Figure 1. Quantitative ChIP
Carmen Navarro
2021-03-12
Last updated: 2021-03-12
Checks: 6 1
Knit directory: hesc-epigenomics/
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Summary
This notebook includes the figures related to the quantitative side of ChIP:
- Global Read count levels.
- ChromHMM descriptive plots per mark (+ EZH2i?)
Supplementary possibles:
- Global ChIP comparison bin-based to other datasets, correlations.
Helper functions
#' Calculate INRC from a mapped read counts table, and append such values
#' to it.
#'
#' @param counts Counts table. Corresponding file is provided as part of the
#' included metadata.
#' @param selector Counts column used. Final_mapped represents the final number
#' of reads after deduplication and blacklisting.
#' @return A table including INRC and INRC norm to naive reference
calculate_inrc <- function(counts, selector = "final_mapped") {
counts$condition <- paste(counts$celltype, counts$treatment, sep="_")
inputs <- counts[counts$ip == "Input", c("library", selector)]
colnames(inputs) <- c("library", "input_reads")
non_inputs <- counts[counts$ip != "Input",]
counts <- merge(non_inputs, inputs, by.x="input", by.y="library")
counts$inrc <- counts[, selector] / counts[, "input_reads"]
references <- counts[grepl("_Ni_pooled", counts$library), c("ip", "inrc")]
colnames(references) <- c("ip", "ref_inrc")
counts <- merge(counts, references, by="ip")
counts$norm_to_naive <- log2(counts$inrc / counts$ref_inrc)
id_vars <- c("ip", "treatment", "celltype", "condition", "replicate", "norm_to_naive")
inrc <- counts[, c(id_vars)]
inrc$condition <- factor(inrc$condition, levels = c("Naive_Untreated", "Primed_Untreated", "Naive_EZH2i", "Primed_EZH2i"))
inrc
}
#' Barplot INRC pooled vs replicates per condition
#'
#' @param inrc Table with the INRC values
#' @param ip Which IP to plot
#' @param colors Corresponding colors
inrc_barplot <- function(inrc, ip, colors, font = 12) {
inrc <- inrc[inrc$ip == ip, ]
max_v <- max(abs(inrc$norm_to_naive))
aesthetics <- aes(x = .data[["condition"]],
y = .data[["norm_to_naive"]],
color = .data[["condition"]])
ggplot(inrc[inrc$replicate!='pooled',], aesthetics) +
geom_point() +
geom_bar(data=inrc[inrc$replicate=='pooled',],
stat='identity', alpha=0.6, aes(fill=condition)) +
scale_fill_manual(values = colors) +
scale_color_manual(values = colors) +
labs(x = "", y = 'log2(INRC vs Naïve)', title = paste(ip, "MINUTE-ChIP")) +
theme_classic(base_size=font) +
theme(axis.text.x = element_text(angle=45, hjust=1)) + ylim(-max_v, max_v)
}
colors_list <- c("Naive_EZH2i"="#5F9EA0",
"Naive_Untreated"="#278b8b",
"Primed_EZH2i"="#f47770",
"Primed_Untreated"="#f44b34")Global read counts
H2AUb levels
counts_file <- file.path(params$datadir, "meta", "Kumar_2020_stats_summary.csv")
counts <- read.table(counts_file, sep="\t", header = T, na.strings = "NA", stringsAsFactors = F)
inrc <- calculate_inrc(counts)
ip <- "H2Aub"
inrc_barplot(inrc, "H2Aub", colors_list)
You can download data values here: download plot data.
H3K27m3 levels
inrc_barplot(inrc, "H3K27m3", colors_list)
You can download data values here: download plot data.
H3K4m3 levels
inrc_barplot(inrc, "H3K4m3", colors_list)
You can download data values here: download plot data.
ChromHMM global
Here global average per ChromHMM categories are shown.
H3K27m3
bwfiles <- list.files(file.path(params$datadir, "bw/Kumar_2020"), pattern = "H3K27m3.*pooled.*hg38.scaled", full.names = T)
labels <- gsub("_pooled.hg38.scaled.bw", "", basename(bwfiles))
labels <- gsub("_H9", "", labels)
chromhmm <- params$chromhmm
plot_bw_loci_summary_heatmap(bwfiles, chromhmm, labels = labels, remove_top=0.001)
H3K4m3
bwfiles <- list.files(file.path(params$datadir, "bw/Kumar_2020"), pattern = "H3K4m3.*pooled.*hg38.scaled", full.names = T)
labels <- gsub("_pooled.hg38.scaled.bw", "", basename(bwfiles))
labels <- gsub("_H9", "", labels)
plot_bw_loci_summary_heatmap(bwfiles, chromhmm, labels = labels, remove_top=0.001)
H2AUb
bwfiles <- list.files(file.path(params$datadir, "bw/Kumar_2020"), pattern = "H2Aub.*pooled.*hg38.scaled", full.names = T)
labels <- gsub("_pooled.hg38.scaled.bw", "", basename(bwfiles))
labels <- gsub("_H9", "", labels)
chromhmm <- params$chromhmm
plot_bw_loci_summary_heatmap(bwfiles, chromhmm, labels = labels, remove_top=0.001)
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] purrr_0.3.4 knitr_1.31 dplyr_1.0.4 reshape2_1.4.4
[5] ggplot2_3.3.3 wigglescout_0.12.8 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 lattice_0.20-41
[3] listenv_0.8.0 Rsamtools_2.6.0
[5] Biostrings_2.58.0 assertthat_0.2.1
[7] rprojroot_2.0.2 digest_0.6.27
[9] parallelly_1.23.0 R6_2.5.0
[11] GenomeInfoDb_1.26.2 plyr_1.8.6
[13] stats4_4.0.4 evaluate_0.14
[15] highr_0.8 pillar_1.4.7
[17] zlibbioc_1.36.0 rlang_0.4.10
[19] furrr_0.2.2 S4Vectors_0.28.1
[21] Matrix_1.3-2 rmarkdown_2.6
[23] labeling_0.4.2 BiocParallel_1.24.1
[25] stringr_1.4.0 RCurl_1.98-1.2
[27] munsell_0.5.0 DelayedArray_0.16.0
[29] compiler_4.0.4 httpuv_1.5.5
[31] rtracklayer_1.50.0 xfun_0.21
[33] askpass_1.1 pkgconfig_2.0.3
[35] BiocGenerics_0.36.0 globals_0.14.0
[37] htmltools_0.5.1.1 openssl_1.4.3
[39] tidyselect_1.1.0 SummarizedExperiment_1.20.0
[41] tibble_3.0.6 GenomeInfoDbData_1.2.4
[43] IRanges_2.24.1 codetools_0.2-18
[45] matrixStats_0.58.0 XML_3.99-0.5
[47] future_1.21.0 withr_2.4.1
[49] crayon_1.4.1 later_1.1.0.1
[51] GenomicAlignments_1.26.0 bitops_1.0-6
[53] grid_4.0.4 gtable_0.3.0
[55] lifecycle_1.0.0 DBI_1.1.1
[57] git2r_0.28.0 magrittr_2.0.1
[59] scales_1.1.1 stringi_1.5.3
[61] farver_2.0.3 XVector_0.30.0
[63] fs_1.5.0 promises_1.2.0.1
[65] ellipsis_0.3.1 generics_0.1.0
[67] vctrs_0.3.6 RColorBrewer_1.1-2
[69] tools_4.0.4 Biobase_2.50.0
[71] glue_1.4.2 MatrixGenerics_1.2.0
[73] parallel_4.0.4 yaml_2.2.1
[75] colorspace_2.0-0 GenomicRanges_1.42.0