Gene analysis using topic modeling and DA results (v2) for Buenrostro et al (2018) scATAC-seq result (data processed using Chen 2019 pipeline)

Last updated: 2022-02-22

Checks: 7 0

Knit directory: scATACseq-topics/

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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/gene_analysis_Buenrostro2018_Chen2019pipeline_v2.Rmd) and HTML (docs/gene_analysis_Buenrostro2018_Chen2019pipeline_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	d8d9892	kevinlkx	2022-02-22	added gene volcano plots
html	d3c0891	kevinlkx	2022-02-16	Build site.
Rmd	e297a8a	kevinlkx	2022-02-16	updated with v2 of DA analysis result
html	f524d5a	kevinlkx	2022-02-16	Build site.
Rmd	c6d4883	kevinlkx	2022-02-16	updated with v2 of DA analysis result

Here we perform TF motif and gene 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.

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(reshape)
library(pathways)

source("code/plots.R")

Load data and topic model results

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 <- poisson2multinom(fit)

Visualize by Structure plot grouped by cell labels.

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,
                     grouping = samples[, "label"],n = Inf,gap = 40,
                     perplexity = 50,colors = colors_topics,
                     num_threads = 6,verbose = FALSE)

print(p.structure)

Version	Author	Date
f524d5a	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/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2"
cat(sprintf("Load results from %s \n", out.dir))
DA_res <- readRDS(file.path(out.dir, paste0("DAanalysis-Buenrostro2018-k=11/DA_regions_topics_10000iters.rds")))

# Load results from /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2

Volcano plot of the regions

m <- ncol(DA_res$z)
plots <- vector("list",m)
names(plots) <- colnames(DA_res$z)
for (k in 1:m) {
  plots[[k]] <- volcano_plot(DA_res, k = k,labels = rep("",nrow(DA_res$z)))
}
do.call(plot_grid, plots)

Version	Author	Date
f524d5a	kevinlkx	2022-02-16

Gene score analysis

Set output directorry

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2"

fig.dir <- "output/plotly/Buenrostro_2018_Chen2019pipeline_v2/"
dir.create(fig.dir, showWarnings = F, recursive = T)

TSS model version 1

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
use abs(z) scores

gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-TSS-absZ-l2")

cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))

genes <- genescore_res$genes
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$logFC

topics <- colnames(gene_scores)
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- topics

for (k in topics){
  top_genes[,k] <- genes$SYMBOL[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}

DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F, caption = "Top 10 genes by abs(gene z-scores)")

# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2/geneanalysis-Buenrostro2018-k=11-TSS-absZ-l2

Volcano plots of gene scores

all topics

genescore_volcano_plot(genescore_res, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

topic 1 and topic 4 examples

p.volcano.1 <- genescore_volcano_plot(genescore_res, k = 1, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

p.volcano.4 <- genescore_volcano_plot(genescore_res, k=4, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

plot_grid(p.volcano.1, p.volcano.4)

TSS model version 2

TSS model, normalized by the sum of weights
use abs(z) scores

gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-TSS-absZ-sum")

cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))

genes <- genescore_res$genes
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$logFC

topics <- colnames(gene_scores)
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- topics

for (k in topics){
  top_genes[,k] <- genes$SYMBOL[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}

DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F, caption = "Top 10 genes by abs(gene z-scores)")

# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2/geneanalysis-Buenrostro2018-k=11-TSS-absZ-sum

Volcano plots of gene scores

all topics

genescore_volcano_plot(genescore_res, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

topic 1 and topic 4 examples

p.volcano.1 <- genescore_volcano_plot(genescore_res, k = 1, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

p.volcano.4 <- genescore_volcano_plot(genescore_res, k=4, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

plot_grid(p.volcano.1, p.volcano.4)

Gene body model version 1

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
use abs(z) scores

gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-genebody-Z-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))

genes <- genescore_res$genes
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$logFC

topics <- colnames(gene_scores)
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- topics

for (k in topics){
  top_genes[,k] <- genes$SYMBOL[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}

DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F, caption = "Top 10 genes by abs(gene z-scores)")

# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2/geneanalysis-Buenrostro2018-k=11-genebody-Z-l2

Volcano plots of gene scores

all topics

genescore_volcano_plot(genescore_res, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

topic 1 and topic 4 examples

p.volcano.1 <- genescore_volcano_plot(genescore_res, k = 1, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

p.volcano.4 <- genescore_volcano_plot(genescore_res, k=4, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

plot_grid(p.volcano.1, p.volcano.4)

Gene body model version 2

Gene body model, normalized by the sum of weights
use abs(z) scores

gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-genebody-Z-sum")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))

genes <- genescore_res$genes
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$logFC

topics <- colnames(gene_scores)
top_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- topics

for (k in topics){
  top_genes[,k] <- genes$SYMBOL[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}

DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F, caption = "Top 10 genes by abs(gene z-scores)")

# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2/geneanalysis-Buenrostro2018-k=11-genebody-Z-sum

Volcano plots of gene scores

all topics

genescore_volcano_plot(genescore_res, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

topic 1 and topic 4 examples

p.volcano.1 <- genescore_volcano_plot(genescore_res, k = 1, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

p.volcano.4 <- genescore_volcano_plot(genescore_res, k=4, label_above_quantile = 0.99,
                       labels = genescore_res$genes$SYMBOL, max.overlaps = 20,
                       subsample_below_quantile = 0.5, subsample_rate = 0.1)

plot_grid(p.volcano.1, p.volcano.4)

Gene-set enrichment analysis (GSEA)

Loading gene set data

cat("Loading human gene set data.\n")
data(gene_sets_human)
gene_sets <- gene_sets_human$gene_sets
gene_set_info <- gene_sets_human$gene_set_info

# Loading human gene set data.

TSS model, normalized by the l2 norm of weights, as in Stouffer's z-score method
use abs(z) scores

gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-TSS-absZ-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
gsea_res <- readRDS(file.path(gene.dir, "gsea_result.rds"))

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/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2/geneanalysis-Buenrostro2018-k=11-TSS-absZ-l2

Gene body model, normalized by the l2 norm of weights, as in Stouffer's z-score method.

gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-genebody-absZ-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
gsea_res <- readRDS(file.path(gene.dir, "gsea_result.rds"))

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/Buenrostro_2018_Chen2019pipeline/binarized/postfit_v2/geneanalysis-Buenrostro2018-k=11-genebody-absZ-l2

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] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
#  [1] pathways_0.1-20   reshape_0.8.8     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_1.0.7       fastTopics_0.6-97 Matrix_1.4-0     
# [13] workflowr_1.7.0  
# 
# loaded via a namespace (and not attached):
#   [1] fgsea_1.21.0         Rtsne_0.15           colorspace_2.0-2    
#   [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   prodlim_2019.11.13  
#  [13] fansi_1.0.2          lubridate_1.8.0      codetools_0.2-18    
#  [16] splines_4.0.4        knitr_1.37           jsonlite_1.7.3      
#  [19] pROC_1.18.0          mcmc_0.9-7           caret_6.0-90        
#  [22] ashr_2.2-47          uwot_0.1.11          compiler_4.0.4      
#  [25] httr_1.4.2           assertthat_0.2.1     fastmap_1.1.0       
#  [28] lazyeval_0.2.2       cli_3.1.1            later_1.3.0         
#  [31] prettyunits_1.1.1    htmltools_0.5.2      quantreg_5.86       
#  [34] tools_4.0.4          coda_0.19-4          gtable_0.3.0        
#  [37] glue_1.6.1           reshape2_1.4.4       fastmatch_1.1-3     
#  [40] Rcpp_1.0.8           jquerylib_0.1.4      vctrs_0.3.8         
#  [43] nlme_3.1-155         conquer_1.2.1        crosstalk_1.2.0     
#  [46] iterators_1.0.13     timeDate_3043.102    gower_0.2.2         
#  [49] xfun_0.29            stringr_1.4.0        globals_0.14.0      
#  [52] ps_1.6.0             lifecycle_1.0.1      irlba_2.3.5         
#  [55] future_1.23.0        getPass_0.2-2        MASS_7.3-55         
#  [58] scales_1.1.1         ipred_0.9-12         hms_1.1.1           
#  [61] promises_1.2.0.1     parallel_4.0.4       SparseM_1.81        
#  [64] yaml_2.2.2           gridExtra_2.3        pbapply_1.5-0       
#  [67] sass_0.4.0           rpart_4.1-15         stringi_1.7.6       
#  [70] SQUAREM_2021.1       highr_0.9            foreach_1.5.1       
#  [73] BiocParallel_1.24.1  lava_1.6.10          truncnorm_1.0-8     
#  [76] rlang_1.0.0          pkgconfig_2.0.3      matrixStats_0.61.0  
#  [79] evaluate_0.14        lattice_0.20-45      invgamma_1.1        
#  [82] purrr_0.3.4          labeling_0.4.2       recipes_0.1.17      
#  [85] processx_3.5.2       tidyselect_1.1.1     parallelly_1.30.0   
#  [88] plyr_1.8.6           magrittr_2.0.2       R6_2.5.1            
#  [91] generics_0.1.1       DBI_1.1.2            pillar_1.6.5        
#  [94] whisker_0.4          withr_2.4.3          survival_3.2-13     
#  [97] mixsqp_0.3-43        nnet_7.3-17          tibble_3.1.6        
# [100] future.apply_1.8.1   crayon_1.4.2         utf8_1.2.2          
# [103] rmarkdown_2.11       progress_1.2.2       grid_4.0.4          
# [106] data.table_1.14.2    callr_3.7.0          git2r_0.29.0        
# [109] ModelMetrics_1.2.2.2 digest_0.6.29        httpuv_1.6.5        
# [112] MCMCpack_1.6-0       RcppParallel_5.1.5   stats4_4.0.4        
# [115] munsell_0.5.0        viridisLite_0.4.0    bslib_0.3.1         
# [118] quadprog_1.5-8

Gene analysis using topic modeling and DA results (v2) for Buenrostro et al (2018) scATAC-seq result (data processed using Chen 2019 pipeline)

Kaixuan Luo

Load packages and some functions used in this analysis

Load data and topic model results

Visualize by Structure plot grouped by cell labels.

Differential accessbility analysis of the ATAC-seq regions for the topics

Gene score analysis

TSS model version 1

TSS model version 2

Gene body model version 1

Gene body model version 2

Gene-set enrichment analysis (GSEA)