038040 0.3298559
# 8 KLF5(Zf) 51.33361 0.1760143 0.3047540
# 9 KLF6(Zf) 43.41642 0.2608836 0.3209959
# 10 Bach1(bZIP) 24.38998 9.9906945 0.4985966GATA family
motif_names <- motif_res$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*$", gene_names, ignore.case=T, value=T)))
common_genes <- intersect(toupper(TF_motifs), toupper(TF_genes))
motif_gene_table <- data.frame(motif = TF_motifs[match(common_genes, toupper(TF_motifs))],
gene = TF_genes[match(common_genes, toupper(TF_genes))])
print(motif_gene_table)# motif gene
# 1 Gata1 Gata1
# 2 Gata2 Gata2
# 3 GATA3 Gata3
# 4 Gata4 Gata4
# 5 Gata6 Gata6Plot GATA motifs in topic 1
# Plot GATA motifs in topic 1
k = 1
motif_order <- order(motif_res$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)
| Version | Author | Date |
|---|---|---|
| 0083668 | kevinlkx | 2022-02-16 |
- 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)
| Version | Author | Date |
|---|---|---|
| 0083668 | kevinlkx | 2022-02-16 |
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_1.0.8 fastTopics_0.6-97 Matrix_1.4-0
# [13] workflowr_1.7.0
#
# loaded via a namespace (and not attached):
# [1] Rtsne_0.15 colorspace_2.0-3 seqinr_4.2-8
# [4] ellipsis_0.3.2 class_7.3-20 rprojroot_2.0.2
# [7] fs_1.5.2 rstudioapi_0.13 farver_2.1.0
# [10] listenv_0.8.0 MatrixModels_0.5-0 bit64_4.0.5
# [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
# [25] Rmpfr_0.8-7 ashr_2.2-47 uwot_0.1.11
# [28] compiler_4.0.4 httr_1.4.2 assertthat_0.2.1
# [31] fastmap_1.1.0 lazyeval_0.2.2 cli_3.2.0
# [34] later_1.3.0 prettyunits_1.1.1 htmltools_0.5.2
# [37] quantreg_5.86 tools_4.0.4 gmp_0.6-2.1
# [40] coda_0.19-4 gtable_0.3.0 glue_1.6.2
# [43] Rcpp_1.0.8 jquerylib_0.1.4 vctrs_0.3.8
# [46] ape_5.6-1 nlme_3.1-155 conquer_1.2.1
# [49] crosstalk_1.2.0 iterators_1.0.13 timeDate_3043.102
# [52] CVXR_1.0-10 gower_0.2.2 xfun_0.29
# [55] stringr_1.4.0 globals_0.14.0 ps_1.6.0
# [58] lifecycle_1.0.1 irlba_2.3.5 future_1.23.0
# [61] getPass_0.2-2 MASS_7.3-55 scales_1.1.1
# [64] ipred_0.9-12 hms_1.1.1 promises_1.2.0.1
# [67] parallel_4.0.4 SparseM_1.81 yaml_2.2.2
# [70] pbapply_1.5-0 sass_0.4.0 rpart_4.1-15
# [73] stringi_1.7.6 SQUAREM_2021.1 highr_0.9
# [76] foreach_1.5.1 lava_1.6.10 truncnorm_1.0-8
# [79] rlang_1.0.1 pkgconfig_2.0.3 matrixStats_0.61.0
# [82] evaluate_0.14 lattice_0.20-45 invgamma_1.1
# [85] purrr_0.3.4 labeling_0.4.2 recipes_0.1.17
# [88] bit_4.0.4 processx_3.5.2 tidyselect_1.1.2
# [91] parallelly_1.30.0 plyr_1.8.6 magrittr_2.0.2
# [94] R6_2.5.1 generics_0.1.2 DBI_1.1.2
# [97] pillar_1.7.0 whisker_0.4 withr_2.4.3
# [100] survival_3.2-13 mixsqp_0.3-43 nnet_7.3-17
# [103] tibble_3.1.6 future.apply_1.8.1 crayon_1.5.0
# [106] utf8_1.2.2 rmarkdown_2.11 progress_1.2.2
# [109] data.table_1.14.2 callr_3.7.0 git2r_0.29.0
# [112] ModelMetrics_1.2.2.2 digest_0.6.29 httpuv_1.6.5
# [115] MCMCpack_1.6-0 RcppParallel_5.1.5 stats4_4.0.4
# [118] munsell_0.5.0 viridisLite_0.4.0 bslib_0.3.1
# [121] quadprog_1.5-8