Last updated: 2022-02-24

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Knit directory: scATACseq-topics/

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    Modified:   scripts/fit_all_models_Cusanovich2018.sh
    Modified:   scripts/postfit_Buenrostro2018_Chen2019pipeline.sh
    Modified:   scripts/postfit_Cusanovich2018.sh
    Modified:   scripts/postfit_gene_analysis.sbatch

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 f05e211 kevinlkx 2022-02-24 wflow_publish("analysis/gene_analysis_Buenrostro2018_Chen2019pipeline_v2.Rmd")
html 4ee008f kevinlkx 2022-02-22 Build site.
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)

Version Author Date
4ee008f kevinlkx 2022-02-22

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)

Version Author Date
4ee008f kevinlkx 2022-02-22

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)

Version Author Date
4ee008f kevinlkx 2022-02-22

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)

Version Author Date
4ee008f kevinlkx 2022-02-22

TSS model version 3

  • TSS model, normalized by the l2 norm of weights
  • use Z scores
gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-TSS-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-TSS-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)

TSS model version 4

  • TSS model, normalized by the sum of weights
  • use Z scores
gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-TSS-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-TSS-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 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-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-genebody-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)

Version Author Date
4ee008f kevinlkx 2022-02-22

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)

Version Author Date
4ee008f kevinlkx 2022-02-22

Gene body model version 2

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 sum of weights
  • use abs(z) scores
gene.dir <- paste0(out.dir, "/geneanalysis-Buenrostro2018-k=11-genebody-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-genebody-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)

Version Author Date
4ee008f kevinlkx 2022-02-22

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)

Version Author Date
4ee008f kevinlkx 2022-02-22

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.
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.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_1.0.8       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_2.0-3    
#   [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_3.2.0            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_1.6.2           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_1.0.1          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_1.1.2     parallelly_1.30.0   
#  [88] plyr_1.8.6           magrittr_2.0.2       R6_2.5.1            
#  [91] generics_0.1.2       DBI_1.1.2            pillar_1.7.0        
#  [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_1.5.0         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