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predict_correlated_deg.Rmd
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---
title: "predict_correlated_deg"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
dyn.load("/home/rstudio/libs/libxml2.so.2")
library(DESeq2)
library(dplyr)
library(GenomicRanges)
library(tibble)
library(rtracklayer)
library(ggplot2)
library(stringr)
```
Define constants:
```{r, include=T}
pcc.fdr = 0.05
```
Define a function for DESeq2 count normalisation across replicates:
```{r, include=T}
normalize_counts = function(raw.counts, cond.table, diff.levels, dds.filename) {
raw.counts = round(raw.counts)
row.names(cond.table) = names(raw.counts)
cond.table$condition = factor(cond.table$condition, levels = diff.levels)
dds = DESeqDataSetFromMatrix(countData = raw.counts,
colData = cond.table,
design = ~ condition)
dds = DESeq(dds)
saveRDS(dds, dds.filename)
dds = estimateSizeFactors(dds)
return(counts(dds, normalized = T))
}
```
Define a function to generate TSS coordinates from transcript coordinates:
```{r, include=T}
generate_tss = function(tx.ranges, tss.bed.filename) {
tss.ranges = GRanges(seqnames = seqnames(tx.ranges),
ranges = IRanges(start = ifelse(strand(tx.ranges) == "+",
start(ranges(tx.ranges)),
end(ranges(tx.ranges)) - 1),
end = ifelse(strand(tx.ranges) == "+",
start(ranges(tx.ranges)) + 1,
end(ranges(tx.ranges))),
names = names(ranges(tx.ranges))),
strand = strand(tx.ranges),
gene_name = tx.ranges$gene_name)
export(tss.ranges, tss.bed.filename)
return(tss.ranges)
}
```
Define a function to generate the TSS annotation of the NFIA-dependent genes:
```{r, include=T}
generate_tss_annot = function(p.dep.genes, p.dep.tss.filename) {
p.dep.genes.annot = mm10.annot.genes[mm10.annot.genes$gene_name %in% p.dep.genes, ]
p.dep.genes.ranges = GRanges(seqnames = p.dep.genes.annot$chr,
ranges = IRanges(start = p.dep.genes.annot$start,
end = p.dep.genes.annot$stop,
names = p.dep.genes.annot$tx_name),
strand = strand(p.dep.genes.annot$strand),
gene_name = p.dep.genes.annot$gene_name)
p.dep.tss.ranges = generate_tss(p.dep.genes.ranges, p.dep.tss.filename)
return(p.dep.tss.ranges)
}
```
Define a function to generate the region-gene assignment area around a region as a vicinity of a fixed radius (in kbp):
```{r, include=T}
generate_vicinity_radius = function(dep.regions, vicinity.radius, vicinity.ranges.bed.filename) {
vicinity.radius = vicinity.radius * 1000
dep.regions.df = as.data.frame(dep.regions) %>%
mutate(seqnames = as.character(seqnames)) %>%
mutate(strand = as.character(strand)) %>%
rowwise() %>%
mutate(., max_position = mm10.chr.sizes[seqnames] - 1) %>%
ungroup()
vicinity.ranges = GRanges(seqnames = dep.regions.df$seqnames,
ranges = IRanges(start = ifelse(dep.regions.df$start - vicinity.radius > 0,
dep.regions.df$start - vicinity.radius,
1),
end = ifelse(dep.regions.df$end + vicinity.radius < dep.regions.df$max_position,
dep.regions.df$end + vicinity.radius,
dep.regions.df$max_position),
names = dep.regions.df$name),
strand = Rle(strand("*")))
export(vicinity.ranges, vicinity.ranges.bed.filename)
return(vicinity.ranges)
}
```
Define a function to generate the region-gene assignment area around a region as the largest size-selected TAD that contains the region (TAD size limits are in kbp):
```{r, include=T}
set_null_names = function(gr) {
names(gr) = NULL
return(gr)
}
generate_vicinity_tad = function(min.tad.size, max.tad.size, dep.regions, domain.name, vicinity.ranges.bed.filename) {
tads.size.selected = tads[end(ranges(tads)) - start(ranges(tads)) >= min.tad.size * 1000 &
end(ranges(tads)) - start(ranges(tads)) <= max.tad.size * 1000]
export(tads.size.selected,
paste0("../r_results/predict_correlated_deg/tads_size_selected_",
min.tad.size, "-", max.tad.size, "kbp.bed"))
dep.regions.in.selected.tads.res = GenomicRanges::findOverlaps(query = dep.regions,
subject = tads.size.selected,
type = "within",
select = "all",
ignore.strand = T)
dep.regions.hits = data.frame(region.row.nums = queryHits(dep.regions.in.selected.tads.res)) %>%
left_join(as.data.frame(dep.regions) %>%
rownames_to_column(var = "region.row.nums") %>%
mutate(region.row.nums = as.integer(region.row.nums)),
by = c("region.row.nums" = "region.row.nums")) %>%
dplyr::select(name)
tads.hits = data.frame(tad.row.nums = subjectHits(dep.regions.in.selected.tads.res)) %>%
left_join(as.data.frame(tads.size.selected) %>%
rownames_to_column(var = "tad.row.nums") %>%
mutate(tad.row.nums = as.integer(tad.row.nums)),
by = c("tad.row.nums" = "tad.row.nums")) %>%
dplyr::rename(tad_width = width)
regions.in.tads.final = dep.regions.hits %>%
bind_cols(tads.hits) %>%
group_by(name) %>%
mutate(is_max = (tad_width == max(tad_width))) %>%
ungroup() %>%
filter(is_max) %>%
group_by(name) %>%
slice(1) %>% # retain only the first largest TAD per region
ungroup() %>%
dplyr::select(name,
seqnames,
start,
end) %>%
dplyr::rename(region = name,
vicinity_start = start,
vicinity_end = end)
regions.in.tads.final.ranges = GRanges(seqnames = regions.in.tads.final$seqnames,
ranges = IRanges(start = regions.in.tads.final$vicinity_start,
end = regions.in.tads.final$vicinity_end,
names = regions.in.tads.final$region),
strand = Rle(strand("*")))
export(regions.in.tads.final.ranges, vicinity.ranges.bed.filename)
tads.matched = as.data.frame(set_null_names(regions.in.tads.final.ranges)) %>%
mutate(seqnames = as.character(seqnames)) %>%
dplyr::select(seqnames, start, end) %>%
arrange(seqnames, start, end) %>%
distinct()
tads.matched.ranges = GRanges(seqnames = tads.matched$seqnames,
ranges = IRanges(start = tads.matched$start,
end = tads.matched$end),
strand = Rle(strand("*")))
export(tads.matched.ranges,
paste0("../r_results/predict_correlated_deg/tads_matched_", domain.name, "_",
min.tad.size, "-", max.tad.size, "kbp_by_dep_regions_fdr",
fdr, "_min-l2fc", min.l2fc, "_min-baseMean", min.baseMean, ".bed"))
cat(domain.name, ":\n")
cat("Number of NFIA-dependent regions in TADs:", length(unique(regions.in.tads.final$region)),
"(", length(unique(regions.in.tads.final$region)) / length(dep.regions) * 100, "% )\n")
cat("Number of max-length TADs with at least one NFIA-dependent region:", (regions.in.tads.final %>% dplyr::select(seqnames, vicinity_start, vicinity_end) %>% distinct() %>% nrow()),
"(", (regions.in.tads.final %>% dplyr::select(seqnames, vicinity_start, vicinity_end) %>% distinct() %>% nrow()) / length(tads.size.selected) * 100, "% of all size-selected TADs)\n")
cat("Number of NFIA-dependent regions per TAD:", length(unique(regions.in.tads.final$region)) / (regions.in.tads.final %>% dplyr::select(seqnames, vicinity_start, vicinity_end) %>% distinct() %>% nrow()), "\n")
cat("---\n")
return(regions.in.tads.final.ranges)
}
```
Define a function to predict NFIA-dependent region - NFIA-dependent gene regulatory associations:
```{r, include=T}
add_gene_suffix = function(column.names) {
return(paste0(column.names, "_gene"))
}
add_region_suffix = function(column.names) {
return(paste0(column.names, "_region"))
}
find_background_pairs = function(dep.tss, vicinity.gr) {
dep.tss.df = as.data.frame(dep.tss) %>%
rownames_to_column(var = "tx_id") %>%
rownames_to_column(var = "tss_num") %>%
dplyr::select(tss_num,
seqnames,
gene_name)
vicinity.df = as.data.frame(vicinity.gr) %>%
rownames_to_column(var = "region_name") %>%
rownames_to_column(var = "region_num") %>%
dplyr::select(region_num,
seqnames,
region_name)
dep.tss.chrs = as.character(unique(sort(dep.tss.df$seqnames)))
chr.corresp.df = bind_rows(lapply(dep.tss.chrs,
function(chr) {
vicinity.df %>%
filter(seqnames != chr) %>%
mutate(chr = chr) %>%
dplyr::select(chr,
region_num)
}))
hits.df = dep.tss.df %>%
left_join(chr.corresp.df,
by = c("seqnames" = "chr"))
hits = Hits(from = as.integer(hits.df$tss_num),
to = as.integer(hits.df$region_num),
nLnode = nrow(dep.tss.df),
nRnode = nrow(vicinity.df))
return(hits)
}
calc_correlations = function(vicinity.gr, dep.tss,
norm.region.counts, norm.gene.counts,
region.sample.names, gene.sample.names,
domain.name, calc.mode, corr.rds.filename) {
region.tss.shared.sample.names = intersect(region.sample.names, gene.sample.names)
if (calc.mode == "target") {
tss.vicinity.hits = GenomicRanges::findOverlaps(query = dep.tss,
subject = vicinity.gr,
type = "within",
select = "all",
ignore.strand = T)
} else if (calc.mode == "background") {
tss.vicinity.hits = find_background_pairs(dep.tss,
vicinity.gr)
}
tss.hits = data.frame(tss_num = queryHits(tss.vicinity.hits)) %>%
left_join(as.data.frame(dep.tss) %>%
rownames_to_column(var = "tss_id") %>%
rownames_to_column(var = "tss_num") %>%
mutate(tss_num = as.integer(tss_num)) %>%
dplyr::select(tss_num,
gene_name),
by = c("tss_num" = "tss_num")) %>%
left_join(norm.gene.counts,
by = c("gene_name" = "gene_names")) %>%
dplyr::select(tss_num,
gene_name,
all_of(sort(names(.)[names(.) %in% region.tss.shared.sample.names]))) %>%
dplyr::rename_with(add_gene_suffix, all_of(region.tss.shared.sample.names))
vicinity.hits = data.frame(vicinity_num = subjectHits(tss.vicinity.hits)) %>%
left_join(as.data.frame(vicinity.gr) %>%
rownames_to_column(var = "region_id") %>%
rownames_to_column(var = "vicinity_num") %>%
mutate(vicinity_num = as.integer(vicinity_num)) %>%
dplyr::select(vicinity_num,
region_id),
by = c("vicinity_num" = "vicinity_num")) %>%
left_join(norm.region.counts,
by = c("region_id" = "region_names")) %>%
dplyr::select(vicinity_num,
region_id,
all_of(sort(names(.)[names(.) %in% region.tss.shared.sample.names]))) %>%
dplyr::rename_with(add_region_suffix, all_of(region.tss.shared.sample.names))
vicinity.tss.corr = vicinity.hits %>%
bind_cols(tss.hits) %>%
dplyr::select(-vicinity_num,
-tss_num) %>%
distinct() %>%
rowwise() %>%
mutate(pcc = cor(x = c_across(ends_with("_region")),
y = c_across(ends_with("_gene")),
method = "pearson")) %>%
ungroup()
vicinity.tss.corr.final = vicinity.tss.corr %>%
dplyr::select(region_id,
gene_name,
pcc) %>%
mutate(abs_pcc = abs(pcc))
cat(domain.name, ":", calc.mode, "\n")
cat("Number of region-gene associations:", nrow(vicinity.tss.corr.final), "\n")
cat("Number of unique regions :", length(unique(vicinity.tss.corr.final$region_id)), "\n")
cat("Number of unique genes :", length(unique(vicinity.tss.corr.final$gene_name)), "\n")
cat("---\n")
saveRDS(vicinity.tss.corr.final, corr.rds.filename)
return(vicinity.tss.corr.final)
}
```
Define a function to calculate empirical p-values for PCCs:
```{r, include=T}
calc_empirical_pvalue = function(abs.pcc, bkgd.df) {
return(nrow(bkgd.df %>% filter(abs_pcc > abs.pcc)) / nrow(bkgd.df))
}
```
Define a function to find significant NFIA-dependent region-gene assignments:
```{r, include=T}
assign_regions_to_genes = function(domain.name, area.type) {
cat(domain.name, ",", area.type, ": fdr =", fdr, ";", "min.l2fc =", min.l2fc, ";", "min.baseMean =", min.baseMean, "\n")
dep.regions = import(paste0("../r_results/select_diff_regions/", domain.name, "_dep_ranges",
"_fdr", fdr, "_min-l2fc", min.l2fc, "_min-baseMean", min.baseMean,
".bed"))
if (area.type == "radius") {
vicinity.file.name = paste0("../r_results/predict_correlated_deg/", domain.name, "_vicinities_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".bed")
vicinity = generate_vicinity_radius(dep.regions,
vicinity.radius,
vicinity.file.name)
} else if (area.type == "tad") {
vicinity.file.name = paste0("../r_results/predict_correlated_deg/", domain.name, "_vicinities_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".bed")
vicinity = generate_vicinity_tad(min.tad.size,
max.tad.size,
dep.regions,
domain.name,
vicinity.file.name)
}
if (domain.name == "p1") {
dep.tss.ranges = p1.dep.tss.ranges
norm.region.counts = p1.norm.region.counts
norm.gene.counts = p1.norm.gene.counts
gene.sample.names = p1.gene.sample.names
region.sample.names = p1.region.sample.names
} else if (domain.name == "p2") {
dep.tss.ranges = p2.dep.tss.ranges
norm.region.counts = p2.norm.region.counts
norm.gene.counts = p2.norm.gene.counts
gene.sample.names = p2.gene.sample.names
region.sample.names = p2.region.sample.names
} else if (domain.name == "pM") {
dep.tss.ranges = pM.dep.tss.ranges
norm.region.counts = pM.norm.region.counts
norm.gene.counts = pM.norm.gene.counts
gene.sample.names = pM.gene.sample.names
region.sample.names = pM.region.sample.names
}
if (area.type == "radius") {
corr.filename = paste0("../r_results/predict_correlated_deg/", domain.name, "_corr_in_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds")
corr.plot.filename = paste0("../r_results/predict_correlated_deg/plots/", domain.name, "_corr_in_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, "_hist.pdf")
bkgd.filename = paste0("../r_results/predict_correlated_deg/", domain.name, "_bkgd_in_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds")
bkgd.plot.filename = paste0("../r_results/predict_correlated_deg/plots/", domain.name, "_bkgd_in_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, "_hist.pdf")
} else if (area.type == "tad") {
corr.filename = paste0("../r_results/predict_correlated_deg/", domain.name, "_corr_in_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds")
corr.plot.filename = paste0("../r_results/predict_correlated_deg/plots/", domain.name, "_corr_in_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, "_hist.pdf")
bkgd.filename = paste0("../r_results/predict_correlated_deg/", domain.name, "_bkgd_in_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds")
bkgd.plot.filename = paste0("../r_results/predict_correlated_deg/plots/", domain.name, "_bkgd_in_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, "_hist.pdf")
}
corr = calc_correlations(vicinity, dep.tss.ranges,
norm.region.counts, norm.gene.counts,
region.sample.names, gene.sample.names,
domain.name,
"target",
corr.filename)
p = ggplot(corr) +
geom_histogram(aes(x = abs_pcc),
binwidth = 0.05) +
theme_classic()
ggsave(filename = corr.plot.filename,
plot = p)
bkgd = calc_correlations(vicinity, dep.tss.ranges,
norm.region.counts, norm.gene.counts,
region.sample.names, gene.sample.names,
domain.name,
"background",
bkgd.filename)
p = ggplot(bkgd) +
geom_histogram(aes(x = abs_pcc),
binwidth = 0.05) +
theme_classic()
ggsave(filename = bkgd.plot.filename,
plot = p)
corr.sign = corr %>%
rowwise() %>%
mutate(pvalue = calc_empirical_pvalue(abs_pcc, bkgd)) %>%
ungroup() %>%
mutate(padj = p.adjust(pvalue, method = "BH")) %>%
mutate(fdr = pcc.fdr) %>%
mutate(sign.padj = (padj < fdr))
cat(domain.name, ":", area.type, "\n")
cat("Number of significant NFIA-dependent region-gene pairs ( FDR =", pcc.fdr, ") :", nrow(corr.sign %>% filter(sign.padj)), "\n")
cat("---\n")
if (nrow(corr.sign %>% filter(sign.padj)) > 0) {
if (domain.name == "p1") {
strictest.dep.regions = p1.dep.regions$name
} else if (domain.name == "p2") {
strictest.dep.regions = p2.dep.regions$name
} else if (domain.name == "pM") {
strictest.dep.regions = pM.dep.regions$name
}
sign.assigned.regions = corr.sign %>%
filter(sign.padj) %>%
pull(region_id) %>%
unique()
cat("Number of the strictest regions among all regions in significant assignments:",
length(intersect(sign.assigned.regions, strictest.dep.regions)),
"(", length(intersect(sign.assigned.regions, strictest.dep.regions)) / length(sign.assigned.regions), ")\n")
}
return(list("vicinity" = vicinity,
"corr" = corr,
"bkgd" = bkgd,
"corr.sign" = corr.sign))
}
```
Load the master table:
```{r, include=T}
master.table = assays(readRDS("../input/salmon.merged.gene_counts.rds"))$counts
gene.names = rownames(master.table)
```
Normalise gene expression across replicates using DESeq2:
```{r, include=T}
# p1
p1.gene.sample.names = unlist(stringr::str_match_all(names(master.table), "WT_D[179]+_p1.*"))
p1.raw.counts = master.table %>%
dplyr::select(all_of(p1.gene.sample.names))
p1.cond = data.frame(condition = stringr::str_extract(names(p1.raw.counts), "D(7|9|11)"))
p1.diff.levels = c("D7", "D9", "D11")
p1.norm.gene.counts = as.data.frame(normalize_counts(p1.raw.counts, p1.cond, p1.diff.levels,
"../r_results/predict_correlated_deg/p1_gene_dds.rds")) %>%
mutate(gene_names = gene.names) %>%
dplyr::select(gene_names, all_of(names(.)[names(.) != "gene_names"]))
saveRDS(p1.norm.gene.counts,
file = "../r_results/predict_correlated_deg/p1_norm_counts_expression.rds")
# p2
p2.gene.sample.names = unlist(stringr::str_match_all(names(master.table), "WT_D[179]+_p2.*"))
p2.raw.counts = master.table %>%
dplyr::select(all_of(p2.gene.sample.names))
p2.cond = data.frame(condition = stringr::str_extract(names(p2.raw.counts), "D(7|9|11)"))
p2.diff.levels = c("D7", "D9", "D11")
p2.norm.gene.counts = as.data.frame(normalize_counts(p2.raw.counts, p2.cond, p2.diff.levels,
"../r_results/predict_correlated_deg/p2_gene_dds.rds")) %>%
mutate(gene_names = gene.names) %>%
dplyr::select(gene_names, all_of(names(.)[names(.) != "gene_names"]))
saveRDS(p2.norm.gene.counts,
file = "../r_results/predict_correlated_deg/p2_norm_counts_expression.rds")
# pMN
pM.gene.sample.names = unlist(stringr::str_match_all(names(master.table), "WT_D[179]+_pM.*"))
pM.raw.counts = master.table %>%
dplyr::select(all_of(pM.gene.sample.names))
pM.cond = data.frame(condition = stringr::str_extract(names(pM.raw.counts), "D(7|9|11)"))
pM.diff.levels = c("D7", "D9", "D11")
pM.norm.gene.counts = as.data.frame(normalize_counts(pM.raw.counts, pM.cond, pM.diff.levels,
"../r_results/predict_correlated_deg/pM_gene_dds.rds")) %>%
mutate(gene_names = gene.names) %>%
dplyr::select(gene_names, all_of(names(.)[names(.) != "gene_names"]))
saveRDS(pM.norm.gene.counts,
file = "../r_results/predict_correlated_deg/pM_norm_counts_expression.rds")
```
Remove the gene expression master table from memory:
```{r, include=T}
rm(master.table)
```
Upload the chromatin accessibility master table:
```{r, include=T}
master.table = read.delim(file = "../input/consensus_peaks.mRp.clN.featureCounts.txt",
header = T,
sep = "\t",
skip = 1)
names(master.table) = gsub(pattern = ".mLb.clN.bam", replacement = "", x = names(master.table))
region.annot = master.table %>%
dplyr::select(Geneid,
Chr,
Start,
End,
Strand,
Length)
```
Normalise chromatin accessibility across replicates using DESeq2:
```{r, include=T}
# p1
p1.region.sample.names = unlist(stringr::str_match_all(names(master.table), "WT_D[179]+_p1.*"))
p1.raw.counts = master.table %>%
dplyr::select(all_of(p1.region.sample.names))
p1.cond = data.frame(condition = stringr::str_extract(names(p1.raw.counts), "D(7|9|11)"))
p1.diff.levels = c("D7", "D9", "D11")
p1.norm.region.counts = as.data.frame(normalize_counts(p1.raw.counts, p1.cond, p1.diff.levels,
"../r_results/predict_correlated_deg/p1_region_dds.rds")) %>%
mutate(region_names = region.annot$Geneid) %>%
dplyr::select(region_names, all_of(names(.)[names(.) != "region_names"]))
saveRDS(p1.norm.region.counts,
file = "../r_results/predict_correlated_deg/p1_norm_counts_accessibility.rds")
# p2
p2.region.sample.names = unlist(stringr::str_match_all(names(master.table), "WT_D[179]+_p2.*"))
p2.raw.counts = master.table %>%
dplyr::select(all_of(p2.region.sample.names))
p2.cond = data.frame(condition = stringr::str_extract(names(p2.raw.counts), "D(7|9|11)"))
p2.diff.levels = c("D7", "D9", "D11")
p2.norm.region.counts = as.data.frame(normalize_counts(p2.raw.counts, p2.cond, p2.diff.levels,
"../r_results/predict_correlated_deg/p2_region_dds.rds")) %>%
mutate(region_names = region.annot$Geneid) %>%
dplyr::select(region_names, all_of(names(.)[names(.) != "region_names"]))
saveRDS(p2.norm.region.counts,
file = "../r_results/predict_correlated_deg/p2_norm_counts_accessibility.rds")
# pMN
pM.region.sample.names = unlist(stringr::str_match_all(names(master.table), "WT_D[179]+_pM.*"))
pM.raw.counts = master.table %>%
dplyr::select(all_of(pM.region.sample.names))
pM.cond = data.frame(condition = stringr::str_extract(names(pM.raw.counts), "D(7|9|11)"))
pM.diff.levels = c("D7", "D9", "D11")
pM.norm.region.counts = as.data.frame(normalize_counts(pM.raw.counts, pM.cond, pM.diff.levels,
"../r_results/predict_correlated_deg/pM_region_dds.rds")) %>%
mutate(region_names = region.annot$Geneid) %>%
dplyr::select(region_names, all_of(names(.)[names(.) != "region_names"]))
saveRDS(pM.norm.region.counts,
file = "../r_results/predict_correlated_deg/pM_norm_counts_accessibility.rds")
```
Remove the chromatin accessibility master table from memory:
```{r, include=T}
rm(master.table)
```
Upload sets of NFIA-dependent regions:
```{r, include=T}
# Joaquina's parameters for choosing significantly regulated regions
fdr = 0.01
min.l2fc = 2 # Min log2(fold change)
min.baseMean = 100
p1.dep.regions = import(paste0("../r_results/select_diff_regions/p1_dep_ranges",
"_fdr", fdr, "_min-l2fc", min.l2fc, "_min-baseMean", min.baseMean,
".bed"))
p2.dep.regions = import(paste0("../r_results/select_diff_regions/p2_dep_ranges",
"_fdr", fdr, "_min-l2fc", min.l2fc, "_min-baseMean", min.baseMean,
".bed"))
pM.dep.regions = import(paste0("../r_results/select_diff_regions/pM_dep_ranges",
"_fdr", fdr, "_min-l2fc", min.l2fc, "_min-baseMean", min.baseMean,
".bed"))
```
Upload the genome annotation for mm10 that was used for the expression quantification analysis:
```{r, include=T}
# These are actually transcripts, not genes, but I retained the name "genes"
# to track the provenance of this BED from the original gene.bed used in
# expression quantification with the nf-core rnaseq pipeline (gene.bed also
# contains transcripts, not genes).
mm10.tx = read.delim(file = "../results/genes_bed6.bed",
header = F)
names(mm10.tx) = c("chr", "start", "stop", "tx_name", "score", "strand")
mm10.gene2tx = read.delim(file = "../results/mm10_genes_to_transcripts.tsv",
header = F)
names(mm10.gene2tx) = c("gene_name", "tx_name")
mm10.annot.genes = mm10.tx %>%
left_join(mm10.gene2tx,
by = c("tx_name" = "tx_name"))
```
Upload sets of NFIA-dependent genes:
```{r, include=T}
p1.dep.genes = readRDS(file = "../r_results/diff_expression/tables/p1_ref_genes.rds")
p2.dep.genes = readRDS(file = "../r_results/diff_expression/tables/p2_ref_genes.rds")
pM.dep.genes = readRDS(file = "../r_results/diff_expression/tables/pM_ref_genes.rds")
```
Check that all NFIA-dependent genes are present in the genome annotation:
```{r, include=T}
cat("Number of p1 NFIA-dependent genes absent from the annotation:", length(p1.dep.genes) - sum(p1.dep.genes %in% mm10.annot.genes$gene_name), "\n")
cat("Number of p2 NFIA-dependent genes absent from the annotation:", length(p2.dep.genes) - sum(p2.dep.genes %in% mm10.annot.genes$gene_name), "\n")
cat("Number of pM NFIA-dependent genes absent from the annotation:", length(pM.dep.genes) - sum(pM.dep.genes %in% mm10.annot.genes$gene_name), "\n")
```
Generate genomic ranges for the TSSs of the NFIA-dependent genes:
```{r, include=T}
p1.dep.tss.ranges = generate_tss_annot(p1.dep.genes,
"../r_results/predict_correlated_deg/p1_dep_genes_tss.bed")
p2.dep.tss.ranges = generate_tss_annot(p2.dep.genes,
"../r_results/predict_correlated_deg/p2_dep_genes_tss.bed")
pM.dep.tss.ranges = generate_tss_annot(pM.dep.genes,
"../r_results/predict_correlated_deg/pM_dep_genes_tss.bed")
```
Upload mm10 chromosome sizes:
```{r, include=T}
mm10.chr.sizes.df = read.delim(file = "../input/mm10.chrom.sizes",
header = F,
sep = "\t")
mm10.chr.sizes = mm10.chr.sizes.df$V2
names(mm10.chr.sizes) = mm10.chr.sizes.df$V1
```
Generate the region-gene assignment areas as vicinities of a fixed radius around the NFIA-dependent regions:
```{r, include=T}
vicinity.radius = 500 # kbp
p1.vicinity.radius = generate_vicinity_radius(p1.dep.regions,
vicinity.radius,
paste0("../r_results/predict_correlated_deg/p1_vicinities_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".bed"))
p2.vicinity.radius = generate_vicinity_radius(p2.dep.regions,
vicinity.radius,
paste0("../r_results/predict_correlated_deg/p2_vicinities_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".bed"))
pM.vicinity.radius = generate_vicinity_radius(pM.dep.regions,
vicinity.radius,
paste0("../r_results/predict_correlated_deg/pM_vicinities_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".bed"))
```
Upload the hierarchical TAD annotation:
```{r, include=T}
tads = import("../input/10_selected_tads_no-emply-line.bed")
```
Assign the largest size-selected TAD to each NFIA-dependent region as its region-gene assignment area:
```{r, include=T}
min.tad.size = 500 # kbp
max.tad.size = 1500 # kbp
p1.vicinity.tad = generate_vicinity_tad(min.tad.size,
max.tad.size,
p1.dep.regions,
"p1",
paste0("../r_results/predict_correlated_deg/p1_vicinities_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".bed"))
p2.vicinity.tad = generate_vicinity_tad(min.tad.size,
max.tad.size,
p2.dep.regions,
"p2",
paste0("../r_results/predict_correlated_deg/p2_vicinities_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".bed"))
pM.vicinity.tad = generate_vicinity_tad(min.tad.size,
max.tad.size,
pM.dep.regions,
"pM",
paste0("../r_results/predict_correlated_deg/pM_vicinities_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".bed"))
```
Calculate Pearson correlation coefficients (PCCs) between NFIA-dependent regions and NFIA-dependent genes within the regions' assignment areas defined by a fixed radius:
```{r, include=T}
p1.corr.radius = calc_correlations(p1.vicinity.radius, p1.dep.tss.ranges,
p1.norm.region.counts, p1.norm.gene.counts,
p1.region.sample.names, p1.gene.sample.names,
"p1",
"target",
paste0("../r_results/predict_correlated_deg/p1_corr_in_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds"))
p2.corr.radius = calc_correlations(p2.vicinity.radius, p2.dep.tss.ranges,
p2.norm.region.counts, p2.norm.gene.counts,
p2.region.sample.names, p2.gene.sample.names,
"p2",
"target",
paste0("../r_results/predict_correlated_deg/p2_corr_in_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds"))
pM.corr.radius = calc_correlations(pM.vicinity.radius, pM.dep.tss.ranges,
pM.norm.region.counts, pM.norm.gene.counts,
pM.region.sample.names, pM.gene.sample.names,
"pM",
"target",
paste0("../r_results/predict_correlated_deg/pM_corr_in_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds"))
```
Calculate Pearson correlation coefficients (PCCs) between NFIA-dependent regions and NFIA-dependent genes within the regions' assignment areas defined as the largest size-selected TADs containing the regions:
```{r, include=T}
p1.corr.tad = calc_correlations(p1.vicinity.tad, p1.dep.tss.ranges,
p1.norm.region.counts, p1.norm.gene.counts,
p1.region.sample.names, p1.gene.sample.names,
"p1",
"target",
paste0("../r_results/predict_correlated_deg/p1_corr_in_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds"))
p2.corr.tad = calc_correlations(p2.vicinity.tad, p2.dep.tss.ranges,
p2.norm.region.counts, p2.norm.gene.counts,
p2.region.sample.names, p2.gene.sample.names,
"p2",
"target",
paste0("../r_results/predict_correlated_deg/p2_corr_in_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds"))
pM.corr.tad = calc_correlations(pM.vicinity.tad, pM.dep.tss.ranges,
pM.norm.region.counts, pM.norm.gene.counts,
pM.region.sample.names, pM.gene.sample.names,
"pM",
"target",
paste0("../r_results/predict_correlated_deg/pM_corr_in_largest_tad_",
min.tad.size, "-", max.tad.size, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds"))
```
Generate background PCC distributions:
```{r, include=T}
p1.bkgd.radius = calc_correlations(p1.vicinity.radius, p1.dep.tss.ranges,
p1.norm.region.counts, p1.norm.gene.counts,
p1.region.sample.names, p1.gene.sample.names,
"p1",
"background",
paste0("../r_results/predict_correlated_deg/p1_bkgd_in_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, ".rds"))
```
Draw the background absolute PCC distribution:
```{r, include=T}
p = ggplot(p1.bkgd.radius) +
geom_histogram(aes(x = abs_pcc),
binwidth = 0.05) +
theme_classic()
ggsave(filename = paste0("../r_results/predict_correlated_deg/plots/p1_bkgd_in_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, "_hist.pdf"),
plot = p)
```
First of all, many randomly paired regions and genes are very well correlated. Secondly, there is an intriguing peak around |PCC| = 0.5 which is not readily explained.
Hence, try to investigate how this peak could be formed. First, check if it is produced by the sum of positive and negative PCCs by looking at the PCC distribution:
```{r, include=T}
p = ggplot(p1.bkgd.radius) +
geom_histogram(aes(x = pcc),
binwidth = 0.05) +
theme_classic()
ggsave(filename = paste0("../r_results/predict_correlated_deg/plots/p1_bkgd_in_radius_",
vicinity.radius, "kbp_dep_regions_fdr", fdr, "_min-l2fc", min.l2fc,
"_min-baseMean", min.baseMean, "_hist_orig.pdf"),
plot = p)
```
The 0.5 peak is produced almost only by the positive PCCs.
Check the number of significantly downregulated NFIA-dependent genes in p1, in WT-D11 vs WT-D7:
```{r}
p1.ref.degs = readRDS("../r_results/diff_expression/tables/p1_ref_genes.rds")
p1.all.degs = readRDS("../r_results/diff_expression/tables/deseq-results_p1-WT-D11-NFIAp_vs_p1-WT-D7-NFIAn.rds")
as.data.frame(p1.all.degs) %>%
rownames_to_column(var = "gene_name") %>%
filter(gene_name %in% p1.ref.degs) %>%
arrange(log2FoldChange) %>%
filter(log2FoldChange < 0)
```
There are only three such genes. This explains the small number of negative PCCs then (3 * 577 = 1731, and the plot shows roughly this total number of negative PCCs).
Check the number of downregulated reference genes in the other two domains:
```{r, include=T}
p2.ref.degs = readRDS("../r_results/diff_expression/tables/p2_ref_genes.rds")
p2.all.degs = readRDS("../r_results/diff_expression/tables/deseq-results_p2-WT-D11-NFIAp_vs_p2-WT-D7-NFIAn.rds")
as.data.frame(p2.all.degs) %>%
rownames_to_column(var = "gene_name") %>%
filter(gene_name %in% p2.ref.degs) %>%
arrange(log2FoldChange) %>%
filter(log2FoldChange < 0)
pM.ref.degs = readRDS("../r_results/diff_expression/tables/pM_ref_genes.rds")
pM.all.degs = readRDS("../r_results/diff_expression/tables/deseq-results_pM-WT-D11-NFIAp_vs_pM-WT-D7-NFIAn.rds")
as.data.frame(pM.all.degs) %>%
rownames_to_column(var = "gene_name") %>%
filter(gene_name %in% pM.ref.degs) %>%
arrange(log2FoldChange) %>%
filter(log2FoldChange < 0)
```
Simulate PCCs between 3-value vectors (representing three batches of close replicates in each vector) with the fixed first and third values:
```{r, include=T}
sim.gene.df = data.frame(a = 20, b = runif(50, min = 0, max = 150), c = 100)
sim.region.df = data.frame(a = 200, b = runif(600, min = 50, max = 1500), c = 1000)
simulated.pccs = c()
for (i in 1:600) {
for (j in 1:50) {
simulated.pccs = c(simulated.pccs, cor(x = t(sim.region.df[i, ]),
y = t(sim.gene.df[j, ]),
method = "pearson"))
}
}
p = ggplot(data.frame(pcc = simulated.pccs)) +
geom_histogram(aes(x = pcc),
binwidth = 0.05) +
theme_classic()
ggsave(filename = "../r_results/predict_correlated_deg/plots/simulated_pcc_hist_random_day9.pdf",
plot = p)