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

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Last updated: 2021-02-05

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Knit directory: scATACseq-topics/

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---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/gene_analysis_Buenrostro2018_Chen2019pipeline.Rmd) and HTML (docs/gene_analysis_Buenrostro2018_Chen2019pipeline.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	be3c653	kevinlkx	2021-02-05	added volcano plots for topic 1 and 4 examples

---

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_multinom <- 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_multinom,
                     grouping = samples[, "label"],n = Inf,gap = 40,
                     perplexity = 50,topics = 1:11,colors = colors_topics,
                     num_threads = 6,verbose = FALSE)

print(p.structure)

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

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_Chen2019pipeline/binarized/"
diffcount_file <- file.path(out.dir, "diffcount-Buenrostro2018-11topics.rds")
if(file.exists(diffcount_file)){
  cat("Load precomputed differential accessbility statistics.\n")
  diff_count_topics <- readRDS(diffcount_file)
}else{
  cat("Computing differential accessbility statistics from topic model.\n")
  timing <- system.time(diff_count_topics <- diff_count_analysis(fit,counts))
  cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
  cat("Saving results.\n")
  saveRDS(diff_count_topics, diffcount_file)
}

# Load precomputed differential accessbility statistics.

Gene score analysis

Set output directorry

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

fig.dir <- "output/plotly/Buenrostro_2018_Chen2019pipeline/"
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.

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

genes <- genescore_res$genes
gene_mean_acc <- genescore_res$colmeans
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$beta

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//geneanalysis-Buenrostro2018-k=11-TSS-l2

• TSS model, normalized by the total weights (i.e. weighted averge).

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

genes <- genescore_res$genes
gene_mean_acc <- genescore_res$colmeans
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$beta

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//geneanalysis-Buenrostro2018-k=11-TSS-sum

• Volcano plots

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)

# 11796 out of 21446 data points will be included in plot

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)

# 11796 out of 21446 data points will be included in plot

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

# Warning: ggrepel: 211 unlabeled data points (too many overlaps). Consider
# increasing max.overlaps

# Warning: ggrepel: 195 unlabeled data points (too many overlaps). Consider
# increasing max.overlaps

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-Buenrostro2018-k=11-genebody-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res_tss <- readRDS(file.path(gene.dir, "genescore_result_topics.rds"))
genescore_res <- genescore_res_tss

genes <- genescore_res$genes
gene_mean_acc <- genescore_res$colmeans
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$beta

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//geneanalysis-Buenrostro2018-k=11-genebody-l2

• Gene body model, normalized by the total weights (i.e. weighted averge).

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

genes <- genescore_res$genes
gene_mean_acc <- genescore_res$colmeans
gene_scores <- genescore_res$Z
gene_logFC <- genescore_res$beta

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//geneanalysis-Buenrostro2018-k=11-genebody-sum

• Volcano plots

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)

# 11796 out of 21446 data points will be included in plot

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)

# 11796 out of 21446 data points will be included in plot

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

# Warning: ggrepel: 204 unlabeled data points (too many overlaps). Consider
# increasing max.overlaps

# Warning: ggrepel: 191 unlabeled data points (too many overlaps). Consider
# increasing max.overlaps

Gene-set enrichment analysis (GSEA)

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

gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-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/Buenrostro_2018_Chen2019pipeline/binarized//geneanalysis-Buenrostro2018-k=11-TSS-l2

• TSS model, normalized by the total weights (i.e. weighted averge).

gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-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/Buenrostro_2018_Chen2019pipeline/binarized//geneanalysis-Buenrostro2018-k=11-TSS-sum

• 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-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/Buenrostro_2018_Chen2019pipeline/binarized//geneanalysis-Buenrostro2018-k=11-genebody-l2

• Gene body model, normalized by the total weights (i.e. weighted averge).

gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-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_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//geneanalysis-Buenrostro2018-k=11-genebody-sum

Session information

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           htmlwidgets_1.5.3 plotly_4.9.3     
#  [5] cowplot_1.1.1     ggrepel_0.9.1     ggplot2_3.3.3     tidyr_1.1.2      
#  [9] dplyr_1.0.3       fastTopics_0.4-29 Matrix_1.2-18     workflowr_1.6.2  
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.6         lattice_0.20-41    prettyunits_1.1.1  rprojroot_2.0.2   
#  [5] digest_0.6.27      plyr_1.8.6         R6_2.5.0           MatrixModels_0.4-1
#  [9] evaluate_0.14      coda_0.19-4        httr_1.4.2         pillar_1.4.7      
# [13] rlang_0.4.10       progress_1.2.2     lazyeval_0.2.2     data.table_1.13.6 
# [17] irlba_2.3.3        SparseM_1.78       whisker_0.4        rmarkdown_2.6     
# [21] labeling_0.4.2     Rtsne_0.15         stringr_1.4.0      munsell_0.5.0     
# [25] compiler_3.6.1     httpuv_1.5.4       xfun_0.19          pkgconfig_2.0.3   
# [29] mcmc_0.9-7         htmltools_0.5.1.1  tidyselect_1.1.0   tibble_3.0.6      
# [33] quadprog_1.5-8     matrixStats_0.58.0 viridisLite_0.3.0  withr_2.4.1       
# [37] crayon_1.4.0       conquer_1.0.2      later_1.1.0.1      MASS_7.3-53       
# [41] grid_3.6.1         jsonlite_1.7.2     gtable_0.3.0       lifecycle_0.2.0   
# [45] DBI_1.1.0          git2r_0.27.1       magrittr_2.0.1     scales_1.1.1      
# [49] RcppParallel_5.0.2 stringi_1.5.3      farver_2.0.3       fs_1.3.1          
# [53] promises_1.1.1     ellipsis_0.3.1     generics_0.1.0     vctrs_0.3.6       
# [57] tools_3.6.1        glue_1.4.2         purrr_0.3.4        crosstalk_1.1.1   
# [61] hms_1.0.0          yaml_2.2.1         colorspace_2.0-0   knitr_1.30        
# [65] quantreg_5.83      MCMCpack_1.5-0