Motif and gene analysis using topic modeling results for Cusanovich et al (2018) scATAC-seq data
Kaixuan Luo
Last updated: 2020-12-22
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Knit directory: scATACseq-topics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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)Load data and topic model results
Load the data. The counts are no longer needed at this stage of the analysis.
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Cusanovich_2018/processed_data/"
load(file.path(data.dir, "Cusanovich_2018.RData"))
rm(counts)Load the \(k = 13\) Poisson NMF fit results.
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.1 <- 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.1)
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
library(reshape)
#
# Attaching package: 'reshape'
# The following objects are masked from 'package:tidyr':
#
# expand, smiths
# The following object is masked from 'package:cowplot':
#
# stamp
# The following object is masked from 'package:dplyr':
#
# rename
# The following object is masked from 'package:Matrix':
#
# expand
zscore_topics <- melt(diff_count_topics$Z)
colnames(zscore_topics) <- c("region", "topic", "zscore")
levels(zscore_topics$topic) <- colnames(diff_count_topics$Z)
# quantile_topics <- data.frame(topic = colnames(diff_count_topics$Z),
# quantile = apply(abs(diff_count_topics$Z), 2, quantile, 0.99))
p.hist.zscores <- ggplot(zscore_topics, aes(x=zscore)) +
geom_histogram(binwidth=1, color="black", fill="white") +
# geom_vline(data = quantile_topics, aes(xintercept=quantile, color="red")) +
# scale_color_discrete(name = "", labels = c("99% quantile")) +
coord_cartesian(xlim = c(-10, 30)) + theme_cowplot(font_size = 10) +
facet_wrap(~ topic, ncol=4)
print(p.hist.zscores)
Motif enrichment analysis using HOMER
Details about HOMER motif analysis: http://homer.ucsd.edu/homer/motif/ http://homer.ucsd.edu/homer/ngs/peakMotifs.html
Motif enrichment result using regions with abs(z-score) above 99% quantile. RCC:
/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/motifanalysis-Cusanovich2018-k=13-quantile/
homer.dir <- paste0(out.dir, "/motifanalysis-Cusanovich2018-k=13-quantile/HOMER/quantile")
homer_res <- readRDS(file.path(homer.dir, "/homer_knownResults.rds"))
top_motifs <- data.frame(matrix(nrow=10, ncol = ncol(diff_count_topics$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.")- Motif enrichment result using regions with zscore > 20. RCC:
/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/motifanalysis-Cusanovich2018-k=13-zscore/
homer.dir <- paste0(out.dir, "/motifanalysis-Cusanovich2018-k=13-zscore/HOMER/zscore")
homer_res <- readRDS(file.path(homer.dir, "/homer_knownResults.rds"))
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$motif, 10)
}
DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F,
caption = "Top 10 motifs enriched in each topic.")Top genes
Gene body model
Gene scores were computed using the gene score model (model 42) in the archR paper with some modifications. This model uses bi-directional exponential decays from the gene TSS (extended upstream by 5 kb by default) and the gene transcription termination site (TTS). Note: the current version of the function does not account for neighboring gene boundaries.
- Gene body model, normalized by the l2 norm of weights, as in Stouffer's z-score method.
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-l2")
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- colnames(gene_scores)
rownames(gene_scores) <- genes[match(rownames(gene_scores), genes$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.")- Gene body model, normalized by the total weights (i.e. weighted averge).
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-sum")
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$ENSEMBL), "SYMBOL"]
for (k in colnames(top_genes)){
top_genes[,k] <- rownames(gene_scores)[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}
DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F,
caption = "Top 10 genes in each topic.")TSS model
Gene scores were computed using TSS based method as in Lareau et al. Nature Biotech, 2019 as well as the model 21 in archR paper. This model weights chromatin accessibility around gene promoters by using bi-directional exponential decays from the TSS.
- TSS model, normalized by the l2 norm of weights, as in Stouffer's z-score method.
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-l2")
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- colnames(gene_scores)
rownames(gene_scores) <- genes[match(rownames(gene_scores), genes$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.")- TSS model, normalized by the total weights (i.e. weighted averge).
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-sum")
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$ENSEMBL), "SYMBOL"]
for (k in colnames(top_genes)){
top_genes[,k] <- rownames(gene_scores)[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}
DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F,
caption = "Top 10 genes in each topic.")Gene-set enrichment analysis (GSEA)
- Gene body model, normalized by the l2 norm of weights, as in Stouffer's z-score method.
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-l2")
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_pathways_up <- top_pathways_down <- data.frame(matrix(nrow=10, ncol = ncol(gsea_res$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$id),c("name", "id")] %>%
unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
}
DT::datatable(data.frame(rank = 1:10, top_pathways_up), rownames = F,
caption = "Top 10 pathways enriched at the top of the gene rank list.")- Gene body model, normalized by the total weights (i.e. weighted averge).
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-sum")
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_pathways_up <- top_pathways_down <- data.frame(matrix(nrow=10, ncol = ncol(gsea_res$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$id),c("name", "id")] %>%
unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
}
DT::datatable(data.frame(rank = 1:10, top_pathways_up), rownames = F,
caption = "Top 10 pathways enriched at the top of the gene rank list.")- TSS model, normalized by the l2 norm of weights, as in Stouffer's z-score method.
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-l2")
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_pathways_up <- top_pathways_down <- data.frame(matrix(nrow=10, ncol = ncol(gsea_res$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$id, ")")
}
DT::datatable(data.frame(rank = 1:10, top_pathways_up), rownames = F,
caption = "Top 10 pathways enriched at the top of the gene rank list.")- TSS model, normalized by the total weights (i.e. weighted averge).
gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-sum")
load(file.path(gene.dir, "genescores_gsea.Rdata"))
top_pathways_up <- top_pathways_down <- data.frame(matrix(nrow=10, ncol = ncol(gsea_res$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.")
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