Histone marks at gene groups per expression level
Carmen Navarro
2021-02-07
Last updated: 2021-02-12
Checks: 7 0
Knit directory: hesc-epigenomics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f538b73 | cnluzon | 2021-02-12 | Groups update |
| html | f538b73 | cnluzon | 2021-02-12 | Groups update |
| html | 8c0355b | cnluzon | 2021-02-07 | Build site. |
| Rmd | f903045 | cnluzon | 2021-02-07 | Histone marks per gene group report |
Summary
Here we look at our histone marks in gene groups separated by expression levels (high-med-low) per cell type. These groups were calculated using RNA-seq data from [@collier2017]. For more details, see corresponding analysis report.
Helper functions
colors_list <- c("Naive_EZH2i"="#5F9EA0",
"Naive_Untreated"="#278b8b",
"Primed_EZH2i"="#f47770",
"Primed_Untreated"="#f44b34")
style_info <- read.table(params$styles, header = T, sep = "\t")
rownames(style_info) <- style_info$bw
# Clean unnecesary labels in a grid
remove_extra_captions <- function(plot_list) {
for (i in 2:length(plot_list)) {
plot_list[[i]] <- plot_list[[i]] + theme(legend.position = "none") + ggtitle("")
}
plot_list
}
set_global_y_axis <- function(plot_list, limits) {
for (i in 1:length(plot_list)) {
plot_list[[i]] <- plot_list[[i]] + ylim(limits)
}
plot_list
}H3K27m3
Highly expressed genes
bedfiles <- list.files(params$genesdir, full.names = T, pattern = "top")
bwfiles <- list.files(params$bwdir, full.names = T)
bwinput <- bwfiles[grepl("IN.*pooled", bwfiles)]
bwsignal <- bwfiles[grepl("H3K27m3.*pooled.hg38.scaled", bwfiles)]
colors <- as.character(style_info[basename(c(bwsignal, bwinput)), "color_cond"])
labels <- style_info[basename(c(bwsignal, bwinput)), "label"]
up <- 10000
dw <- 10000
bs <- 500
md <- "stretch"
global_lims <- c(0, 1.5)
this_plot <- partial(plot_bw_profile, bwfiles = c(bwsignal, bwinput),
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels)
plots <- purrr::map(bedfiles, this_plot)
plots[[1]] <- plots[[1]] + ylim(global_lims) + theme(legend.position = "none") + ggtitle("H3K27m3 @Top genes")
plots[[2]] <- plots[[2]] + ylim(global_lims) + ylab("") + ggtitle("")
plot_grid(plotlist=plots, nrow=1)
Norm to input
colors <- as.character(style_info[basename(bwsignal), "color_cond"])
labels <- style_info[basename(bwsignal), "label"]
global_lims <- c(0, 1.5)
this_plot <- partial(plot_bw_profile, bwfiles = bwsignal, bg_bwfiles = bwinput,
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels)
plots <- purrr::map(bedfiles, this_plot)
plots[[1]] <- plots[[1]] + ylim(global_lims) + theme(legend.position = "none") + ggtitle("H3K27m3 @Top genes")
plots[[2]] <- plots[[2]] + ylim(global_lims) + ylab("") + ggtitle("")
plot_grid(plotlist=plots, nrow=1)
As shown in the previous report, there is some overlap between these groups of genes:
naive_top <- import(bedfiles[[1]])
primed_top <- import(bedfiles[[2]])
field_list <- list(naive=naive_top$name,
primed=primed_top$name)
ggvenn(field_list, fill_color = c(colors_list[["Naive_Untreated"]], colors_list[["Primed_Untreated"]]), fill_alpha = 0.5, text_size = 5, stroke_color = "#ffffff") +
ggtitle("Naive vs primed top expressed genes")
Naive-only vs primed-only
naive_only_top <- setdiff(naive_top, primed_top)
primed_only_top <- setdiff(primed_top, naive_top)
both <- intersect(naive_top, primed_top)
colors <- as.character(style_info[basename(c(bwsignal, bwinput)), "color_cond"])
labels <- style_info[basename(c(bwsignal, bwinput)), "label"]
global_lims <- c(0, 2)
this_plot <- partial(plot_bw_profile, bwfiles = c(bwsignal, bwinput),
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels)
plots <- purrr::map(list(naive_only_top, primed_only_top, both), this_plot)
plots[[1]] <- plots[[1]] + ylim(global_lims) + theme(legend.position = "none") + ggtitle("Naive-only")
plots[[2]] <- plots[[2]] + ylim(global_lims) + ylab("") + theme(legend.position = "none") + ggtitle("Primed-only")
plots[[3]] <- plots[[3]] + ylim(global_lims) + ylab("") + ggtitle("Both")
plot_grid(plotlist=plots, ncol=2)
Norm to input
colors <- as.character(style_info[basename(bwsignal), "color_cond"])
labels <- style_info[basename(bwsignal), "label"]
global_lims <- c(0, 2)
this_plot <- partial(plot_bw_profile, bwfiles = bwsignal, bg_bwfiles = bwinput,
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels)
plots <- purrr::map(list(naive_only_top, primed_only_top, both), this_plot)
plots[[1]] <- plots[[1]] + ylim(global_lims) + theme(legend.position = "none") + ggtitle("Naive-only")
plots[[2]] <- plots[[2]] + ylim(global_lims) + ylab("") + theme(legend.position = "none") + ggtitle("Primed-only")
plots[[3]] <- plots[[3]] + ylim(global_lims) + ylab("") + ggtitle("Both")
plot_grid(plotlist=plots, ncol=2)
High-med-low expressed genes
bedfiles_top <- list.files(params$genesdir, full.names = T, pattern = "_top")
bedfiles_med <- list.files(params$genesdir, full.names = T, pattern = "_med")
bedfiles_low <- list.files(params$genesdir, full.names = T, pattern = "_low")
# Ensures proper order
bedfiles <- c(bedfiles_top, bedfiles_med, bedfiles_low)
bwsignal <- bwfiles[grepl("H3K27m3.*pooled.hg38.scaled", bwfiles)]
colors <- as.character(style_info[basename(c(bwsignal, bwinput)), "color_cond"])
labels <- style_info[basename(c(bwsignal, bwinput)), "label"]
up <- 10000
dw <- 10000
bs <- 500
md <- "stretch"
global_lims <- c(0, 2.5)
this_plot <- partial(plot_bw_profile, bwfiles = c(bwsignal, bwinput),
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels,
verbose = FALSE) # Plot gets too crowded
plots <- purrr::map(bedfiles, this_plot)
plots <- remove_extra_captions(plots)
plots <- set_global_y_axis(plots, global_lims)
plots[[1]] <- plots[[1]] + ggtitle("H3K27m3 per gene class: Naive")
plots[[2]] <- plots[[2]] + ggtitle("- Primed" )
plot_grid(plotlist=plots, nrow=3)
Boxplots
bedfiles_top <- list.files(params$genesdir, full.names = T, pattern = "_top")
bedfiles_med <- list.files(params$genesdir, full.names = T, pattern = "_med")
bedfiles_low <- list.files(params$genesdir, full.names = T, pattern = "_low")
# Ensures proper order
bedfiles <- c(bedfiles_top, bedfiles_med, bedfiles_low)
bwsignal <- bwfiles[grepl("H3K27m3.*pooled.hg38.scaled", bwfiles)]
colors <- as.character(style_info[basename(c(bwsignal, bwinput)), "color_cond"])
labels <- style_info[basename(c(bwsignal, bwinput)), "label"]
# Use tss for this
tss_window <- 2500
bed_ranges <- lapply(bedfiles, import)
tss_ranges <- lapply(bed_ranges, promoters, upstream = tss_window, downstream = tss_window)
this_plot <- partial(bw_loci, bwfiles = c(bwsignal, bwinput))
#
plots <- purrr::map(tss_ranges, this_plot)
names(plots) <- basename(bedfiles)
# Pivot 1 item:
library(tidyr)
Attaching package: 'tidyr'The following object is masked from 'package:S4Vectors':
expandpivot_bed <- function(granges, loci_name) {
bin_ids <- c("seqnames", "start", "end", "width", "strand")
pivoted <- pivot_longer(data = data.frame(granges),
cols = contains("pooled.hg38"),
names_pattern = "(.*)_pooled.hg38.*",
names_to = "sample",
values_to = "value")
pivoted[["group"]] <- loci_name
pivoted
}
pivoted_list <- map2(plots, names(plots), pivot_bed)
all_vals <- do.call(rbind, pivoted_list)
ggplot(all_vals, aes(x = sample, y = value, color = sample, group = sample)) + geom_boxplot() + facet_wrap(group ~ .) + theme_default() + theme(axis.text.x = element_text(angle=45, hjust = 1)) + scale_color_manual(name = "sample", values = colors)
H3K4m3
Highly expressed genes
bedfiles <- list.files(params$genesdir, full.names = T, pattern = "top")
bwfiles <- list.files(params$bwdir, full.names = T)
bwinput <- bwfiles[grepl("IN.*pooled", bwfiles)]
bwsignal <- bwfiles[grepl("H3K4m3.*pooled.hg38.scaled", bwfiles)]
colors <- as.character(style_info[basename(c(bwsignal, bwinput)), "color_cond"])
labels <- style_info[basename(c(bwsignal, bwinput)), "label"]
up <- 10000
dw <- 10000
bs <- 500
md <- "stretch"
global_lims <- c(0, 140)
this_plot <- partial(plot_bw_profile, bwfiles = c(bwsignal, bwinput),
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels)
plots <- purrr::map(bedfiles, this_plot)
plots[[1]] <- plots[[1]] + ylim(global_lims) + theme(legend.position = "none") + ggtitle("H3K4m3 @Top genes")
plots[[2]] <- plots[[2]] + ylim(global_lims) + ylab("") + ggtitle("")
plot_grid(plotlist=plots, nrow=1)
Norm to input
colors <- as.character(style_info[basename(bwsignal), "color_cond"])
labels <- style_info[basename(bwsignal), "label"]
global_lims <- c(0, 170)
this_plot <- partial(plot_bw_profile, bwfiles = bwsignal, bg_bwfiles = bwinput,
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels)
plots <- purrr::map(bedfiles, this_plot)
plots[[1]] <- plots[[1]] + ylim(global_lims) + theme(legend.position = "none") + ggtitle("H3K4m3 @Top genes")
plots[[2]] <- plots[[2]] + ylim(global_lims) + ylab("") + ggtitle("")
plot_grid(plotlist=plots, nrow=1)
Naive-only vs primed-only
naive_only_top <- setdiff(naive_top, primed_top)
primed_only_top <- setdiff(primed_top, naive_top)
both <- intersect(naive_top, primed_top)
global_lims <- c(0, 140)
colors <- as.character(style_info[basename(c(bwsignal, bwinput)), "color_cond"])
labels <- style_info[basename(c(bwsignal, bwinput)), "label"]
this_plot <- partial(plot_bw_profile, bwfiles = c(bwsignal, bwinput),
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels)
plots <- purrr::map(list(naive_only_top, primed_only_top, both), this_plot)
plots[[1]] <- plots[[1]] + ylim(global_lims) + theme(legend.position = "none") + ggtitle("Naive-only")
plots[[2]] <- plots[[2]] + ylim(global_lims) + ylab("") + theme(legend.position = "none") + ggtitle("Primed-only")
plots[[3]] <- plots[[3]] + ylim(global_lims) + ylab("") + ggtitle("Both")
plot_grid(plotlist=plots, ncol=2)
Norm to input:
colors <- as.character(style_info[basename(bwsignal), "color_cond"])
labels <- style_info[basename(bwsignal), "label"]
global_lims <- c(0, 160)
this_plot <- partial(plot_bw_profile, bwfiles = bwsignal, bg_bwfiles = bwinput,
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels)
plots <- purrr::map(list(naive_only_top, primed_only_top, both), this_plot)
plots[[1]] <- plots[[1]] + ylim(global_lims) + theme(legend.position = "none") + ggtitle("Naive-only")
plots[[2]] <- plots[[2]] + ylim(global_lims) + ylab("") + theme(legend.position = "none") + ggtitle("Primed-only")
plots[[3]] <- plots[[3]] + ylim(global_lims) + ylab("") + ggtitle("Both")
plot_grid(plotlist=plots, ncol=2)
High-med-low expressed genes
bedfiles_top <- list.files(params$genesdir, full.names = T, pattern = "_top")
bedfiles_med <- list.files(params$genesdir, full.names = T, pattern = "_med")
bedfiles_low <- list.files(params$genesdir, full.names = T, pattern = "_low")
global_lims <- c(0, 140)
# Ensures proper order
bedfiles <- c(bedfiles_top, bedfiles_med, bedfiles_low)
bwsignal <- bwfiles[grepl("H3K4m3.*pooled.hg38.scaled", bwfiles)]
colors <- as.character(style_info[basename(c(bwsignal, bwinput)), "color_cond"])
labels <- style_info[basename(c(bwsignal, bwinput)), "label"]
up <- 10000
dw <- 10000
bs <- 500
md <- "stretch"
global_lims <- c(0, 150)
this_plot <- partial(plot_bw_profile, bwfiles = c(bwsignal, bwinput),
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels,
verbose = FALSE) # Plot gets too crowded
plots <- purrr::map(bedfiles, this_plot)
plots <- remove_extra_captions(plots)
plots <- set_global_y_axis(plots, global_lims)
plots[[1]] <- plots[[1]] + ggtitle("H3K4m3 per gene class: Naive")
plots[[2]] <- plots[[2]] + ggtitle("Primed" )
plot_grid(plotlist=plots, nrow=3)
H2AUb
High-med-low expressed genes
bedfiles_top <- list.files(params$genesdir, full.names = T, pattern = "_top")
bedfiles_med <- list.files(params$genesdir, full.names = T, pattern = "_med")
bedfiles_low <- list.files(params$genesdir, full.names = T, pattern = "_low")
# Ensures proper order
bedfiles <- c(bedfiles_top, bedfiles_med, bedfiles_low)
bwsignal <- bwfiles[grepl("H2A.*pooled.hg38.scaled", bwfiles)]
colors <- as.character(style_info[basename(c(bwsignal, bwinput)), "color_cond"])
labels <- style_info[basename(c(bwsignal, bwinput)), "label"]
up <- 10000
dw <- 10000
bs <- 500
md <- "stretch"
global_lims <- c(0, 3)
this_plot <- partial(plot_bw_profile, bwfiles = c(bwsignal, bwinput),
bin_size = bs,
mode = md,
upstream = up,
downstream = dw,
colors = colors,
labels = labels,
verbose = FALSE) # Plot gets too crowded
plots <- purrr::map(bedfiles, this_plot)
plots <- remove_extra_captions(plots)
plots <- set_global_y_axis(plots, global_lims)
plots[[1]] <- plots[[1]] + ggtitle("H2AUb per gene class: Naive")
plots[[2]] <- plots[[2]] + ggtitle("Primed" )
plot_grid(plotlist=plots, nrow=3)
sessionInfo()R version 4.0.3 (2020-10-10)
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] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] tidyr_1.1.2 cowplot_1.1.1 xfun_0.20
[4] purrr_0.3.4 rtracklayer_1.50.0 GenomicRanges_1.42.0
[7] GenomeInfoDb_1.26.2 IRanges_2.24.1 S4Vectors_0.28.1
[10] BiocGenerics_0.36.0 ggvenn_0.1.8 dplyr_1.0.4
[13] knitr_1.31 ggplot2_3.3.3 wigglescout_0.12.8
[16] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] MatrixGenerics_1.2.0 Biobase_2.50.0
[3] assertthat_0.2.1 highr_0.8
[5] GenomeInfoDbData_1.2.4 Rsamtools_2.6.0
[7] yaml_2.2.1 globals_0.14.0
[9] pillar_1.4.7 lattice_0.20-41
[11] glue_1.4.2 digest_0.6.27
[13] RColorBrewer_1.1-2 promises_1.1.1
[15] XVector_0.30.0 colorspace_2.0-0
[17] htmltools_0.5.1.1 httpuv_1.5.5
[19] Matrix_1.3-2 plyr_1.8.6
[21] XML_3.99-0.5 pkgconfig_2.0.3
[23] listenv_0.8.0 zlibbioc_1.36.0
[25] scales_1.1.1 whisker_0.4
[27] later_1.1.0.1 BiocParallel_1.24.1
[29] git2r_0.28.0 tibble_3.0.6
[31] generics_0.1.0 farver_2.0.3
[33] ellipsis_0.3.1 withr_2.4.1
[35] SummarizedExperiment_1.20.0 furrr_0.2.2
[37] magrittr_2.0.1 crayon_1.4.0
[39] evaluate_0.14 fs_1.5.0
[41] future_1.21.0 parallelly_1.23.0
[43] tools_4.0.3 lifecycle_0.2.0
[45] matrixStats_0.58.0 stringr_1.4.0
[47] munsell_0.5.0 DelayedArray_0.16.0
[49] Biostrings_2.58.0 compiler_4.0.3
[51] rlang_0.4.10 RCurl_1.98-1.2
[53] bitops_1.0-6 labeling_0.4.2
[55] rmarkdown_2.6 gtable_0.3.0
[57] codetools_0.2-18 DBI_1.1.1
[59] reshape2_1.4.4 R6_2.5.0
[61] GenomicAlignments_1.26.0 rprojroot_2.0.2
[63] stringi_1.5.3 Rcpp_1.0.6
[65] vctrs_0.3.6 tidyselect_1.1.0