Master bins table
Carmen Navarro
2021-05-26
Last updated: 2021-07-08
Checks: 7 0
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 = "_")
# df_diff$name <- rownames(df_diff)
cbind(data.frame(loci)[, c("seqnames", "start", "end", "strand")], df_diff)
}
make_label <- function(fnames) {
labs <- gsub("_pooled.hg38.*scaled.bw", "", basename(fnames))
# Remove the uncomfortable . in EZH2i elements
labs <- gsub("-", "_", labs)
labs <- gsub("H9_", "", labs)
paste(labs, "mean_cov", sep = "_")
}
make_label2 <- function(fnames) {
labs <- gsub(".hg38.*scaled.bw", "", basename(fnames))
# Remove the uncomfortable . in EZH2i elements
labs <- gsub("-", "_", labs)
labs <- gsub("H9_", "", labs)
paste(labs, "mean_cov", sep = "_")
}
gr_cbind <- function(lociset) {
dfs <- lapply(lociset, data.frame)
values <- dfs %>% reduce(left_join, by=c("seqnames", "start", "end", "strand", "width"))
values
}
diff_analysis <- function(bwfiles_c1, bwfiles_c2, gr, name_c1, name_c2, shrink, pval) {
c1 <- bw_loci(bwfiles_c1, gr)
c2 <- bw_loci(bwfiles_c2, gr)
diff <- bw_granges_diff_analysis(c1, c2, name_c1, name_c2,
estimate_size_factors = FALSE)
coef <- paste0("condition_", name_c2, "_vs_", name_c1)
diff_lfc <- calculate_results(diff, shrink, pval, coef)
diff_lfc
}Config analysis
get_bw_files <- function(pattern) {
bwdir <- file.path(params$datadir, "bw/Kumar_2020")
list.files(bwdir, pattern = pattern, full.names = T)
}
bivalent_gr <- import(file.path(params$datadir, "bed/Bivalent_Court2017.hg38.bed"))
bins_gr <- build_bins(bin_size = params$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_dfRaw 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 4.1.0 (2021-05-18)
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] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] biomaRt_2.48.2
[2] DESeq2_1.32.0
[3] SummarizedExperiment_1.22.0
[4] MatrixGenerics_1.4.0
[5] matrixStats_0.59.0
[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_4.1.0
[13] codetools_0.2-18 cachem_1.0.5 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_4.1.0
[22] httr_1.4.2 assertthat_0.2.1 Matrix_1.3-4
[25] fastmap_1.1.0 later_1.2.0 htmltools_0.5.1.1
[28] prettyunits_1.1.1 tools_4.1.0 gtable_0.3.0
[31] glue_1.4.2 GenomeInfoDbData_1.2.6 reshape2_1.4.4
[34] rappdirs_0.3.3 Rcpp_1.0.6 jquerylib_0.1.4
[37] vctrs_0.3.8 Biostrings_2.60.1 stringr_1.4.0
[40] globals_0.14.0 lifecycle_1.0.0 restfulr_0.0.13
[43] XML_3.99-0.6 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_1.6.2
[55] RSQLite_2.2.7 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_2.5.0
[73] generics_0.1.0 DelayedArray_0.18.0 DBI_1.1.1
[76] pillar_1.6.1 whisker_0.4 withr_2.4.2
[79] survival_3.2-11 KEGGREST_1.32.0 RCurl_1.98-1.3
[82] tibble_3.1.2 crayon_1.4.1 utf8_1.2.1
[85] BiocFileCache_2.0.0 rmarkdown_2.9 progress_1.2.2
[88] locfit_1.5-9.4 grid_4.1.0 blob_1.2.1
[91] git2r_0.28.0 digest_0.6.27 xtable_1.8-4
[94] httpuv_1.6.1 munsell_0.5.0 bslib_0.2.5.1