Last updated: 2021-03-21
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Knit directory: hesc-epigenomics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 701a84d | cnluzon | 2021-03-21 | Annotations cleanup |
| Rmd | ef20c53 | cnluzon | 2021-03-11 | Annotations |
| html | ef20c53 | cnluzon | 2021-03-11 | Annotations |
This notebook shows how the genome annotations relevant for this publication were calculated.
hg38.hg38 overlapping with bivalent peaks from (Court and Arnaud 2017).#' Select DESeq results by pval and fc cutoff and returns GRanges
#'
#' @param diff_lfc Results dataframe
#' @param loci Corresponding GRanges
#' @param p_cutoff Pvalue cutoff
#' @param fc_cutoff Fold change cutoff
#'
#' @return GRanges with filtered elements
select_signif <- function(diff_lfc, loci, p_cutoff = 0.05, fc_cutoff = 1) {
diff_lfc <- diff_lfc[!is.na(diff_lfc$padj), ]
signif_tss <- diff_lfc[diff_lfc$padj <= p_cutoff & abs(diff_lfc$log2FoldChange) > fc_cutoff, ]
mcols(loci) <- NULL
signif_gr <- loci[as.numeric(rownames(signif_tss)), ]
signif_gr$score <- signif_tss$log2FoldChange
signif_gr
}
#' Intersect 3 gene results by gene name
#'
#' @param glist List of gene results
#' @param field Field used to intersect
#'
#' @return A list of intersecting elements
intersect_genes_3 <- function(glist, field = "gene_id") {
intersect(intersect(glist[[1]][[field]],
glist[[2]][[field]]),
glist[[3]][[field]])
}
#' Subset a named granges by name
#'
#' @param names Names list
#' @param granges GRanges to subset.
#'
#' @return GRanges
select_genes <- function(names, granges) {
sort(granges[granges$name %in% names, ])
}
#' Export genes that overlap in a set of replicates (3)
#'
#' @param replicates Gene counts results list
#' @param path Output file
export_gene_set <- function(replicates, path) {
gene_names <- intersect_genes_3(replicates)
genes_all <- genes_hg38()
genes_all <- genes_all[, "name"]
result <- select_genes(gene_names, genes_all)
export(result, path)
}
#' Select by quantile
#'
#' @param counts Counts table.
#' @param length_cutoff Gene length cutoff.
#' @param value_cutoff Value cutoff
#' @param min_q Minimum quantile to consider
#' @param max_q Maximum quantile to consider
#' @param field Which field to use (default = TPM).
#'
#' @return filtered data.frame
quantile_group <- function(counts, min_q, max_q,
value_cutoff = 0,
length_cutoff = 500,
field = "TPM") {
counts <- dplyr::filter(counts, .data[[field]] > value_cutoff &
length > length_cutoff)
q <- quantile(counts[, field], probs = c(min_q, max_q))
dplyr::filter(counts, .data[[field]] >= q[1] & .data[[field]] <= q[2])
}Gene annotation is obtained from UCSC hg38 annotation.
genes <- genes_hg38()Only genes that have a gene symbol associated to it are kept, leaving a set of 25747.
Download gene annotation.
Bivalent genes are obtained from UCSC hg38 annotation using as names the ones in (Court and Arnaud 2017) supplementary table.
gene_file <- file.path(params$datadir, "meta/Court_2017_gene_names_uniq.txt")
gene_list <- read.table(gene_file, header=F)
colnames(gene_list) <- c("name")
biv_genes_name <- get_genes_by_name(gene_list$name)From the list of 5378 gene names from (Court and Arnaud 2017), 3925 unique genomic loci are successfully retrieved.
Download bivalent gene annotation.
Download genes list.
Bivalent genes are obtained from overlap of hg38-liftovered from original (Court and Arnaud 2017) genes with UCSC hg38 annotation.
gene_file <- file.path(params$datadir, "bed/Bivalent_Court2017.hg38.bed")
gene_loci <- import(gene_file)
biv_genes_overlap <- get_genes_by_overlap(gene_loci)
export(biv_genes_overlap, "./data/bed/Kumar_2020/Court_2017_bivalent_genes.bed")From the list of 5378 gene names from (Court and Arnaud 2017), 4874 unique genomic loci are successfully retrieved.
Download bivalent gene annotation.
field_list <- list(names = biv_genes_name$name, overlap = biv_genes_overlap$name)
ggvenn(field_list, fill_alpha = 0.5, text_size = 5, stroke_color = "#ffffff") +
ggtitle("Names vs location")
| Version | Author | Date |
|---|---|---|
| ef20c53 | cnluzon | 2021-03-11 |
Some of the names cases are: either the gene has not been annotated because locus is proximal to annotation but not overlapping it (this may be because of lift over of coordinates), or because the name from UCSC hg38 annotation does not match the name in Court 2017 original annotated gene name.
bw_cond_1 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H3K27m3_H9_Ni_rep[1-3].hg38.scaled.bw"
)
bw_cond_2 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H3K27m3_H9_Pr_rep[1-3].hg38.scaled.bw"
)
all_hg38_genes <- genes_hg38()
tss <- promoters(all_hg38_genes, upstream = 2500, downstream = 2500)
mcols(tss) <- NULL
c1 <- bw_loci(bw_cond_1, tss)
c2 <- bw_loci(bw_cond_2, tss)
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "Primed", estimate_size_factors = FALSE)
diff_lfc <- lfcShrink(diff, coef="condition_Primed_vs_Naive", type="apeglm")
diff <- results(diff, alpha = params$pval_cutoff)
plotMA(diff_lfc)
| Version | Author | Date |
|---|---|---|
| ef20c53 | cnluzon | 2021-03-11 |
signif_gr <- select_signif(diff, c1, p_cutoff = params$pval_cutoff)
hits <- findOverlaps(signif_gr, all_hg38_genes)
signif_gr_w_name <- signif_gr
signif_gr_w_name$name <- "None"
signif_gr_w_name[queryHits(hits), ]$name <- all_hg38_genes[subjectHits(hits), ]$name
export(signif_gr_w_name, file.path(params$outgenes, "Kumar_2020_H3K27m3_diff_tss.bed"))
# Select bivalent ones
signif_gr_biv <- subsetByOverlaps(signif_gr_w_name, biv_genes_overlap)
export(signif_gr_biv, file.path(params$outgenes, "Kumar_2020_H3K27m3_diff_bivalent.bed"))biv_genes <- biv_genes_overlap
tss <- promoters(biv_genes, upstream = 2500, downstream = 2500)
mcols(tss) <- NULL
c1 <- bw_loci(bw_cond_1, tss)
c2 <- bw_loci(bw_cond_2, tss)
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "Primed", estimate_size_factors = FALSE)
diff_lfc <- lfcShrink(diff, coef="condition_Primed_vs_Naive", type="apeglm")
diff <- results(diff, alpha = params$pval_cutoff)
plotMA(diff_lfc)
| Version | Author | Date |
|---|---|---|
| ef20c53 | cnluzon | 2021-03-11 |
signif_gr <- select_signif(diff, c1, p_cutoff = params$pval_cutoff)
# export(signif_gr, "./data/bed/Kumar_2020_H3K27m3_diff_tss_bivalent.bed")global <- import(file.path(params$outgenes, "Kumar_2020_H3K27m3_diff_bivalent.bed"))
local <- subsetByOverlaps(biv_genes_overlap, signif_gr)
field_list <- list(global = global$name, local = local$name)
ggvenn(field_list, fill_alpha = 0.5, text_size = 5, stroke_color = "#ffffff") +
ggtitle("Global vs local")
| Version | Author | Date |
|---|---|---|
| ef20c53 | cnluzon | 2021-03-11 |
bw_cond_1 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H2Aub_H9_Ni_rep[1-3].hg38.scaled.bw"
)
bw_cond_2 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H2Aub_H9_Pr_rep[1-3].hg38.scaled.bw"
)
all_hg38_genes <- genes_hg38()
tss <- promoters(all_hg38_genes, upstream = 1500, downstream = 1500)
mcols(tss) <- NULL
c1 <- bw_loci(bw_cond_1, tss)
c2 <- bw_loci(bw_cond_2, tss)
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "Primed", estimate_size_factors = FALSE)
diff_lfc <- lfcShrink(diff, coef="condition_Primed_vs_Naive", type="apeglm")
diff <- results(diff, alpha = params$pval_cutoff)
plotMA(diff_lfc)
| Version | Author | Date |
|---|---|---|
| ef20c53 | cnluzon | 2021-03-11 |
signif_gr <- select_signif(diff, c1, p_cutoff = params$pval_cutoff)
hits <- findOverlaps(signif_gr, all_hg38_genes)
signif_gr_w_name <- signif_gr
signif_gr_w_name$name <- "None"
signif_gr_w_name[queryHits(hits), ]$name <- all_hg38_genes[subjectHits(hits), ]$name
export(signif_gr_w_name, file.path(params$outgenes, "Kumar_2020_H2Aub_diff_tss.bed"))
# Select bivalent ones
signif_gr_biv <- subsetByOverlaps(signif_gr_w_name, biv_genes_overlap)
export(signif_gr_biv, file.path(params$outgenes, "Kumar_2020_H2Aub_diff_bivalent.bed"))bw_cond_1 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H3K4m3_H9_Ni_rep[1-3].hg38.scaled.bw"
)
bw_cond_2 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H3K4m3_H9_Pr_rep[1-3].hg38.scaled.bw"
)
all_hg38_genes <- genes_hg38()
tss <- promoters(all_hg38_genes, upstream = 1500, downstream = 1500)
mcols(tss) <- NULL
c1 <- bw_loci(bw_cond_1, tss)
c2 <- bw_loci(bw_cond_2, tss)
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "Primed", estimate_size_factors = FALSE)
diff_lfc <- lfcShrink(diff, coef="condition_Primed_vs_Naive", type="apeglm")
diff <- results(diff, alpha = params$pval_cutoff)
plotMA(diff_lfc)
| Version | Author | Date |
|---|---|---|
| ef20c53 | cnluzon | 2021-03-11 |
signif_gr <- select_signif(diff, c1, p_cutoff = params$pval_cutoff)
hits <- findOverlaps(signif_gr, all_hg38_genes)
signif_gr_w_name <- signif_gr
signif_gr_w_name$name <- "None"
signif_gr_w_name[queryHits(hits), ]$name <- all_hg38_genes[subjectHits(hits), ]$name
export(signif_gr_w_name, file.path(params$outgenes, "Kumar_2020_H3K4m3_diff_tss.bed"))
# Select bivalent ones
signif_gr_biv <- subsetByOverlaps(signif_gr_w_name, biv_genes_overlap)
export(signif_gr_biv, file.path(params$outgenes, "Kumar_2020_H3K4m3_diff_bivalent.bed"))bw_cond_1 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H3K27m3_H9_Ni_rep[1-3].hg38.scaled.bw"
)
bw_cond_2 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H3K27m3_H9_Pr_rep[1-3].hg38.scaled.bw"
)
bs <- params$bin_size
c1 <- bw_bins(bw_cond_1, bin_size = bs, genome = "hg38")
c2 <- bw_bins(bw_cond_2, bin_size = bs, genome = "hg38")
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "Primed", estimate_size_factors = FALSE)
diff <- results(diff, alpha = params$pval_cutoff)
mcols(c1) <- NULL
c1$logfc <- diff$log2FoldChange
c1$padj <- diff$padj
p_cutoff <- params$pval_cutoff
c1$score <- 0
c1 <- c1[!is.na(c1$padj), ]
c1[c1$padj <= p_cutoff & c1$logfc > 0, ]$score <- 1
c1[c1$padj <= p_cutoff & c1$logfc < 0, ]$score <- -1
c1 <- c1[c1$padj <= p_cutoff]
export(c1, file.path(params$outbins, "Kumar_2020_H3K27m3_signif_ni_05.bed"))
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
bw_cond_1 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H3K4m3_H9_Ni_rep[1-3].hg38.scaled.bw"
)
bw_cond_2 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H3K4m3_H9_Pr_rep[1-3].hg38.scaled.bw"
)
bs <- params$bin_size
c1 <- bw_bins(bw_cond_1, bin_size = bs, genome = "hg38")
c2 <- bw_bins(bw_cond_2, bin_size = bs, genome = "hg38")
diff <- bw_granges_diff_analysis(c2, c1, "Primed", "Naive", estimate_size_factors = FALSE)
diff <- results(diff, alpha = params$pval_cutoff)
mcols(c1) <- NULL
c1$logfc <- diff$log2FoldChange
c1$padj <- diff$padj
p_cutoff <- params$pval_cutoff
c1$score <- 0
c1 <- c1[!is.na(c1$padj), ]
c1[c1$padj <= p_cutoff & c1$logfc > 0, ]$score <- 1
c1[c1$padj <= p_cutoff & c1$logfc < 0, ]$score <- -1
c1 <- c1[c1$padj <= p_cutoff]
export(c1, file.path(params$outbins, "Kumar_2020_H3K4m3_signif_ni_05.bed"))
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
bw_cond_1 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H2Aub_H9_Ni_rep[1-3].hg38.scaled.bw"
)
bw_cond_2 <-
list.files(
file.path(params$datadir, "bw/Kumar_2020"),
full.names = T,
pattern = "H2Aub_H9_Pr_rep[1-3].hg38.scaled.bw"
)
bs <- params$bin_size
c1 <- bw_bins(bw_cond_1, bin_size = bs, genome = "hg38")
c2 <- bw_bins(bw_cond_2, bin_size = bs, genome = "hg38")
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "Primed", estimate_size_factors = FALSE)
diff <- results(diff, alpha = params$pval_cutoff)
mcols(c1) <- NULL
c1$logfc <- diff$log2FoldChange
c1$padj <- diff$padj
p_cutoff <- params$pval_cutoff
c1$score <- 0
c1 <- c1[!is.na(c1$padj), ]
c1[c1$padj <= p_cutoff & c1$logfc > 0, ]$score <- 1
c1[c1$padj <= p_cutoff & c1$logfc < 0, ]$score <- -1
c1 <- c1[c1$padj <= p_cutoff]
export(c1, file.path(params$outbins,"Kumar_2020_H2AUb_signif_ni_05.bed"))
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
Data: Used public data from (Collier et al. 2017): H9 embryonic stem cells in Naïve and Primed state, three biological replicates.
| dataset_id | gsm_id | description | filename | run_id | input | layout | experiment | |
|---|---|---|---|---|---|---|---|---|
| 26 | Collier_2017 | GSM2449055 | hESC_H9_naive_1 | hESC_H9_naive_1 | SRR5151102 | - | single | RNA-seq |
| 27 | Collier_2017 | GSM2449056 | hESC_H9_naive_2 | hESC_H9_naive_2 | SRR5151103 | - | single | RNA-seq |
| 28 | Collier_2017 | GSM2449057 | hESC_H9_naive_3 | hESC_H9_naive_3 | SRR5151104 | - | single | RNA-seq |
| 29 | Collier_2017 | GSM2449058 | hESC_H9_primed_1 | hESC_H9_primed_1 | SRR5151105 | - | single | RNA-seq |
| 30 | Collier_2017 | GSM2449059 | hESC_H9_primed_2 | hESC_H9_primed_2 | SRR5151106 | - | single | RNA-seq |
| 31 | Collier_2017 | GSM2449060 | hESC_H9_primed_3 | hESC_H9_primed_3 | SRR5151107 | - | single | RNA-seq |
RNA-seq data was analysed by a standard RNA-seq pipeline v2.0, available on nf-core (Ewels et al. 2019) with mostly default settings. For more information about the specific steps involved in this analysis, refer to the repository documentation: https://nf-co.re/rnaseq/2.0, using the option --aligner star_rsem.
Reference genome used was hg38, downloaded from illumina iGenomes:
rsem_files <- list(
naive = list.files(params$rnaseqdir,
pattern = "Naive.*genes.results", full.names = T),
primed = list.files(params$rnaseqdir,
pattern = "Primed.*genes.results", full.names = T)
)
counts_naive <- lapply(rsem_files$naive,
read.csv,
header = T,
sep = "\t")
counts_primed <- lapply(rsem_files$primed,
read.csv,
header = T,
sep = "\t")
# Size of the quantile group
size = 0.1
min_qs <- c(0, 0.5 - (size/2), 1-size)
max_qs <- c(0+size, 0.5 + (size/2), 1)
quantile_low <- purrr::partial(
quantile_group,
min_q = min_qs[1],
max_q = max_qs[1],
length_cutoff = params$length_cutoff,
value_cutoff = params$value_cutoff,
field = params$deciding_stat
)
quantile_med <- purrr::partial(
quantile_group,
min_q = min_qs[2],
max_q = max_qs[2],
length_cutoff = params$length_cutoff,
value_cutoff = params$value_cutoff,
field = params$deciding_stat
)
quantile_high <- purrr::partial(
quantile_group,
min_q = min_qs[3],
max_q = max_qs[3],
length_cutoff = params$length_cutoff,
value_cutoff = params$value_cutoff,
field = params$deciding_stat
)
naive_top <- map(counts_naive, quantile_high)
naive_med <- map(counts_naive, quantile_med)
naive_low <- map(counts_naive, quantile_low)
primed_top <- map(counts_primed, quantile_high)
primed_med <- map(counts_primed, quantile_med)
primed_low <- map(counts_primed, quantile_low)
export_gene_set(naive_top, file.path("./data/bed/Collier_2017", "Collier_2017_Naive_top.bed"))
export_gene_set(naive_med, file.path("./data/bed/Collier_2017", "Collier_2017_Naive_med.bed"))
export_gene_set(naive_low, file.path("./data/bed/Collier_2017", "Collier_2017_Naive_low.bed"))
export_gene_set(primed_top, file.path("./data/bed/Collier_2017", "Collier_2017_Primed_top.bed"))
export_gene_set(primed_med, file.path("./data/bed/Collier_2017", "Collier_2017_Primed_med.bed"))
export_gene_set(primed_low, file.path("./data/bed/Collier_2017", "Collier_2017_Primed_low.bed"))
Here I build a table where each Gene is annotated with flags:
annotate_up_and_down <- function(df, gr, field) {
df[[field]] <- 0
names_up <- gr[gr$score > 0, ]$name
df[names_up, field] <- 1
names_down <- gr[gr$score < 0, ]$name
df[names_down, field] <- -1
df
}
genes_all <- genes_hg38()
annotations <- c("./data/bed/Kumar_2020/Court_2017_bivalent_genes.bed",
"./data/bed/Kumar_2020/gene_stats/Kumar_2020_H3K27m3_diff_tss.bed",
"./data/bed/Kumar_2020/gene_stats/Kumar_2020_H3K4m3_diff_tss.bed",
"./data/bed/Kumar_2020/gene_stats/Kumar_2020_H2Aub_diff_tss.bed",
"./data/bed/Collier_2017/Collier_2017_Naive_up_05_logfc1.bed",
"./data/bed/Collier_2017/Collier_2017_Naive_down_05_logfc1.bed")
gr_annot <- lapply(annotations, import)
df <- data.frame(genes_all)
rownames(df) <- df$name
df$bivalent <- 0
df[gr_annot[[1]]$name, "bivalent"] <- 1
df <- annotate_up_and_down(df, gr_annot[[2]], "H3K27m3")
df <- annotate_up_and_down(df, gr_annot[[3]], "H3K4m3")
df <- annotate_up_and_down(df, gr_annot[[4]], "H2Aub")
df$expression <- 0
df[gr_annot[[5]]$name, "expression"] <- 1
df[gr_annot[[6]]$name, "expression"] <- -1
df$chrX <- 0
df[df$seqnames == "chrX", "chrX"] <- 1
write.table(df, "./data/bed/Kumar_2020_gene_annotation_table.tsv", row.names = F, sep = "\t", quote = F)
# write.table(df, "./data/bed/Kumar_2020_gene_annotation_table_verbose.tsv", row.names = F, sep = "\t", quote = F)
sessionInfo()R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=sv_SE.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=sv_SE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=sv_SE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=sv_SE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] DESeq2_1.30.0
[2] SummarizedExperiment_1.20.0
[3] MatrixGenerics_1.2.0
[4] matrixStats_0.58.0
[5] tidyr_1.1.2
[6] cowplot_1.1.1
[7] xfun_0.21
[8] purrr_0.3.4
[9] rtracklayer_1.50.0
[10] org.Hs.eg.db_3.11.4
[11] TxDb.Hsapiens.UCSC.hg38.knownGene_3.10.0
[12] GenomicFeatures_1.40.1
[13] AnnotationDbi_1.52.0
[14] Biobase_2.50.0
[15] GenomicRanges_1.42.0
[16] GenomeInfoDb_1.26.2
[17] IRanges_2.24.1
[18] S4Vectors_0.28.1
[19] BiocGenerics_0.36.0
[20] ggvenn_0.1.8
[21] dplyr_1.0.4
[22] knitr_1.31
[23] ggplot2_3.3.3
[24] wigglescout_0.12.8
[25] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ellipsis_0.3.1 rprojroot_2.0.2
[4] XVector_0.30.0 fs_1.5.0 listenv_0.8.0
[7] furrr_0.2.2 farver_2.0.3 bit64_4.0.5
[10] mvtnorm_1.1-1 apeglm_1.10.0 xml2_1.3.2
[13] codetools_0.2-18 splines_4.0.4 cachem_1.0.4
[16] geneplotter_1.68.0 Rsamtools_2.6.0 annotate_1.68.0
[19] dbplyr_2.1.0 compiler_4.0.4 httr_1.4.2
[22] assertthat_0.2.1 Matrix_1.3-2 fastmap_1.1.0
[25] later_1.1.0.1 htmltools_0.5.1.1 prettyunits_1.1.1
[28] tools_4.0.4 coda_0.19-4 gtable_0.3.0
[31] glue_1.4.2 GenomeInfoDbData_1.2.4 reshape2_1.4.4
[34] rappdirs_0.3.3 Rcpp_1.0.6 bbmle_1.0.23.1
[37] vctrs_0.3.6 Biostrings_2.58.0 stringr_1.4.0
[40] globals_0.14.0 lifecycle_1.0.0 XML_3.99-0.5
[43] future_1.21.0 MASS_7.3-53.1 zlibbioc_1.36.0
[46] scales_1.1.1 hms_1.0.0 promises_1.2.0.1
[49] RColorBrewer_1.1-2 yaml_2.2.1 curl_4.3
[52] memoise_2.0.0 emdbook_1.3.12 bdsmatrix_1.3-4
[55] biomaRt_2.44.4 stringi_1.5.3 RSQLite_2.2.3
[58] highr_0.8 genefilter_1.72.0 BiocParallel_1.24.1
[61] rlang_0.4.10 pkgconfig_2.0.3 bitops_1.0-6
[64] evaluate_0.14 lattice_0.20-41 GenomicAlignments_1.26.0
[67] labeling_0.4.2 bit_4.0.4 tidyselect_1.1.0
[70] parallelly_1.23.0 plyr_1.8.6 magrittr_2.0.1
[73] R6_2.5.0 generics_0.1.0 DelayedArray_0.16.0
[76] DBI_1.1.1 pillar_1.4.7 whisker_0.4
[79] withr_2.4.1 survival_3.2-7 RCurl_1.98-1.2
[82] tibble_3.0.6 crayon_1.4.1 BiocFileCache_1.12.1
[85] rmarkdown_2.6 progress_1.2.2 locfit_1.5-9.4
[88] blob_1.2.1 git2r_0.28.0 digest_0.6.27
[91] xtable_1.8-4 numDeriv_2016.8-1.1 httpuv_1.5.5
[94] openssl_1.4.3 munsell_0.5.0 askpass_1.1