Gene analysis using topic modeling and DA results (v2) for Cusanovich et al (2018) scATAC-seq data

Last updated: 2022-02-17

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Knit directory: scATACseq-topics/

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File	Version	Author	Date	Message
Rmd	57ff8a0	kevinlkx	2022-02-17	updated with v2 of DA analysis

Here we perform 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(fastTopics)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
library(cowplot)
library(plotly)
library(htmlwidgets)
library(DT)
library(reshape)
source("code/plots.R")

Load samples and topic model (\(k = 13\)) results.

data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
samples <- readRDS(paste0(data.dir, "/samples-clustering-Cusanovich2018.rds"))

fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(fit.dir, "/fit-Cusanovich2018-scd-ex-k=13.rds"))$fit
fit <- 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,loadings = rows),
                              grouping = samples[rows, "tissue"],n = Inf,gap = 40,
                              perplexity = 50,colors = colors_topics,
                              num_threads = 4,verbose = FALSE)

print(p.structure)

Load the clustering results

set.seed(10)
rows <- sample(nrow(fit$L),4000)

p.structure.kmeans <- structure_plot(select(fit,loadings = rows),
                                     grouping = samples$cluster_kmeans[rows],n = Inf,gap = 40,
                                     perplexity = 50,colors = colors_topics,
                                     num_threads = 4,verbose = FALSE)
print(p.structure.kmeans)

Distribution of tissue labels by cluster.

freq_table_cluster_tissue <- with(samples,table(tissue,cluster_kmeans))

freq_table_cluster_tissue <- as.data.frame.matrix(freq_table_cluster_tissue)
# DT::datatable(freq_table_cluster_tissue, 
#               options = list(pageLength = nrow(freq_table_cluster_tissue)), 
#               rownames = T, caption = "Number of cells")

create_celllabel_cluster_heatmap(samples$tissue, samples$cluster_kmeans, normalize_by = "column")

Distribution of cell labels by cluster.

freq_table_cluster_celllabel <- with(samples,table(cell_label,cluster_kmeans))

freq_table_cluster_celllabel <- as.data.frame.matrix(freq_table_cluster_celllabel)
# DT::datatable(freq_table_cluster_celllabel, 
#               options = list(pageLength = nrow(freq_table_cluster_celllabel)), 
#               rownames = T, caption = "Number of cells")

create_celllabel_cluster_heatmap(samples$cell_label, samples$cluster_kmeans, normalize_by = "column")

Top 5 cell types

top_celltypes_table <- data.frame(matrix(nrow=5, ncol = ncol(freq_table_cluster_celllabel)))
colnames(top_celltypes_table) <- colnames(freq_table_cluster_celllabel)
for (k in 1:ncol(freq_table_cluster_celllabel)){
  top_celltypes <- rownames(freq_table_cluster_celllabel)[head(order(freq_table_cluster_celllabel[,k], decreasing=TRUE), 5)]
  freq_top_celltypes <- freq_table_cluster_celllabel[top_celltypes, k]
  percent_top_celltypes <- freq_table_cluster_celllabel[top_celltypes, k]/sum(freq_table_cluster_celllabel[,k])
  top_celltypes_table[,k] <- sprintf("%s (%.1f%%)", top_celltypes, percent_top_celltypes*100)
}

DT::datatable(top_celltypes_table, rownames = T, caption = "Top 5 cell types in each cluster")

We can see the major cell types in the clusters (topics):

cluster 1 (topic 1): Erythroblasts
cluster 2 (topic 3): Endothelial cells
cluster 3 (topic 7): Cardiomyocytes cells
cluster 4 (topic 6): B cells, Monocytes, Dendritic cells
cluster 5 (topic 9): Astrocytes, Oligodendrocytes
cluster 8 (topic 5): Proximal tubule
cluster 10 (topic 4): a mixture of pneumocytes, Loop of henle, Enterocytes, DCT/CD
cluster 11 (topic 12): T cells
cluster 13 (topic 2): Cerebellar granule cells
cluster 14 (topic 11): Ex. neurons and Inhibitory neurons
cluster 15 (topic 8): Hepatocytes

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/Cusanovich_2018/postfit_v2"
cat(sprintf("Load results from %s \n", out.dir))
DA_res <- readRDS(file.path(out.dir, paste0("DAanalysis-Cusanovich2018-k=13-quantile/DA_regions_topics_10000iters.rds")))

# Load results from /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2

Gene score analysis

Set output directory

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2"

fig.dir <- "output/plotly/Cusanovich2018_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
Top genes

gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-absZ-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_tss_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))
genescore_res <- genescore_tss_res

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/Cusanovich_2018/postfit_v2/geneanalysis-Cusanovich2018-k=13-TSS-absZ-l2

Topic 1 (Erythroblasts)

Check some known marker genes

marker_genes <- c("Hbb-b1", "Hbb-b2", "Gypa")

gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 3 (Endothelial cells)

Check some known marker genes

marker_genes <- c("PECAM1", "CD106", "CD62E", "Sele", "Kdr", "ENG")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 5 (Proximal tubule)

Check some known marker genes

marker_genes <- c("PALDOB", "CUBN", "LRP2", "SLC34A1")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 7 (Cardiomyocytes)

Check some known marker genes

marker_genes <- c("Nppa", "Myl4", "SLN", "PITX2", "Myl7", "Gja5", "Myl2", "Myl3", "IRX4", "HAND1", "HEY2")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/3),3))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 8 (Hepatocytes)

Check some known marker genes

marker_genes <- c("SERPINA1", "TTR", "ALB","AFP","CYP3A4","CYP7A1","FABP1","ALR","Glut1","MET","FoxA1","FoxA2","CD29","PTP4A2","Prox1", "HNF1B")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/3),3))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

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.

Top genes

gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-genebody-absZ-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_gb_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))
genescore_res <- genescore_gb_res

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/Cusanovich_2018/postfit_v2/geneanalysis-Cusanovich2018-k=13-genebody-absZ-l2

Topic 1 (Erythroblasts)

Check some known marker genes

marker_genes <- c("Hbb-b1", "Hbb-b2", "Gypa")

gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 3 (Endothelial cells)

Check some known marker genes

marker_genes <- c("PECAM1", "CD106", "CD62E", "Sele", "Kdr", "ENG")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 5 (Proximal tubule)

Check some known marker genes

marker_genes <- c("PALDOB", "CUBN", "LRP2", "SLC34A1")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/2),2))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 7 (Cardiomyocytes)

Check some known marker genes

marker_genes <- c("Nppa", "Myl4", "SLN", "PITX2", "Myl7", "Gja5", "Myl2", "Myl3", "IRX4", "HAND1", "HEY2")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/3),3))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Topic 8 (Hepatocytes)

Check some known marker genes for Hepatocytes

marker_genes <- c("SERPINA1", "TTR", "ALB","AFP","CYP3A4","CYP7A1","FABP1","ALR","Glut1","MET","FoxA1","FoxA2","CD29","PTP4A2","Prox1", "HNF1B")
gene_scores <- genescore_res$Z
rownames(gene_scores) <- genescore_res$genes$SYMBOL
marker_gene_scores <- gene_scores[grep(paste(sprintf("^%s$", marker_genes), collapse = "|"), rownames(gene_scores), ignore.case = T),]

par(mfrow = c(ceiling(nrow(marker_gene_scores)/3),3))
for(i in 1:nrow(marker_gene_scores)){
  barplot(marker_gene_scores[i,], xlab = "topics", ylab = "gene score", main = rownames(marker_gene_scores)[i], col = colors_topics)
}

Compare gene scores from the gene-body model vs. the TSS model.

m     <- ncol(genescore_gb_res$Z)
plots <- vector("list",m)
names(plots) <- colnames(genescore_gb_res$Z)
for (i in 1:m) {
  dat <- data.frame(genebody = genescore_gb_res$Z[,i], tss = genescore_tss_res$Z[,i])
  plots[[i]] <- 
    ggplot(dat,aes_string(x = "genebody",y = "tss")) +
    geom_point(shape = 21, na.rm = TRUE, size = 1, alpha = 1/10) +
    geom_abline(intercept = 0, slope = 1, color="blue") + 
    labs(x = "gene body model",y = "TSS model", 
         title = paste("topic",i)) +
    theme_cowplot(9)
}

do.call(plot_grid,plots)

Gene-set enrichment analysis (GSEA)

Loading gene set data

library(pathways)

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

# Loading mouse gene set data.

TSS model

Top gene sets/pathways

gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-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/Cusanovich_2018/postfit_v2/geneanalysis-Cusanovich2018-k=13-TSS-absZ-l2

Gene body model

Top gene sets

gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-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/Cusanovich_2018/postfit_v2/geneanalysis-Cusanovich2018-k=13-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 Cusanovich et al (2018) scATAC-seq data

Kaixuan Luo

Load packages and some functions used in this analysis

Load samples and topic model (\(k = 13\)) results.

Visualize by Structure plot grouped by tissues

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

Gene score analysis

TSS model

Gene body model

Compare gene scores from the gene-body model vs. the TSS model.

Gene-set enrichment analysis (GSEA)

TSS model

Gene body model