Figure 1. Quantitative ChIP
Carmen Navarro
2021-03-12
Last updated: 2021-04-19
Checks: 6 1
Knit directory: hesc-epigenomics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | a9d0a78 | cnluzon | 2021-04-13 | Build site. |
| Rmd | dcfe4a4 | cnluzon | 2021-04-13 | wflow_publish(“./analysis/fig_01_quantitative_chip.Rmd”) |
| html | 054d3f5 | cnluzon | 2021-03-22 | Build site. |
| Rmd | 07bb6d9 | cnluzon | 2021-03-22 | H9 ChromHMM annotation |
| html | a6a00b6 | cnluzon | 2021-03-12 | Fig1 with ttest |
| Rmd | 8f01ba6 | cnluzon | 2021-03-12 | Stat tests in global counts |
| Rmd | 0f37941 | cnluzon | 2021-03-12 | Fig1 fresh |
| html | 0f37941 | cnluzon | 2021-03-12 | Fig1 fresh |
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 = 16) {
inrc <- inrc[inrc$ip == ip, ]
# So paired test takes right replicates
inrc <- inrc[order(inrc$condition, inrc$replicate), ]
max_v <- max(abs(inrc$norm_to_naive))
aesthetics <- aes(x = .data[["condition"]],
y = .data[["norm_to_naive"]],
color = .data[["condition"]])
my_comp <- list(c("Naive_Untreated", "Primed_Untreated"),
c("Naive_Untreated", "Naive_EZH2i"),
c("Primed_Untreated", "Primed_EZH2i"),
c("Naive_EZH2i", "Primed_EZH2i"))
stats_method <- "t.test"
ggplot(inrc[inrc$replicate != 'pooled',], aesthetics) +
geom_point() +
stat_compare_means(
method = stats_method,
paired = TRUE,
comparisons = my_comp,
label = "p.format"
) +
geom_bar(
data = inrc[inrc$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"),
caption = paste(stats_method, "signif. test, paired")
) +
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"="#82c5c6",
"Naive_Untreated"="#278b8b",
"Primed_EZH2i"="#f49797",
"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.
sessionInfo()R version 4.0.5 (2021-03-31)
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] svglite_2.0.0 knitr_1.31 ggpubr_0.4.0.999 dplyr_1.0.5
[5] reshape2_1.4.4 ggplot2_3.3.3 wigglescout_0.12.8 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] bitops_1.0-6 matrixStats_0.58.0
[3] fs_1.5.0 RColorBrewer_1.1-2
[5] rprojroot_2.0.2 GenomeInfoDb_1.26.2
[7] tools_4.0.5 backports_1.2.1
[9] bslib_0.2.4 utf8_1.2.1
[11] R6_2.5.0 DBI_1.1.1
[13] BiocGenerics_0.36.0 colorspace_2.0-0
[15] withr_2.4.1 tidyselect_1.1.0
[17] curl_4.3 compiler_4.0.5
[19] git2r_0.28.0 Biobase_2.50.0
[21] DelayedArray_0.16.0 labeling_0.4.2
[23] rtracklayer_1.50.0 sass_0.3.1
[25] scales_1.1.1 askpass_1.1
[27] systemfonts_1.0.1 stringr_1.4.0
[29] digest_0.6.27 Rsamtools_2.6.0
[31] foreign_0.8-81 rmarkdown_2.7
[33] rio_0.5.26 XVector_0.30.0
[35] pkgconfig_2.0.3 htmltools_0.5.1.1
[37] parallelly_1.24.0 MatrixGenerics_1.2.0
[39] highr_0.8 readxl_1.3.1
[41] rlang_0.4.10 farver_2.1.0
[43] jquerylib_0.1.3 generics_0.1.0
[45] jsonlite_1.7.2 BiocParallel_1.24.1
[47] zip_2.1.1 car_3.0-10
[49] RCurl_1.98-1.3 magrittr_2.0.1
[51] GenomeInfoDbData_1.2.4 Matrix_1.3-2
[53] Rcpp_1.0.6 munsell_0.5.0
[55] S4Vectors_0.28.1 fansi_0.4.2
[57] abind_1.4-5 lifecycle_1.0.0
[59] furrr_0.2.2 stringi_1.5.3
[61] whisker_0.4 yaml_2.2.1
[63] carData_3.0-4 SummarizedExperiment_1.20.0
[65] zlibbioc_1.36.0 plyr_1.8.6
[67] grid_4.0.5 parallel_4.0.5
[69] listenv_0.8.0 promises_1.2.0.1
[71] forcats_0.5.1 crayon_1.4.1
[73] lattice_0.20-41 Biostrings_2.58.0
[75] haven_2.3.1 hms_1.0.0
[77] pillar_1.6.0 GenomicRanges_1.42.0
[79] ggsignif_0.6.1 codetools_0.2-18
[81] stats4_4.0.5 XML_3.99-0.6
[83] glue_1.4.2 evaluate_0.14
[85] data.table_1.14.0 vctrs_0.3.7
[87] httpuv_1.5.5 cellranger_1.1.0
[89] openssl_1.4.3 gtable_0.3.0
[91] purrr_0.3.4 tidyr_1.1.3
[93] future_1.21.0 assertthat_0.2.1
[95] openxlsx_4.2.3 xfun_0.22
[97] broom_0.7.6 rstatix_0.7.0
[99] later_1.1.0.1 tibble_3.1.0
[101] GenomicAlignments_1.26.0 IRanges_2.24.1
[103] globals_0.14.0 ellipsis_0.3.1