Motif and gene analysis using topic modeling results for Cusanovich et al (2018) scATAC-seq data
Kaixuan Luo
Last updated: 2020-12-23
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Knit directory: scATACseq-topics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 796a0a8 | kevinlkx | 2020-12-23 | wflow_publish("motif_gene_analysis_Cusanovich2018.Rmd") |
| html | b9aa0bf | kevinlkx | 2020-12-22 | Build site. |
| Rmd | 004a4b9 | kevinlkx | 2020-12-22 | first version of motif and gene analysis based on K = 13 topic model result |
Here we perform TF motif and gene 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(dplyr)
library(ggplot2)
library(cowplot)
library(fastTopics)
library(dplyr)
library(tidyr)
library(DT)
library(reshape)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)out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(out.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)
# Perplexity automatically changed to 42 because original setting of 50 was too large for the number of samples (132)
# Perplexity automatically changed to 40 because original setting of 50 was too large for the number of samples (124)
print(p.structure)
| Version | Author | Date |
|---|---|---|
| b9aa0bf | kevinlkx | 2020-12-22 |
Differential accessbility analysis of the ATAC-seq regions for the topics
Load results from differential accessbility analysis for the topics
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)
| Version | Author | Date |
|---|---|---|
| b9aa0bf | kevinlkx | 2020-12-22 |
# 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 20.71928Motif enrichment analysis using HOMER
- Details about HOMER motif analysis:
- Motif enrichment result using regions with z-score above 99% quantile.
homer.dir <- paste0(out.dir, "/motifanalysis-Cusanovich2018-k=13-quantile/HOMER/quantile")
cat(sprintf("Directory of motif analysis result: %s \n", homer.dir))
homer_res <- readRDS(file.path(homer.dir, "/homer_knownResults.rds"))
selected_regions <- readRDS(file.path(homer.dir, "/selected_regions.rds"))
cat("Number of regions selected for each topic: \n")
print(mapply(nrow, selected_regions[1:(length(selected_regions)-1)]))
top_motifs <- data.frame(matrix(nrow=10, ncol = ncol(diff_count_topics$Z)))
colnames(top_motifs) <- colnames(diff_count_topics$Z)
for (k in colnames(top_motifs)){
homer_motifs <- homer_res[[k]]
colnames(homer_motifs) <- c("Motif.name", "Consensus", "P-value", "Log.P-value", "q-value (Benjamini)",
"# of Target Sequences with Motif", "% of Target Sequences with Motif",
"# of Background Sequences with Motif", "% of Background Sequences with Motif")
homer_motifs <- homer_motifs %>% separate(Motif.name, c("motif", "experiment", "database"), "/")
top_motifs[,k] <- head(homer_motifs$motif, 10)
}
DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F,
caption = "Top 10 motifs enriched in each topic.")
# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/motifanalysis-Cusanovich2018-k=13-quantile/HOMER/quantile
# Number of regions selected for each topic:
# k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 k11 k12 k13
# 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363 4363- Motif enrichment result using regions with zscore > 20.
homer.dir <- paste0(out.dir, "/motifanalysis-Cusanovich2018-k=13-zscore/HOMER/zscore")
cat(sprintf("Directory of motif analysis result: %s \n", homer.dir))
homer_res <- readRDS(file.path(homer.dir, "/homer_knownResults.rds"))
selected_regions <- readRDS(file.path(homer.dir, "/selected_regions.rds"))
cat("Number of regions selected for each topic: \n")
print(mapply(nrow, selected_regions[1:(length(selected_regions)-1)]))
top_motifs <- data.frame(matrix(nrow=10, ncol = ncol(diff_count_topics$Z)))
colnames(top_motifs) <- colnames(diff_count_topics$Z)
for (k in colnames(top_motifs)){
homer_motifs <- homer_res[[k]]
colnames(homer_motifs) <- c("Motif.name", "Consensus", "P-value", "Log.P-value", "q-value (Benjamini)",
"# of Target Sequences with Motif", "% of Target Sequences with Motif",
"# of Background Sequences with Motif", "% of Background Sequences with Motif")
homer_motifs <- homer_motifs %>% separate(Motif.name, c("motif", "experiment", "database"), "/")
top_motifs[,k] <- head(homer_motifs$motif, 10)
}
DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F,
caption = "Top 10 motifs enriched in each topic.")
# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/motifanalysis-Cusanovich2018-k=13-zscore/HOMER/zscore
# Number of regions selected for each topic:
# k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 k11 k12 k13
# 5199 9695 8446 9752 17209 17758 12988 22940 8024 747 27044 20069 5467Top genes
Gene body model
Gene scores were computed using the gene score model (model 42) in the archR paper with some modifications. This model uses bi-directional exponential decays from the gene TSS (extended upstream by 5 kb by default) and the gene transcription termination site (TTS). Note: the current version of the function does not account for neighboring gene boundaries.
- Gene body model, normalized by the l2 norm of weights, as in Stouffer's z-score method.
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- colnames(gene_scores)
rownames(gene_scores) <- genes[match(rownames(gene_scores), genes$ENSEMBL), "SYMBOL"]
for (k in colnames(top_genes)){
top_genes[,k] <- rownames(gene_scores)[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}
DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F,
caption = "Top 10 genes in each topic.")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/geneanalysis-Cusanovich2018-k=13-genebody-l2- Gene body model, normalized by the total weights (i.e. weighted averge).
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- colnames(gene_scores)
rownames(gene_scores) <- genes[match(rownames(gene_scores), genes$ENSEMBL), "SYMBOL"]
for (k in colnames(top_genes)){
top_genes[,k] <- rownames(gene_scores)[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}
DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F,
caption = "Top 10 genes in each topic.")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/geneanalysis-Cusanovich2018-k=13-genebody-sumTSS model
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.
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- colnames(gene_scores)
rownames(gene_scores) <- genes[match(rownames(gene_scores), genes$ENSEMBL), "SYMBOL"]
for (k in colnames(top_genes)){
top_genes[,k] <- rownames(gene_scores)[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}
DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F,
caption = "Top 10 genes in each topic.")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/geneanalysis-Cusanovich2018-k=13-TSS-l2- TSS model, normalized by the total weights (i.e. weighted averge).
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- colnames(gene_scores)
rownames(gene_scores) <- genes[match(rownames(gene_scores), genes$ENSEMBL), "SYMBOL"]
for (k in colnames(top_genes)){
top_genes[,k] <- rownames(gene_scores)[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}
DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F,
caption = "Top 10 genes in each topic.")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/geneanalysis-Cusanovich2018-k=13-TSS-sumGene-set enrichment analysis (GSEA)
- Gene body model, normalized by the l2 norm of weights, as in Stouffer's z-score method.
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_pathways_up <- top_pathways_down <- data.frame(matrix(nrow=10, ncol = ncol(gsea_res$pval)))
colnames(top_pathways_up) <- colnames(top_pathways_down) <- colnames(gsea_res$pval)
for (k in 1:ncol(gsea_res$pval)){
gsea_topic <- data.frame(pathway = rownames(gsea_res$pval),
pval = gsea_res$pval[,k],
log2err = gsea_res$log2err[,k],
ES = gsea_res$ES[,k],
NES = gsea_res$NES[,k])
gsea_up <- gsea_topic[gsea_topic$ES > 0,]
top_IDs_up <- as.character(gsea_up[head(order(gsea_up$pval), 10), "pathway"])
top_pathways_up[,k] <- gene_set_info[match(top_IDs_up, gene_set_info$id),c("name", "id")] %>%
unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
gsea_down <- gsea_topic[gsea_topic$ES < 0,]
top_IDs_down <- as.character(gsea_down[head(order(gsea_down$pval), 10), "pathway"])
top_pathways_down[,k] <- gene_set_info[match(top_IDs_down, gene_set_info$id),c("name", "id")] %>%
unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
}
DT::datatable(data.frame(rank = 1:10, top_pathways_up), rownames = F,
caption = "Top 10 pathways enriched at the top of the gene rank list.")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/geneanalysis-Cusanovich2018-k=13-genebody-l2- Gene body model, normalized by the total weights (i.e. weighted averge).
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_pathways_up <- top_pathways_down <- data.frame(matrix(nrow=10, ncol = ncol(gsea_res$pval)))
colnames(top_pathways_up) <- colnames(top_pathways_down) <- colnames(gsea_res$pval)
for (k in 1:ncol(gsea_res$pval)){
gsea_topic <- data.frame(pathway = rownames(gsea_res$pval),
pval = gsea_res$pval[,k],
log2err = gsea_res$log2err[,k],
ES = gsea_res$ES[,k],
NES = gsea_res$NES[,k])
gsea_up <- gsea_topic[gsea_topic$ES > 0,]
top_IDs_up <- as.character(gsea_up[head(order(gsea_up$pval), 10), "pathway"])
top_pathways_up[,k] <- gene_set_info[match(top_IDs_up, gene_set_info$id),c("name", "id")] %>%
unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
gsea_down <- gsea_topic[gsea_topic$ES < 0,]
top_IDs_down <- as.character(gsea_down[head(order(gsea_down$pval), 10), "pathway"])
top_pathways_down[,k] <- gene_set_info[match(top_IDs_down, gene_set_info$id),c("name", "id")] %>%
unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
}
DT::datatable(data.frame(rank = 1:10, top_pathways_up), rownames = F,
caption = "Top 10 pathways enriched at the top of the gene rank list.")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/geneanalysis-Cusanovich2018-k=13-genebody-sum- TSS model, normalized by the l2 norm of weights, as in Stouffer's z-score method.
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_pathways_up <- top_pathways_down <- data.frame(matrix(nrow=10, ncol = ncol(gsea_res$pval)))
colnames(top_pathways_up) <- colnames(top_pathways_down) <- colnames(gsea_res$pval)
for (k in 1:ncol(gsea_res$pval)){
gsea_topic <- data.frame(pathway = rownames(gsea_res$pval),
pval = gsea_res$pval[,k],
log2err = gsea_res$log2err[,k],
ES = gsea_res$ES[,k],
NES = gsea_res$NES[,k])
gsea_up <- gsea_topic[gsea_topic$ES > 0,]
top_IDs_up <- as.character(gsea_up[head(order(gsea_up$pval), 10), "pathway"])
top_IDs_up <- gene_set_info[match(top_IDs_up, gene_set_info$id),c("name", "id")]
top_pathways_up[,k] <- paste0(top_IDs_up$name, "(", top_IDs_up$id, ")")
gsea_down <- gsea_topic[gsea_topic$ES < 0,]
top_IDs_down <- as.character(gsea_down[head(order(gsea_down$pval), 10), "pathway"])
top_IDs_down <- gene_set_info[match(top_IDs_down, gene_set_info$id),c("name", "id")]
top_pathways_down[,k] <- paste0(top_IDs_down$name, "(", top_IDs_down$id, ")")
}
DT::datatable(data.frame(rank = 1:10, top_pathways_up), rownames = F,
caption = "Top 10 pathways enriched at the top of the gene rank list.")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/geneanalysis-Cusanovich2018-k=13-TSS-l2- TSS model, normalized by the total weights (i.e. weighted averge).
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_pathways_up <- top_pathways_down <- data.frame(matrix(nrow=10, ncol = ncol(gsea_res$pval)))
colnames(top_pathways_up) <- colnames(top_pathways_down) <- colnames(gsea_res$pval)
for (k in 1:ncol(gsea_res$pval)){
gsea_topic <- data.frame(pathway = rownames(gsea_res$pval),
pval = gsea_res$pval[,k],
log2err = gsea_res$log2err[,k],
ES = gsea_res$ES[,k],
NES = gsea_res$NES[,k])
gsea_up <- gsea_topic[gsea_topic$ES > 0,]
top_IDs_up <- as.character(gsea_up[head(order(gsea_up$pval), 10), "pathway"])
top_IDs_up <- gene_set_info[match(top_IDs_up, gene_set_info$id),c("name", "id")]
top_pathways_up[,k] <- paste0(top_IDs_up$name, "(", top_IDs_up$id, ")")
gsea_down <- gsea_topic[gsea_topic$ES < 0,]
top_IDs_down <- as.character(gsea_down[head(order(gsea_down$pval), 10), "pathway"])
top_IDs_down <- gene_set_info[match(top_IDs_down, gene_set_info$id),c("name", "id")]
top_pathways_down[,k] <- paste0(top_IDs_down$name, "(", top_IDs_down$id, ")")
}
DT::datatable(data.frame(rank = 1:10, top_pathways_up), rownames = F,
caption = "Top 10 pathways enriched at the top of the gene rank list.")
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/geneanalysis-Cusanovich2018-k=13-TSS-sum
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] reshape_0.8.8 DT_0.16 tidyr_1.1.2 fastTopics_0.4-6
# [5] cowplot_1.1.0 ggplot2_3.3.2 dplyr_1.0.2 Matrix_1.2-18
#
# loaded via a namespace (and not attached):
# [1] ggrepel_0.9.0 Rcpp_1.0.5 lattice_0.20-41 prettyunits_1.1.1
# [5] rprojroot_2.0.2 digest_0.6.27 plyr_1.8.6 R6_2.5.0
# [9] MatrixModels_0.4-1 evaluate_0.14 coda_0.19-4 httr_1.4.2
# [13] pillar_1.4.7 rlang_0.4.9 progress_1.2.2 lazyeval_0.2.2
# [17] data.table_1.13.4 irlba_2.3.3 SparseM_1.78 whisker_0.4
# [21] rmarkdown_2.6 labeling_0.4.2 Rtsne_0.15 stringr_1.4.0
# [25] htmlwidgets_1.5.3 munsell_0.5.0 compiler_3.6.1 httpuv_1.5.4
# [29] xfun_0.19 pkgconfig_2.0.3 mcmc_0.9-7 htmltools_0.5.0
# [33] tidyselect_1.1.0 tibble_3.0.4 workflowr_1.6.2 quadprog_1.5-8
# [37] matrixStats_0.57.0 viridisLite_0.3.0 crayon_1.3.4 conquer_1.0.2
# [41] withr_2.3.0 later_1.1.0.1 MASS_7.3-53 grid_3.6.1
# [45] jsonlite_1.7.2 gtable_0.3.0 lifecycle_0.2.0 git2r_0.27.1
# [49] magrittr_2.0.1 scales_1.1.1 RcppParallel_5.0.2 stringi_1.5.3
# [53] farver_2.0.3 fs_1.3.1 promises_1.1.1 ellipsis_0.3.1
# [57] generics_0.1.0 vctrs_0.3.6 tools_3.6.1 glue_1.4.2
# [61] purrr_0.3.4 crosstalk_1.1.0.1 hms_0.5.3 yaml_2.2.1
# [65] colorspace_2.0-0 plotly_4.9.2.1 knitr_1.30 quantreg_5.75
# [69] MCMCpack_1.4-9