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: 2022-02-16

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Knit directory: scATACseq-topics/

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File	Version	Author	Date	Message
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)
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)

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-quantile/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

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)

all topics

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)

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

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

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
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

Gene-set enrichment analysis (GSEA)

Loading gene set data

library(pathways)
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      
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#  [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      
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#  [88] plyr_1.8.6           magrittr_2.0.2       R6_2.5.1            
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# [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