hESC data global read counts
Carmen Navarro
2021-02-03
Last updated: 2021-02-03
Checks: 7 0
Knit directory: hesc-epigenomics/
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Summary
This is a report on global read counts for Hu2 data.
Pipeline version: https://github.com/NBISweden/minute/tree/8c7646949abae12ef5bd9295f20fa6eeb7182533 .
Mapping stats summary: data/meta/Kumar_2020_stats_summary.csv, data/meta/Kumar_2020_scalinginfo.csv.
INRC stands for Input Normalized Read Counts. Each sample number of mapped reads is divided by the corresponding number of reads in the Input. This can be done because of the multiplexed nature of MINUTE-ChIP protocol.
In the following plots, the value shown is the log2 enrichment over the reference INRC value per sample, which is the matched untreated Naïve sample (pooled). Each dot represents one replicate.
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) {
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=params$fontsize) +
theme(axis.text.x = element_text(angle=45, hjust=1)) + ylim(-max_v, max_v)
}
colors_list <- c("Naive_EZH2i"="#5F9EA0",
"Naive_Untreated"="#278b8b",
"Primed_EZH2i"="#f47770",
"Primed_Untreated"="#f44b34")Global read counts levels
H2AUb levels
counts_file <- file.path(params$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)
H3K27m3 levels
inrc_barplot(inrc, "H3K27m3", colors_list)
H3K4m3 levels
inrc_barplot(inrc, "H3K4m3", colors_list)
H3 levels
inrc_barplot(inrc, "H3", colors_list)
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 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] dplyr_1.0.3 reshape2_1.4.4 ggplot2_3.3.3 wigglescout_0.12.8
[5] 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_4.0.3 evaluate_0.14
[15] pillar_1.4.7 zlibbioc_1.36.0
[17] rlang_0.4.10 rstudioapi_0.13
[19] whisker_0.4 furrr_0.2.1
[21] S4Vectors_0.28.1 Matrix_1.3-2
[23] rmarkdown_2.6 labeling_0.4.2
[25] BiocParallel_1.24.1 stringr_1.4.0
[27] RCurl_1.98-1.2 munsell_0.5.0
[29] DelayedArray_0.16.0 compiler_4.0.3
[31] httpuv_1.5.4 rtracklayer_1.50.0
[33] xfun_0.20 pkgconfig_2.0.3
[35] BiocGenerics_0.36.0 globals_0.14.0
[37] htmltools_0.5.1 SummarizedExperiment_1.20.0
[39] tidyselect_1.1.0 tibble_3.0.5
[41] GenomeInfoDbData_1.2.4 matrixStats_0.57.0
[43] IRanges_2.24.1 codetools_0.2-18
[45] XML_3.99-0.5 future_1.21.0
[47] withr_2.4.0 crayon_1.3.4
[49] later_1.1.0.1 GenomicAlignments_1.26.0
[51] bitops_1.0-6 grid_4.0.3
[53] gtable_0.3.0 lifecycle_0.2.0
[55] DBI_1.1.0 git2r_0.27.1
[57] magrittr_2.0.1 scales_1.1.1
[59] stringi_1.5.3 farver_2.0.3
[61] XVector_0.30.0 fs_1.5.0
[63] promises_1.1.1 ellipsis_0.3.1
[65] generics_0.1.0 vctrs_0.3.6
[67] RColorBrewer_1.1-2 tools_4.0.3
[69] Biobase_2.50.0 glue_1.4.2
[71] purrr_0.3.4 MatrixGenerics_1.2.0
[73] parallel_4.0.3 yaml_2.2.1
[75] colorspace_2.0-0 GenomicRanges_1.42.0
[77] knitr_1.30