Last updated: 2021-09-10

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Knit directory: hesc-epigenomics/

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This notebook shows how the master bins table is generated. Essentially, hg38 human genome is partitioned into 10000 bp windows and their mean coverage value is calculated for H3K4m3, H3K27m3 and H2AUb. DeSeq2 is applied in a Minute-ChIP specific manner and bins are annotated as differential across conditions:

  • Primed vs Naïve,
  • EZH2i treated Naïve vs Naïve and
  • EZH2i treated Primed vs Primed.

Additionally, also a cross-comparison between H2Aub and H3K27m3 marks is done, both for Naïve state and Primed state.

Final table includes these values, fold-change differences and statistical significance scores for all genes.

Helper functions

calculate_results <- function(diff, shrink, pval, coef) {
  diff_lfc <- NULL
  if (shrink == TRUE) {
    diff_lfc <- lfcShrink(diff, coef=coef, type="apeglm")
  } else {
    diff_lfc <- results(diff, alpha = pval)
  }
  diff_lfc
}

make_diff_df <- function(diff_lfc, loci, prefix) {
  df_diff <- data.frame(diff_lfc)
  # DS stands for DeSeq
  colnames(df_diff) <- paste(prefix, colnames(df_diff), sep = "_")

  cbind(data.frame(loci)[, c("seqnames", "start", "end", "strand")], df_diff)
}

make_label <- function(fnames) {
  labs <- gsub("_pooled.hg38.*scaled.bw", "", basename(fnames))
  # Remove the uncomfortable . in EZH2i elements
  labs <- gsub("-", "_", labs)
  labs <- gsub("H9_", "", labs)
  paste(labs, "mean_cov", sep = "_")
}


make_label2 <- function(fnames) {
  labs <- gsub(".hg38.*scaled.bw", "", basename(fnames))
  # Remove the uncomfortable . in EZH2i elements
  labs <- gsub("-", "_", labs)
  labs <- gsub("H9_", "", labs)
  paste(labs, "mean_cov", sep = "_")
}


gr_cbind <- function(lociset) {
  dfs <- lapply(lociset, data.frame)
  values <- dfs %>% reduce(left_join, by=c("seqnames", "start", "end", "strand", "width"))
  values
}

diff_analysis <- function(bwfiles_c1, bwfiles_c2, gr, name_c1, name_c2, shrink, pval) {
  c1 <- bw_loci(bwfiles_c1, gr)
  c2 <- bw_loci(bwfiles_c2, gr)

  diff <- bw_granges_diff_analysis(c1, c2, name_c1, name_c2,
                                   estimate_size_factors = FALSE)
  
  coef <- paste0("condition_", name_c2, "_vs_", name_c1)
  diff_lfc <- calculate_results(diff, shrink, pval, coef)
  diff_lfc
}

Config analysis

get_bw_files <- function(pattern) {
  bwdir <- file.path(params$datadir, "bw/Kumar_2020")
  list.files(bwdir, pattern = pattern, full.names = T)
}

bins_gr <- build_bins(bin_size = params$bin_size, genome = "hg38")

# Subsample for testing
# bins_gr <- sort(bins_gr[sample(1:length(bins_gr), 2000), ])

bwfiles <-
  list(
    k4_naive = get_bw_files("H3K4m3_H9_Ni_rep[1-3].hg38.scaled.bw"),
    k4_naive_ezh2i = get_bw_files("H3K4m3_H9_Ni-EZH2i_rep[1-3].hg38.scaled.bw"),
    k4_primed = get_bw_files("H3K4m3_H9_Pr_rep[1-3].hg38.scaled.bw"),
    k4_primed_ezh2i = get_bw_files("H3K4m3_H9_Pr-EZH2i_rep[1-3].hg38.scaled.bw"),
    k27_naive = get_bw_files("H3K27m3_H9_Ni_rep[1-3].hg38.scaled.bw"),
    k27_primed = get_bw_files("H3K27m3_H9_Pr_rep[1-3].hg38.scaled.bw"),
    ub_naive = get_bw_files("H2Aub_H9_Ni_rep[1-3].hg38.scaled.bw"),
    ub_naive_ezh2i = get_bw_files("H2Aub_H9_Ni-EZH2i_rep[1-3].hg38.scaled.bw"),
    ub_primed = get_bw_files("H2Aub_H9_Pr_rep[1-3].hg38.scaled.bw"),
    ub_primed_ezh2i = get_bw_files("H2Aub_H9_Pr-EZH2i_rep[1-3].hg38.scaled.bw"),
    in_naive = get_bw_files("IN_H9_Ni.*rep[1-3].hg38.*.bw"),
    in_naive_ezh2i = get_bw_files("IN_H9_Ni-EZH2i.*rep[1-3].hg38.*.bw"),
    in_primed = get_bw_files("IN_H9_Pr_rep[1-3].hg38.*.bw"),
    in_primed_ezh2i = get_bw_files("IN_H9_Pr-EZH2i.*rep[1-3].hg38.*.bw")
  )

bwfiles_pooled <-
  list(
    k4 = get_bw_files("H3K4m3.*pooled.hg38.scaled.*"),
    k27 = get_bw_files("H3K27m3.*pooled.hg38.scaled.*"),
    ub = get_bw_files("H2Aub.*pooled.hg38.scaled.*"),
    input = get_bw_files("IN.*pooled.hg38.*")
  )

Raw pooled values at bins

pooled_k4 <- bw_loci(bwfiles_pooled$k4, bins_gr, labels = make_label(bwfiles_pooled$k4))
pooled_k27 <- bw_loci(bwfiles_pooled$k27, bins_gr, labels = make_label(bwfiles_pooled$k27))
pooled_h2aub <- bw_loci(bwfiles_pooled$ub, bins_gr, labels = make_label(bwfiles_pooled$ub))
pooled_inp <- bw_loci(bwfiles_pooled$input, bins_gr, labels = make_label(bwfiles_pooled$input))

pooled_df <- gr_cbind(list(pooled_k4, pooled_k27, pooled_h2aub, pooled_inp))

master_df <- pooled_df

Raw replicates values at bins

reps_df <- bw_loci(unname(unlist(bwfiles)), loci = bins_gr, labels = make_label2(unname(unlist(bwfiles))))
# In case we want the replicates as well in the master table
master_df <- gr_cbind(list(pooled_k4, pooled_k27, pooled_h2aub, pooled_inp, reps_df))

K27m3 diff analysis

Primed vs Naive

diff_lfc <- diff_analysis(
  bwfiles$k27_naive,
  bwfiles$k27_primed,
  bins_gr,
  "Naive",
  "Primed",
  params$shrink,
  params$pval_cutoff
)

plotMA(diff_lfc)

Version Author Date
92b99ca C. Navarro 2021-07-02
df_diff <- make_diff_df(diff_lfc, bins_gr, "H3K27m3_DS_Pr_vs_Ni")

master_df <- left_join(master_df, df_diff,
                       by = c("seqnames", "start", "end", "strand"))

EZH2i vs Naive and primed

These are skipped, as EZH2i treatment wipes all H3K27me3 so it does not make any sense to do the differential analysis in this context.

H3K4m3 diff analysis

Primed vs Naive

diff_lfc <- diff_analysis(
  bwfiles$k4_naive,
  bwfiles$k4_primed,
  bins_gr,
  "Naive",
  "Primed",
  params$shrink,
  params$pval_cutoff
)

plotMA(diff_lfc)

Version Author Date
92b99ca C. Navarro 2021-07-02
df_diff <- make_diff_df(diff_lfc, bins_gr, "H3K4m3_DS_Pr_vs_Ni")

master_df <- left_join(master_df, df_diff,
                       by = c("seqnames", "start", "end", "strand"))

EZH2i vs Naive

diff_lfc <- diff_analysis(
  bwfiles$k4_naive,
  bwfiles$k4_naive_ezh2i,
  bins_gr,
  "Naive",
  "EZH2i",
  params$shrink,
  params$pval_cutoff
)

plotMA(diff_lfc)

Version Author Date
92b99ca C. Navarro 2021-07-02
df_diff <- make_diff_df(diff_lfc, bins_gr, "H3K4m3_DS_EZH2i_vs_Ni")

master_df <- left_join(master_df, df_diff,
                       by = c("seqnames", "start", "end", "strand"))

EZH2i vs Primed

diff_lfc <- diff_analysis(
  bwfiles$k4_primed,
  bwfiles$k4_primed_ezh2i,
  bins_gr,
  "Primed",
  "EZH2i",
  params$shrink,
  params$pval_cutoff
)

plotMA(diff_lfc)

Version Author Date
92b99ca C. Navarro 2021-07-02
df_diff <- make_diff_df(diff_lfc, bins_gr, "H3K4m3_DS_EZH2i_vs_Pr")

master_df <- left_join(master_df, df_diff,
                       by = c("seqnames", "start", "end", "strand"))

H2AUb diff analysis

Primed vs Naive

diff_lfc <- diff_analysis(
  bwfiles$ub_naive,
  bwfiles$ub_primed,
  bins_gr,
  "Naive",
  "Primed",
  params$shrink,
  params$pval_cutoff
)

plotMA(diff_lfc)

Version Author Date
92b99ca C. Navarro 2021-07-02
df_diff <- make_diff_df(diff_lfc, bins_gr, "H2Aub_DS_Pr_vs_Ni")

master_df <- left_join(master_df, df_diff,
                       by = c("seqnames", "start", "end", "strand"))

EZH2i vs Naive

diff_lfc <- diff_analysis(
  bwfiles$ub_naive,
  bwfiles$ub_naive_ezh2i,
  bins_gr,
  "Naive",
  "EZH2i",
  params$shrink,
  params$pval_cutoff
)

plotMA(diff_lfc)

Version Author Date
92b99ca C. Navarro 2021-07-02
df_diff <- make_diff_df(diff_lfc, bins_gr, "H2Aub_DS_EZH2i_vs_Ni")

master_df <- left_join(master_df, df_diff,
                       by = c("seqnames", "start", "end", "strand"))

EZH2i vs Primed

diff_lfc <- diff_analysis(
  bwfiles$ub_primed,
  bwfiles$ub_primed_ezh2i,
  bins_gr,
  "Primed",
  "EZH2i",
  params$shrink,
  params$pval_cutoff
)

plotMA(diff_lfc)

Version Author Date
92b99ca C. Navarro 2021-07-02
df_diff <- make_diff_df(diff_lfc, bins_gr, "H2Aub_DS_EZH2i_vs_Pr")

master_df <- left_join(master_df, df_diff,
                       by = c("seqnames", "start", "end", "strand"))

H3K27m3 vs H2AUb diff

Naïve

diff_lfc <- diff_analysis(
  bwfiles$ub_naive,
  bwfiles$k27_naive,
  bins_gr,
  "H2Aub_Ni",
  "H3K27m3_Ni",
  params$shrink,
  params$pval_cutoff
)

plotMA(diff_lfc)

Version Author Date
92b99ca C. Navarro 2021-07-02
df_diff <- make_diff_df(diff_lfc, bins_gr, "DS_H3K27m3_Ni_vs_H2Aub_Ni")

master_df <- left_join(master_df, df_diff,
                       by = c("seqnames", "start", "end", "strand"))

Primed

diff_lfc <- diff_analysis(
  bwfiles$ub_primed,
  bwfiles$k27_primed,
  bins_gr,
  "H2Aub_Pr",
  "H3K27m3_Pr",
  params$shrink,
  params$pval_cutoff
)

plotMA(diff_lfc)

Version Author Date
92b99ca C. Navarro 2021-07-02
df_diff <- make_diff_df(diff_lfc, bins_gr, "DS_H3K27m3_Pr_vs_H2Aub_Pr")

master_df <- left_join(master_df, df_diff,
                       by = c("seqnames", "start", "end", "strand"))

Final table

write.table(
  format(master_df, digits = 4),
  file = table_file, 
  sep = "\t",
  col.names = T,
  quote = F,
  row.names = F
)

sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 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] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] biomaRt_2.48.3                          
 [2] DESeq2_1.32.0                           
 [3] SummarizedExperiment_1.22.0             
 [4] MatrixGenerics_1.4.0                    
 [5] matrixStats_0.60.1                      
 [6] tidyr_1.1.3                             
 [7] cowplot_1.1.1                           
 [8] xfun_0.24                               
 [9] dplyr_1.0.7                             
[10] purrr_0.3.4                             
[11] rtracklayer_1.52.0                      
[12] org.Hs.eg.db_3.13.0                     
[13] TxDb.Hsapiens.UCSC.hg38.knownGene_3.13.0
[14] GenomicFeatures_1.44.0                  
[15] AnnotationDbi_1.54.1                    
[16] Biobase_2.52.0                          
[17] GenomicRanges_1.44.0                    
[18] GenomeInfoDb_1.28.1                     
[19] IRanges_2.26.0                          
[20] S4Vectors_0.30.0                        
[21] BiocGenerics_0.38.0                     
[22] knitr_1.33                              
[23] ggplot2_3.3.5                           
[24] wigglescout_0.13.1                      
[25] workflowr_1.6.2                         

loaded via a namespace (and not attached):
 [1] colorspace_2.0-2         rjson_0.2.20             ellipsis_0.3.2          
 [4] rprojroot_2.0.2          XVector_0.32.0           fs_1.5.0                
 [7] listenv_0.8.0            furrr_0.2.3              bit64_4.0.5             
[10] fansi_0.5.0              xml2_1.3.2               splines_4.1.1           
[13] codetools_0.2-18         cachem_1.0.6             geneplotter_1.70.0      
[16] jsonlite_1.7.2           Rsamtools_2.8.0          annotate_1.70.0         
[19] dbplyr_2.1.1             png_0.1-7                compiler_4.1.1          
[22] httr_1.4.2               assertthat_0.2.1         Matrix_1.3-4            
[25] fastmap_1.1.0            later_1.3.0              htmltools_0.5.2         
[28] prettyunits_1.1.1        tools_4.1.1              gtable_0.3.0            
[31] glue_1.4.2               GenomeInfoDbData_1.2.6   reshape2_1.4.4          
[34] rappdirs_0.3.3           Rcpp_1.0.7               jquerylib_0.1.4         
[37] vctrs_0.3.8              Biostrings_2.60.2        stringr_1.4.0           
[40] globals_0.14.0           lifecycle_1.0.0          restfulr_0.0.13         
[43] XML_3.99-0.7             future_1.21.0            zlibbioc_1.38.0         
[46] scales_1.1.1             hms_1.1.0                promises_1.2.0.1        
[49] RColorBrewer_1.1-2       yaml_2.2.1               curl_4.3.2              
[52] memoise_2.0.0            sass_0.4.0               stringi_1.7.4           
[55] RSQLite_2.2.8            highr_0.9                genefilter_1.74.0       
[58] BiocIO_1.2.0             filelock_1.0.2           BiocParallel_1.26.0     
[61] rlang_0.4.11             pkgconfig_2.0.3          bitops_1.0-7            
[64] evaluate_0.14            lattice_0.20-44          GenomicAlignments_1.28.0
[67] bit_4.0.4                tidyselect_1.1.1         parallelly_1.26.1       
[70] plyr_1.8.6               magrittr_2.0.1           R6_2.5.1                
[73] generics_0.1.0           DelayedArray_0.18.0      DBI_1.1.1               
[76] pillar_1.6.2             whisker_0.4              withr_2.4.2             
[79] survival_3.2-13          KEGGREST_1.32.0          RCurl_1.98-1.4          
[82] tibble_3.1.4             crayon_1.4.1             utf8_1.2.2              
[85] BiocFileCache_2.0.0      rmarkdown_2.9            progress_1.2.2          
[88] locfit_1.5-9.4           grid_4.1.1               blob_1.2.2              
[91] git2r_0.28.0             digest_0.6.27            xtable_1.8-4            
[94] httpuv_1.6.2             munsell_0.5.0            bslib_0.2.5.1