Last updated: 2021-01-05

Checks: 7 0

Knit directory: scATACseq-topics/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it's best to always run the code in an empty environment.

The command set.seed(20200729) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 8f90a29. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/diff_count_Buenrostro2018_chomVAR_scPeaks.Rmd
    Untracked:  analysis/process_data_Buenrostro2018_Chen2019.Rmd
    Untracked:  analysis/single_cell_rnaseq_demo.Rmd
    Untracked:  output/clustering-Cusanovich2018.rds
    Untracked:  scripts/fit_all_models_Buenrostro_2018_chromVar_scPeaks_filtered.sbatch

Unstaged changes:
    Modified:   analysis/cisTopic_Buenrostro2018_chomVAR_scPeaks.Rmd
    Modified:   analysis/motif_gene_analysis_Buenrostro2018_Chen2019pipeline.Rmd
    Modified:   analysis/plots_Lareau2019_bonemarrow.Rmd
    Modified:   analysis/process_data_Buenrostro2018.Rmd
    Modified:   code/motif_analysis.R
    Modified:   scripts/diffcount_motif_analysis.R
    Modified:   scripts/fit_all_models_Buenrostro_2018.sbatch
    Modified:   scripts/fit_cisTopic_Buenrostro_2018_chromVAR_scPeaks.sh
    Modified:   scripts/postfit_Buenrostro2018.sh
    Modified:   scripts/postfit_gene_analysis.sbatch
    Modified:   scripts/postfit_motif_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/motif_gene_analysis_Buenrostro2018_chomVAR_scPeaks.Rmd) and HTML (docs/motif_gene_analysis_Buenrostro2018_chomVAR_scPeaks.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 8f90a29 kevinlkx 2021-01-05 wflow_publish("motif_gene_analysis_Buenrostro2018_chomVAR_scPeaks.Rmd")
html df5f7f4 kevinlkx 2021-01-05 Build site.
Rmd 1d060f3 kevinlkx 2021-01-05 fixed an issue with the gene score results and added motif results using top 2000 regions
html 7aec35f kevinlkx 2021-01-05 Build site.
Rmd 939efd8 kevinlkx 2021-01-05 fixed an issue with the gene score results and added motif results using top 2000 regions
html 9dbeb26 kevinlkx 2020-12-23 Build site.
Rmd be44b91 kevinlkx 2020-12-23 wflow_publish("motif_gene_analysis_Buenrostro2018_chomVAR_scPeaks.Rmd")
html 920a2d0 kevinlkx 2020-12-23 Build site.
Rmd 0f7f879 kevinlkx 2020-12-23 motif and gene analysis for Buenrostro result, data processed using chromVAR processed counts with scPeaks

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 based on chromVAR processed counts with scPeaks (> 1 sample).

Load packages and some functions used in this analysis

library(Matrix)
library(dplyr)
library(ggplot2)
library(cowplot)
library(fastTopics)
library(dplyr)
library(tidyr)
library(DT)
library(reshape)

Load data and topic model results

Load the binarized data and the \(k = 11\) Poisson NMF fit results from Buenrostro et al 2018 dataset using binarized scPeaks based on chromVAR processed counts with scPeaks (> 1 sample).

data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data_Chen2019pipeline/chromVAR/"
load(file.path(data.dir, "Buenrostro_2018_binarized_scPeaks.RData"))
cat(sprintf("%d x %d counts matrix.\n",nrow(counts),ncol(counts)))
# 2034 x 228965 counts matrix.
fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/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 = 4,verbose = FALSE)

print(p.structure)

Version Author Date
920a2d0 kevinlkx 2020-12-23

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

out.dir <- "/project2/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/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.

Distribution of z-scores

zscore_topics <-  melt(diff_count_topics$Z)
colnames(zscore_topics) <- c("region", "topic", "zscore")
levels(zscore_topics$topic) <- colnames(diff_count_topics$Z)

z.quantile.99 <- apply(abs(diff_count_topics$Z), 2, quantile, 0.99)
cat("z-score 99% quantile: \n")
print(z.quantile.99)

p.hist.zscores <- ggplot(zscore_topics, aes(x=zscore)) + 
  geom_histogram(binwidth=1, color="black", fill="white") + 
  coord_cartesian(xlim = c(-10, 20)) + theme_cowplot(font_size = 10) +
  facet_wrap(~ topic, ncol=4)

print(p.hist.zscores)

Version Author Date
920a2d0 kevinlkx 2020-12-23
# z-score 99% quantile: 
#       k1       k2       k3       k4       k5       k6       k7       k8 
# 5.429064 7.020322 5.828474 6.308036 6.161995 6.931451 4.862751 7.977127 
#       k9      k10      k11 
# 6.756263 6.858868 9.739615

Motif enrichment analysis using HOMER

homer.dir <- paste0(out.dir, "/motifanalysis-Buenrostro2018-k=11-quantile/HOMER/")
cat(sprintf("Directory of motif analysis result: %s \n", homer.dir))
homer_res <- readRDS(file.path(homer.dir, "/homer_knownResults.rds"))
selected_regions <- readRDS(file.path(homer.dir, "/selected_regions.rds"))

cat("Number of regions selected for each topic: \n")
print(mapply(nrow, selected_regions[1:(length(selected_regions)-1)]))

top_motifs <- data.frame(matrix(nrow=10, ncol = ncol(diff_count_topics$Z)))
colnames(top_motifs) <- colnames(diff_count_topics$Z)
for (k in colnames(top_motifs)){
  homer_motifs <- homer_res[[k]]
  colnames(homer_motifs) <- c("Motif.name", "Consensus", "P-value", "Log.P-value", "q-value (Benjamini)", 
                              "# of Target Sequences with Motif", "% of Target Sequences with Motif",
                              "# of Background Sequences with Motif", "% of Background Sequences with Motif")
  homer_motifs <- homer_motifs %>% separate(Motif.name, c("motif", "experiment", "database"), "/")
  top_motifs[,k] <- head(homer_motifs$motif, 10)
}

DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F,
              caption = "Top 10 motifs enriched in each topic.")
# Directory of motif analysis result: /project2/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/binarized//motifanalysis-Buenrostro2018-k=11-quantile/HOMER/ 
# Number of regions selected for each topic: 
#   k1   k2   k3   k4   k5   k6   k7   k8   k9  k10  k11 
# 2290 2290 2290 2290 2253 2258 2290 2290 2278 2264 2289
homer.dir <- paste0(out.dir, "/motifanalysis-Buenrostro2018-k=11-topN/HOMER/")
cat(sprintf("Directory of motif analysis result: %s \n", homer.dir))
homer_res <- readRDS(file.path(homer.dir, "/homer_knownResults.rds"))
selected_regions <- readRDS(file.path(homer.dir, "/selected_regions.rds"))

cat("Number of regions selected for each topic: \n")
print(mapply(nrow, selected_regions[1:(length(selected_regions)-1)]))

top_motifs <- data.frame(matrix(nrow=10, ncol = ncol(diff_count_topics$Z)))
colnames(top_motifs) <- colnames(diff_count_topics$Z)
for (k in colnames(top_motifs)){
  homer_motifs <- homer_res[[k]]
  colnames(homer_motifs) <- c("Motif.name", "Consensus", "P-value", "Log.P-value", "q-value (Benjamini)", 
                              "# of Target Sequences with Motif", "% of Target Sequences with Motif",
                              "# of Background Sequences with Motif", "% of Background Sequences with Motif")
  homer_motifs <- homer_motifs %>% separate(Motif.name, c("motif", "experiment", "database"), "/")
  top_motifs[,k] <- head(homer_motifs$motif, 10)
}

DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F,
              caption = "Top 10 motifs enriched in each topic.")
# Directory of motif analysis result: /project2/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/binarized//motifanalysis-Buenrostro2018-k=11-topN/HOMER/ 
# Number of regions selected for each topic: 
#   k1   k2   k3   k4   k5   k6   k7   k8   k9  k10  k11 
# 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000

Top genes

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))
load(file.path(gene.dir, "genescores_gsea.Rdata"))

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

rownames(gene_scores) <- genes[match(rownames(gene_scores), genes$ENSEMBL), "SYMBOL"]

for (k in colnames(top_genes)){
  top_genes[,k] <- rownames(gene_scores)[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}

DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F,
              caption = "Top 10 genes in each topic.")
# Directory of gene analysis result: /project2/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/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_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- colnames(gene_scores)

rownames(gene_scores) <- genes[match(rownames(gene_scores), genes$ENSEMBL), "SYMBOL"]

for (k in colnames(top_genes)){
  top_genes[,k] <- rownames(gene_scores)[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}

DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F,
              caption = "Top 10 genes in each topic.")
# Directory of gene analysis result: /project2/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/binarized//geneanalysis-Buenrostro2018-k=11-genebody-sum

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))
load(file.path(gene.dir, "genescores_gsea.Rdata"))

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

rownames(gene_scores) <- genes[match(rownames(gene_scores), genes$ENSEMBL), "SYMBOL"]

for (k in colnames(top_genes)){
  top_genes[,k] <- rownames(gene_scores)[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}

DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F,
              caption = "Top 10 genes in each topic.")
# Directory of gene analysis result: /project2/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/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_genes <- data.frame(matrix(nrow=10, ncol = ncol(gene_scores)))
colnames(top_genes) <- colnames(gene_scores)

rownames(gene_scores) <- genes[match(rownames(gene_scores), genes$ENSEMBL), "SYMBOL"]

for (k in colnames(top_genes)){
  top_genes[,k] <- rownames(gene_scores)[head(order(abs(gene_scores[,k]), decreasing=TRUE), 10)]
}

DT::datatable(data.frame(rank = 1:10, top_genes), rownames = F,
              caption = "Top 10 genes in each topic.")
# Directory of gene analysis result: /project2/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/binarized//geneanalysis-Buenrostro2018-k=11-TSS-sum

Gene-set enrichment analysis (GSEA)

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_pathways_up[,k] <- gene_set_info[match(top_IDs_up, gene_set_info$id),c("name", "id")] %>% 
                     unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
  
  gsea_down <- gsea_topic[gsea_topic$ES < 0,]
  top_IDs_down <- as.character(gsea_down[head(order(gsea_down$pval), 10), "pathway"])
  top_pathways_down[,k] <- gene_set_info[match(top_IDs_down, gene_set_info$id),c("name", "id")] %>% 
                     unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
}

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/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/binarized//geneanalysis-Buenrostro2018-k=11-genebody-l2
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_pathways_up[,k] <- gene_set_info[match(top_IDs_up, gene_set_info$id),c("name", "id")] %>% 
                     unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
  
  gsea_down <- gsea_topic[gsea_topic$ES < 0,]
  top_IDs_down <- as.character(gsea_down[head(order(gsea_down$pval), 10), "pathway"])
  top_pathways_down[,k] <- gene_set_info[match(top_IDs_down, gene_set_info$id),c("name", "id")] %>% 
                     unite("pathway", c("name", "id"), sep = ".", remove = TRUE)
}

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/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/binarized//geneanalysis-Buenrostro2018-k=11-genebody-sum
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/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/binarized//geneanalysis-Buenrostro2018-k=11-TSS-l2
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/xinhe/kevinluo/scATACseq-topics/output/Buenrostro_2018_chromVAR_scPeaks/binarized//geneanalysis-Buenrostro2018-k=11-TSS-sum

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          tidyr_1.1.2      fastTopics_0.4-6
# [5] cowplot_1.1.0    ggplot2_3.3.2    dplyr_1.0.2      Matrix_1.2-18   
# 
# loaded via a namespace (and not attached):
#  [1] ggrepel_0.9.0      Rcpp_1.0.5         lattice_0.20-41    prettyunits_1.1.1 
#  [5] rprojroot_2.0.2    digest_0.6.27      plyr_1.8.6         R6_2.5.0          
#  [9] MatrixModels_0.4-1 evaluate_0.14      coda_0.19-4        httr_1.4.2        
# [13] pillar_1.4.7       rlang_0.4.9        progress_1.2.2     lazyeval_0.2.2    
# [17] data.table_1.13.4  irlba_2.3.3        SparseM_1.78       whisker_0.4       
# [21] rmarkdown_2.6      labeling_0.4.2     Rtsne_0.15         stringr_1.4.0     
# [25] htmlwidgets_1.5.3  munsell_0.5.0      compiler_3.6.1     httpuv_1.5.4      
# [29] xfun_0.19          pkgconfig_2.0.3    mcmc_0.9-7         htmltools_0.5.0   
# [33] tidyselect_1.1.0   tibble_3.0.4       workflowr_1.6.2    quadprog_1.5-8    
# [37] matrixStats_0.57.0 viridisLite_0.3.0  crayon_1.3.4       conquer_1.0.2     
# [41] withr_2.3.0        later_1.1.0.1      MASS_7.3-53        grid_3.6.1        
# [45] jsonlite_1.7.2     gtable_0.3.0       lifecycle_0.2.0    git2r_0.27.1      
# [49] magrittr_2.0.1     scales_1.1.1       RcppParallel_5.0.2 stringi_1.5.3     
# [53] farver_2.0.3       fs_1.3.1           promises_1.1.1     ellipsis_0.3.1    
# [57] generics_0.1.0     vctrs_0.3.6        tools_3.6.1        glue_1.4.2        
# [61] purrr_0.3.4        crosstalk_1.1.0.1  hms_0.5.3          yaml_2.2.1        
# [65] colorspace_2.0-0   plotly_4.9.2.1     knitr_1.30         quantreg_5.75     
# [69] MCMCpack_1.4-9