Master gene table
Carmen Navarro
2021-05-26
Last updated: 2022-02-05
Checks: 7 0
Knit directory: hesc-epigenomics/
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Summary
This notebook shows how the master gene table is generated. Essentially, genes from hg38 human genome annotation are retrieved and the region around their TSS is scored for H3K4m3, H3K27m3 and H2AUb. DeSeq2 is applied in a Minute-ChIP specific manner and genes are annotated as differential across conditions: Primed vs Naïve, EZH2i treated Naïve vs Naïve and EZH2i treated Primed vs Primed. Final table includes these values, fold change differences and statistical significance scores for all genes.
Additionally, expression values are also used to do a DeSeq2 analysis and such scores are incorporated to the table.
- Gene annotation: Genes obtained from illumina iGenomes UCSC
hg38refFlat.txt file that was also used for the RNA-seq primary analysis: http://igenomes.illumina.com.s3-website-us-east-1.amazonaws.com/Homo_sapiens/UCSC/hg38/Homo_sapiens_UCSC_hg38.tar.gz
The annotation file used is the one coming from Annotations/Genes/refFlat.txt.
Additionally, since all isoforms available are annotated, one is selected per gene to do the TSS analysis. If corresponding identifier in knownCanonical from UCSC data tables exists, then corresponding isoform is used. If more than one identifier corresponds, the longest annotation is selected. For the rest, longest annotation is selected.
Bivalent genes are further annotated by H3K27m3 status, bivalency status and germlayer.
Helper functions
ni_pr_expression_analysis <- function(datadir, alpha = 0.05, shrink = TRUE) {
counts_file <- file.path(datadir, "rnaseq/Kumar_2020/rsem.merged.gene_counts.tsv")
c1_columns <- paste("Kumar_2020_Naive", c("R1", "R2", "R3"), sep = "_")
c2_columns <- paste("Kumar_2020_Primed", c("R1", "R2", "R3"), sep = "_")
rsem_deseq_analysis(counts_file, c1_columns, c2_columns, "Naive", "Primed", "Naive", alpha, shrink = shrink)
}
ni_ezh2i_expression_analysis <- function(datadir, alpha = 0.05, shrink = TRUE) {
counts_file <- file.path(datadir, "rnaseq/Kumar_2020/rsem.merged.gene_counts.tsv")
c1_columns <- paste("Kumar_2020_Naive", c("R1", "R2", "R3"), sep = "_")
c2_columns <- paste("Kumar_2020_Naive_EZH2i", c("R1", "R2", "R3"), sep = "_")
rsem_deseq_analysis(counts_file, c1_columns, c2_columns, "Naive", "EZH2i", "Naive", alpha, shrink = shrink)
}
pr_ezh2i_expression_analysis <- function(datadir, alpha = 0.05, shrink = TRUE) {
counts_file <- file.path(datadir, "rnaseq/Kumar_2020/rsem.merged.gene_counts.tsv")
c1_columns <- paste("Kumar_2020_Primed", c("R1", "R2", "R3"), sep = "_")
c2_columns <- paste("Kumar_2020_Primed_EZH2i", c("R1", "R2", "R3"), sep = "_")
rsem_deseq_analysis(counts_file, c1_columns, c2_columns, "Primed", "EZH2i", "Primed", alpha, shrink = shrink)
}
make_df <- function(diffres, name_suffix) {
df <- data.frame(diffres)
colnames(df) <- paste(colnames(df), name_suffix, sep = "_")
df$gene <- rownames(df)
df
}
make_label <- function(fnames) {
labs <- gsub("_pooled.hg38.*scaled.bw", "", basename(fnames))
# Remove the uncomfortable . in EZH2i elements
labs <- gsub("-", "_", labs)
labs <- gsub("H9_", "", labs)
paste(labs, "mean_cov", sep = "_")
}
merge_by_name <- function(lociset) {
mcols_df <- function(gr) { data.frame(mcols(gr)) }
dfs <- lapply(lociset, mcols_df)
dfs %>% reduce(full_join, by = "name")
}
make_diff_df <- function(diff_lfc, prefix) {
df_diff <- data.frame(diff_lfc)
# DS stands for DeSeq
colnames(df_diff) <- paste(prefix, colnames(df_diff), sep = "_")
df_diff$name <- rownames(df_diff)
df_diff
}Config analysis
genes <- rtracklayer::import( "./data/bed/Kumar_2020/Kumar_2020_genes_hg38_UCSC_frozen.bed")
genes_tss_broad <- promoters(genes, upstream = params$tss_wide, downstream = params$tss_wide)
genes_tss_narrow <- promoters(genes, upstream = params$tss_narrow, downstream = params$tss_narrow)
# bwfiles per histone mark
bwdir <- file.path(params$datadir, "bw/Kumar_2020")
bwfiles <-
list(
k4_naive = list.files(bwdir, pattern = "H3K4m3_H9_Ni_rep[1-3].hg38.scaled.bw", full.names = T),
k4_naive_ezh2i = list.files(bwdir, pattern = "H3K4m3_H9_Ni-EZH2i_rep[1-3].hg38.scaled.bw", full.names = T),
k4_primed = list.files(bwdir, pattern = "H3K4m3_H9_Pr_rep[1-3].hg38.scaled.bw", full.names = T),
k4_primed_ezh2i = list.files(bwdir, pattern = "H3K4m3_H9_Pr-EZH2i_rep[1-3].hg38.scaled.bw", full.names = T),
k27_naive = list.files(bwdir, pattern = "H3K27m3_H9_Ni_rep[1-3].hg38.scaled.bw", full.names = T),
k27_primed = list.files(bwdir, pattern = "H3K27m3_H9_Pr_rep[1-3].hg38.scaled.bw", full.names = T),
ub_naive = list.files(bwdir, pattern = "H2Aub_H9_Ni_rep[1-3].hg38.scaled.bw", full.names = T),
ub_naive_ezh2i = list.files(bwdir, pattern = "H2Aub_H9_Ni-EZH2i_rep[1-3].hg38.scaled.bw", full.names = T),
ub_primed = list.files(bwdir, pattern = "H2Aub_H9_Pr_rep[1-3].hg38.scaled.bw", full.names = T),
ub_primed_ezh2i = list.files(bwdir, pattern = "H2Aub_H9_Pr-EZH2i_rep[1-3].hg38.scaled.bw", full.names = T),
in_naive = list.files(bwdir, pattern = "IN_H9_Ni.*rep[1-3].hg38.*.bw", full.names = T),
in_naive_ezh2i = list.files(bwdir, pattern = "IN_H9_Ni-EZH2i.*rep[1-3].hg38.*.bw", full.names = T),
in_primed = list.files(bwdir, pattern = "IN_H9_Pr_rep[1-3].hg38.*.bw", full.names = T),
in_primed_ezh2i = list.files(bwdir, pattern = "IN_H9_Pr-EZH2i.*rep[1-3].hg38.*.bw", full.names = T)
)
bwfiles_pooled <-
list(
k4 = list.files(bwdir, pattern = "H3K4m3.*pooled.hg38.scaled.*", full.names = T),
k27 = list.files(bwdir, pattern = "H3K27m3.*pooled.hg38.scaled.*", full.names = T),
ub = list.files(bwdir, pattern = "H2Aub.*pooled.hg38.scaled.*", full.names = T),
input = list.files(bwdir, pattern = "IN.*pooled.hg38.*", full.names = T)
)
sorted_colors <- unname(c(gl_condition_colors["Naive_Untreated"],
gl_condition_colors["Naive_EZH2i"],
gl_condition_colors["Primed_Untreated"],
gl_condition_colors["Primed_EZH2i"]))
grey_colors <- c("#cccccc", "#aaaaaa", "#888888", "#555555")Raw pooled values at TSS per gene
At this point kept area around TSS the same size even though K4 is narrower, so it’s fairer to put them all in the same table.
pooled_k4 <- bw_loci(bwfiles_pooled$k4, genes_tss_broad, labels = make_label(bwfiles_pooled$k4))
pooled_k27 <- bw_loci(bwfiles_pooled$k27, genes_tss_broad, labels = make_label(bwfiles_pooled$k27))
pooled_h2aub <- bw_loci(bwfiles_pooled$ub, genes_tss_broad, labels = make_label(bwfiles_pooled$ub))
pooled_inp <- bw_loci(bwfiles_pooled$input, genes_tss_broad, labels = make_label(bwfiles_pooled$input))
pooled_df <- merge_by_name(list(pooled_k4, pooled_k27, pooled_h2aub, pooled_inp))
master_df <- pooled_dfK27m3 diff analysis
Primed vs Naive
c1 <- bw_loci(bwfiles$k27_naive, genes_tss_broad)
c2 <- bw_loci(bwfiles$k27_primed, genes_tss_broad)
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "Primed", estimate_size_factors = FALSE)
if (params$shrink_histones == TRUE) {
diff_lfc <- lfcShrink(diff, coef="condition_Primed_vs_Naive", type="apeglm")
diff <- diff_lfc
} else {
diff <- results(diff, alpha = params$pval_cutoff)
}
plotMA(diff)
df_diff <- make_diff_df(diff, "H3K27m3_DS_Pr_vs_Ni")
master_df <- left_join(master_df, df_diff, by = "name")H3K4m3 diff analysis
Primed vs Naive
c1 <- bw_loci(bwfiles$k4_naive, genes_tss_narrow)
c2 <- bw_loci(bwfiles$k4_primed, genes_tss_narrow)
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "Primed", estimate_size_factors = FALSE)
if (params$shrink_histones == TRUE) {
diff_lfc <- lfcShrink(diff, coef="condition_Primed_vs_Naive", type="apeglm")
diff <- diff_lfc
} else {
diff <- results(diff, alpha = params$pval_cutoff)
}
plotMA(diff)
df_diff <- make_diff_df(diff, "H3K4m3_DS_Pr_vs_Ni")
master_df <- left_join(master_df, df_diff, by = "name")EZH2i vs Naive
c1 <- bw_loci(bwfiles$k4_naive, genes_tss_narrow)
c2 <- bw_loci(bwfiles$k4_naive_ezh2i, genes_tss_narrow)
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "EZH2i", estimate_size_factors = FALSE)
if (params$shrink_histones == TRUE) {
diff_lfc <- lfcShrink(diff, coef="condition_EZH2i_vs_Naive", type="apeglm")
diff <- diff_lfc
} else {
diff <- results(diff, alpha = params$pval_cutoff)
}
plotMA(diff)
#
df_diff <- make_diff_df(diff, "H3K4m3_DS_EZH2i_vs_Ni")
master_df <- left_join(master_df, df_diff, by = "name")EZH2i vs Primed
c1 <- bw_loci(bwfiles$k4_primed, genes_tss_narrow)
c2 <- bw_loci(bwfiles$k4_primed_ezh2i, genes_tss_narrow)
diff <- bw_granges_diff_analysis(c1, c2, "Primed", "EZH2i", estimate_size_factors = FALSE)
if (params$shrink_histones == TRUE) {
diff_lfc <- lfcShrink(diff, coef="condition_EZH2i_vs_Primed", type="apeglm")
diff <- diff_lfc
} else {
diff <- results(diff, alpha = params$pval_cutoff)
}
plotMA(diff)
#
df_diff <- make_diff_df(diff, "H3K4m3_DS_EZH2i_vs_Pr")
master_df <- left_join(master_df, df_diff, by = "name")H2AUb diff analysis
Primed vs Naive
c1 <- bw_loci(bwfiles$ub_naive, genes_tss_broad)
c2 <- bw_loci(bwfiles$ub_primed, genes_tss_broad)
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "Primed", estimate_size_factors = FALSE)
if (params$shrink_histones == TRUE) {
diff_lfc <- lfcShrink(diff, coef="condition_Primed_vs_Naive", type="apeglm")
diff <- diff_lfc
} else {
diff <- results(diff, alpha = params$pval_cutoff)
}
plotMA(diff)
df_diff <- make_diff_df(diff, "H2Aub_DS_Pr_vs_Ni")
master_df <- left_join(master_df, df_diff, by = "name")EZH2i vs Naive
c1 <- bw_loci(bwfiles$ub_naive, genes_tss_broad)
c2 <- bw_loci(bwfiles$ub_naive_ezh2i, genes_tss_broad)
diff <- bw_granges_diff_analysis(c1, c2, "Naive", "EZH2i", estimate_size_factors = FALSE)
if (params$shrink_histones == TRUE) {
diff_lfc <- lfcShrink(diff, coef="condition_EZH2i_vs_Naive", type="apeglm")
diff <- diff_lfc
} else {
diff <- results(diff, alpha = params$pval_cutoff)
}
plotMA(diff)
df_diff <- make_diff_df(diff, "H2Aub_DS_EZH2i_vs_Ni")
master_df <- left_join(master_df, df_diff, by = "name")EZH2i vs Primed
c1 <- bw_loci(bwfiles$ub_primed, genes_tss_broad)
c2 <- bw_loci(bwfiles$ub_primed_ezh2i, genes_tss_broad)
diff <- bw_granges_diff_analysis(c1, c2, "Primed", "EZH2i", estimate_size_factors = FALSE)
if (params$shrink_histones == TRUE) {
diff_lfc <- lfcShrink(diff, coef="condition_EZH2i_vs_Primed", type="apeglm")
diff <- diff_lfc
} else {
diff <- results(diff, alpha = params$pval_cutoff)
}
plotMA(diff)
df_diff <- make_diff_df(diff, "H2Aub_DS_EZH2i_vs_Pr")
master_df <- left_join(master_df, df_diff, by = "name")RNA-seq diff analysis
Primed vs Naive
ni_pr_diff <- ni_pr_expression_analysis(params$datadir, alpha = params$pval_cutoff, shrink = params$shrink_rnaseq)
plotMA(ni_pr_diff)
EZH2i vs Naive
ni_ezh2i_diff <- ni_ezh2i_expression_analysis(params$datadir, alpha = params$pval_cutoff, shrink = params$shrink_rnaseq)
plotMA(ni_ezh2i_diff)
EZH2i vs Primed
pr_ezh2i_diff <- pr_ezh2i_expression_analysis(params$datadir, alpha = params$pval_cutoff, shrink = params$shrink_rnaseq)
plotMA(pr_ezh2i_diff)
Expression table
counts_file <- file.path(params$datadir, "rnaseq/Kumar_2020/rsem.merged.gene_tpm.tsv")
tpm_counts <- read.table(counts_file, sep = "\t", header = TRUE)
columns <- colnames(tpm_counts)[!colnames(tpm_counts) %in% c("transcript_id.s.")]
tpm_counts <- tpm_counts[, columns]
new_values <- paste("RNASeq_TPM",
gsub("Kumar_2020_", "", columns[2:length(columns)]), sep = "_")
new_values <- gsub("Naive", "Ni", new_values)
new_values <- gsub("Primed", "Pr", new_values)
new_columns <- c("name", new_values)
colnames(tpm_counts) <- new_columns
make_df <- function(diffres, name_suffix) {
df <- data.frame(diffres)
colnames(df) <- paste(colnames(df), name_suffix, sep = "_")
df$gene <- rownames(df)
df
}
dfs <- list(make_diff_df(ni_pr_diff, "RNASeq_DS_Pr_vs_Ni"),
make_diff_df(ni_ezh2i_diff, "RNASeq_DS_EZH2i_vs_Ni"),
make_diff_df(pr_ezh2i_diff, "RNASeq_DS_EZH2i_vs_Pr"),
tpm_counts)
expr_results_all <- reduce(dfs, full_join, by = "name")H3K27m3 groups
select_groups <- function(df, pval_col, fc_col, basemean_col, quantile,
p_cutoff = 0.05, fc_cutoff = 1, basemean_quantile = 0.1) {
# I don't want to discard the NAs as they will go to the unenriched group.
df[is.na(df[[pval_col]]), pval_col] <- 1
min_mean <- quantile(df[[basemean_col]], probs = basemean_quantile)
signif_up_tss <- df %>% filter(.data[[pval_col]] <= p_cutoff & .data[[fc_col]] > fc_cutoff & .data[[basemean_col]] > min_mean)
signif_down_tss <- df %>% filter(.data[[pval_col]] <= p_cutoff & .data[[fc_col]] < -fc_cutoff & .data[[basemean_col]] > min_mean)
not_signif <- df %>% filter(.data[[pval_col]] > p_cutoff)
# Top
mean_cutoff <- quantile(not_signif[[basemean_col]], quantile)
always_up <- not_signif %>% filter(.data[[basemean_col]] >= mean_cutoff)
rest <- not_signif %>% filter(.data[[basemean_col]] < mean_cutoff)
list(up = signif_up_tss,
down = signif_down_tss,
always_up = always_up,
not_enriched = rest)
}
select_groups_bivalent <- function(df, quantile = 0.8, p_cutoff = 0.05, fc_cutoff = 1, basemean_quantile = 0.1, min_k4 = 2) {
select_k4 <- function(df, min_k4) {
df %>% filter(.data[["H3K4m3_Pr_mean_cov"]] > min_k4 | .data[["H3K4m3_Ni_mean_cov"]] > min_k4)
}
groups <- select_groups(
df,
"H3K27m3_DS_Pr_vs_Ni_padj",
"H3K27m3_DS_Pr_vs_Ni_log2FoldChange",
"H3K27m3_DS_Pr_vs_Ni_baseMean",
quantile,
p_cutoff,
fc_cutoff,
basemean_quantile
)
lapply(groups, select_k4, min_k4 = min_k4)
}
k27_groups <- select_groups_bivalent(master_df, fc_cutoff = log2(1.5), p_cutoff = 0.05)
# Annotate our groups
master_df$k27_bivalency_grp <- "None"
master_df[master_df$name %in% k27_groups$up$name, "k27_bivalency_grp"] <- "Pr_higher_than_Ni"
master_df[master_df$name %in% k27_groups$down$name, "k27_bivalency_grp"] <- "Ni_higher_than_Pr"
master_df[master_df$name %in% k27_groups$always_up$name, "k27_bivalency_grp"] <- "Always_up"
master_df[master_df$name %in% k27_groups$not_enriched$name, "k27_bivalency_grp"] <- "K4_only"External annotations
genes_loci <- import("./data/bed/Kumar_2020/Kumar_2020_genes_hg38_UCSC_frozen.bed")
genes_tss <- promoters(genes_loci, upstream = 2500, downstream = 2500)
court_bivalent <- rtracklayer::import("./data/bed/Bivalent_Court2017.hg38.bed")
court_biv_genes <- subsetByOverlaps(genes_tss, court_bivalent)
master_df$court_bivalent <- "No"
master_df[master_df$name %in% court_biv_genes$name, "court_bivalent"] <- "Yes"Final table
final <- left_join(master_df, expr_results_all, by = "name")
# Add TSS broad coords
loci <- data.frame(genes_tss_broad)
final <- left_join(final, loci, by = "name")
columns <- colnames(final)
first_cols <- c("name", "seqnames", "start", "end", "strand", "k27_bivalency_grp", "court_bivalent")
order <-
c(first_cols,
sort(columns[!(columns %in% first_cols)]))
filename <- "./data/meta/Kumar_2020_master_gene_table_rnaseq_shrunk_annotated.tsv"
write.table(
format(final[, order], digits = 4),
file = filename,
sep = "\t",
col.names = T,
quote = F,
row.names = F
)
sessionInfo()R version 4.1.2 (2021-11-01)
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 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] biomaRt_2.50.0
[2] DESeq2_1.34.0
[3] SummarizedExperiment_1.24.0
[4] MatrixGenerics_1.6.0
[5] matrixStats_0.61.0
[6] tidyr_1.1.4
[7] cowplot_1.1.1
[8] xfun_0.28
[9] dplyr_1.0.7
[10] purrr_0.3.4
[11] rtracklayer_1.54.0
[12] org.Hs.eg.db_3.14.0
[13] TxDb.Hsapiens.UCSC.hg38.knownGene_3.14.0
[14] GenomicFeatures_1.46.1
[15] AnnotationDbi_1.56.2
[16] Biobase_2.54.0
[17] GenomicRanges_1.46.0
[18] GenomeInfoDb_1.30.0
[19] IRanges_2.28.0
[20] S4Vectors_0.32.2
[21] BiocGenerics_0.40.0
[22] knitr_1.36
[23] ggplot2_3.3.5
[24] wigglescout_0.13.5
[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_0.34.0 fs_1.5.0
[7] listenv_0.8.0 furrr_0.2.3 bit64_4.0.5
[10] mvtnorm_1.1-3 apeglm_1.16.0 fansi_0.5.0
[13] xml2_1.3.2 splines_4.1.2 codetools_0.2-18
[16] cachem_1.0.6 geneplotter_1.72.0 jsonlite_1.7.2
[19] Rsamtools_2.10.0 annotate_1.72.0 dbplyr_2.1.1
[22] png_0.1-7 compiler_4.1.2 httr_1.4.2
[25] assertthat_0.2.1 Matrix_1.4-0 fastmap_1.1.0
[28] later_1.3.0 htmltools_0.5.2 prettyunits_1.1.1
[31] tools_4.1.2 coda_0.19-4 gtable_0.3.0
[34] glue_1.5.1 GenomeInfoDbData_1.2.7 reshape2_1.4.4
[37] rappdirs_0.3.3 Rcpp_1.0.7 bbmle_1.0.24
[40] jquerylib_0.1.4 vctrs_0.3.8 Biostrings_2.62.0
[43] stringr_1.4.0 globals_0.14.0 lifecycle_1.0.1
[46] restfulr_0.0.13 XML_3.99-0.8 future_1.23.0
[49] MASS_7.3-54 zlibbioc_1.40.0 scales_1.1.1
[52] hms_1.1.1 promises_1.2.0.1 parallel_4.1.2
[55] RColorBrewer_1.1-2 yaml_2.2.1 curl_4.3.2
[58] memoise_2.0.0 emdbook_1.3.12 sass_0.4.0
[61] bdsmatrix_1.3-4 stringi_1.7.6 RSQLite_2.2.8
[64] highr_0.9 genefilter_1.76.0 BiocIO_1.4.0
[67] filelock_1.0.2 BiocParallel_1.28.0 rlang_0.4.12
[70] pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.14
[73] lattice_0.20-45 GenomicAlignments_1.30.0 bit_4.0.4
[76] tidyselect_1.1.1 parallelly_1.28.1 plyr_1.8.6
[79] magrittr_2.0.1 R6_2.5.1 generics_0.1.1
[82] DelayedArray_0.20.0 DBI_1.1.1 pillar_1.6.4
[85] whisker_0.4 withr_2.4.2 survival_3.2-13
[88] KEGGREST_1.34.0 RCurl_1.98-1.5 tibble_3.1.6
[91] crayon_1.4.2 utf8_1.2.2 BiocFileCache_2.2.0
[94] rmarkdown_2.11 progress_1.2.2 locfit_1.5-9.4
[97] grid_4.1.2 blob_1.2.2 git2r_0.28.0
[100] digest_0.6.28 xtable_1.8-4 numDeriv_2016.8-1.1
[103] httpuv_1.6.3 munsell_0.5.0 bslib_0.3.1