Last updated: 2021-04-19

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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 = 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)

Version Author Date
a9d0a78 cnluzon 2021-04-13
a6a00b6 cnluzon 2021-03-12
0f37941 cnluzon 2021-03-12

You can download data values here: download plot data.

H3K27m3 levels

inrc_barplot(inrc, "H3K27m3", colors_list)

Version Author Date
a9d0a78 cnluzon 2021-04-13
a6a00b6 cnluzon 2021-03-12
0f37941 cnluzon 2021-03-12

You can download data values here: download plot data.

H3K4m3 levels

inrc_barplot(inrc, "H3K4m3", colors_list)

Version Author Date
a9d0a78 cnluzon 2021-04-13
a6a00b6 cnluzon 2021-03-12
0f37941 cnluzon 2021-03-12

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