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predict_closest_deg.Rmd
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predict_closest_deg.Rmd
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---
title: "predict_closest_deg"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
dyn.load("/home/rstudio/libs/libxml2.so.2")
library(dplyr)
library(GenomicRanges)
dyn.load("/home/rstudio/libs/libgsl.so.25")
dyn.load("/home/rstudio/libs/libgslcblas.so.0")
library(tibble)
library(Biostrings)
library(hypeR)
library(rtracklayer)
library(ggplot2)
```
Set constant FDRs:
```{r, include=T}
fdr.degs = 0.05
fdr.matches = 0.05
```
Prepare a function to generate TSS coordinates from transcript coordinates:
```{r, include=T}
generate_tss = function(tx.ranges, tss.bed.filename) {
tss.ranges = GRanges(seqnames = seqnames(tx.ranges),
ranges = IRanges(start = ifelse(strand(tx.ranges) == "+",
start(ranges(tx.ranges)),
end(ranges(tx.ranges)) - 1),
end = ifelse(strand(tx.ranges) == "+",
start(ranges(tx.ranges)) + 1,
end(ranges(tx.ranges))),
names = names(ranges(tx.ranges))),
strand = strand(tx.ranges),
gene_name = tx.ranges$gene_name)
export(tss.ranges, tss.bed.filename)
return(tss.ranges)
}
```
Prepare a general function to generate predictions:
```{r, include=T}
predict_targets = function(cutoff.list, domain.name) {
fdr.regions = unlist(cutoff.list)["fdr.regions"]
min.l2fc = unlist(cutoff.list)["min.l2fc"]
min.baseMean = unlist(cutoff.list)["min.baseMean"]
p.dep.nfia.ranges = readRDS(paste0("../r_results/select_diff_regions/",
domain.name, "_dep_nfia_ranges_with_sites_plus-strand_80pc_match-fdr_", fdr.matches,
"_in-regions-of_fdr", fdr.regions, "_min-l2fc", min.l2fc, "_min-baseMean", min.baseMean, ".rds"))
if (domain.name == "p1") {
p.deg.tss.ranges = p1.deg.tss.ranges
} else if (domain.name == "p2") {
p.deg.tss.ranges = p2.deg.tss.ranges
} else if (domain.name == "pM") {
p.deg.tss.ranges = pM.deg.tss.ranges
} else {
cat("Unknown domain!")
p.deg.tss.ranges = NULL
}
p.nfia.targets.nums = GenomicRanges::nearest(p.dep.nfia.ranges, p.deg.tss.ranges, ignore.strand = T)
p.nfia.targets = p.deg.tss.ranges[p.nfia.targets.nums]$gene_name
p.nfia.targets.dist = GenomicRanges::distanceToNearest(p.dep.nfia.ranges, p.deg.tss.ranges, ignore.strand = T)
relative.location.df = data.frame(region_start = start(ranges(p.dep.nfia.ranges)),
region_end = end(ranges(p.dep.nfia.ranges)),
tss_coord = start(ranges(p.deg.tss.ranges[p.nfia.targets.nums])),
tss_strand = strand(p.deg.tss.ranges[p.nfia.targets.nums])) %>%
mutate(region_to_target = ifelse(tss_coord >= region_start & tss_coord <= region_end, "overlap",
ifelse(tss_strand == "+",
ifelse(tss_coord > region_end, "upstream", "downstream"),
ifelse(tss_coord > region_end, "downstream", "upstream"))))
return(data.frame(regions = names(p.dep.nfia.ranges),
targets = p.nfia.targets,
dist_to_target = p.nfia.targets.dist,
region_to_target = relative.location.df$region_to_target))
}
```
Prepare a function to select predictions within TADs:
```{r, include=T}
fragments_to_granges = function(regions.tss.fragments) {
return(GRanges(seqnames = regions.tss.fragments$seqnames,
ranges = IRanges(start = regions.tss.fragments$start,
end = regions.tss.fragments$end,
names = regions.tss.fragments$fragment_names),
strand = strand(Rle("+"))))
}
predict_targets_in_tads = function(cutoff.list, p.df, domain.name) {
fdr.regions = unlist(cutoff.list)["fdr.regions"]
min.l2fc = unlist(cutoff.list)["min.l2fc"]
min.baseMean = unlist(cutoff.list)["min.baseMean"]
p.dep.nfia.ranges = readRDS(paste0("../r_results/select_diff_regions/",
domain.name, "_dep_nfia_ranges_with_sites_plus-strand_80pc_match-fdr_", fdr.matches,
"_in-regions-of_fdr", fdr.regions, "_min-l2fc", min.l2fc, "_min-baseMean", min.baseMean, ".rds"))
if (domain.name == "p1") {
p.deg.tss.ranges = p1.deg.tss.ranges
} else if (domain.name == "p2") {
p.deg.tss.ranges = p2.deg.tss.ranges
} else if (domain.name == "pM") {
p.deg.tss.ranges = pM.deg.tss.ranges
} else {
cat("Unknown domain!")
p.deg.tss.ranges = NULL
}
p.regions.tss.fragments = p.df %>%
dplyr::select(regions, targets) %>%
left_join(as.data.frame(p.dep.nfia.ranges) %>%
rownames_to_column(var = "region_id"),
by = c("regions" = "region_id")) %>%
dplyr::select(-width,
-strand) %>%
dplyr::rename("seqnames_regions" = "seqnames",
"start_regions" = "start",
"end_regions" = "end") %>%
left_join(as.data.frame(p.deg.tss.ranges) %>%
rownames_to_column(var = "tx_id") %>%
dplyr::select(-width,
-strand) %>%
dplyr::rename("seqnames_targets" = "seqnames",
"start_targets" = "start",
"end_targets" = "end"),
by = c("targets" = "gene_name")) %>%
rowwise() %>%
mutate(start_fragments = min(start_regions, start_targets),
end_fragments = max(end_regions, end_targets)) %>%
ungroup() %>%
dplyr::select(regions,
targets,
seqnames_regions,
start_fragments,
end_fragments) %>%
dplyr::rename("seqnames_fragments" = "seqnames_regions") %>%
rowwise() %>%
mutate(fragment_names = paste0(regions, "|", targets)) %>%
ungroup() %>%
dplyr::select(-regions,
-targets) %>%
dplyr::select(fragment_names,
seqnames_fragments,
start_fragments,
end_fragments) %>%
dplyr::rename("seqnames" = "seqnames_fragments",
"start" = "start_fragments",
"end" = "end_fragments") %>%
distinct()
p.fragment.ranges = fragments_to_granges(p.regions.tss.fragments)
export(p.fragment.ranges,
paste0("../r_results/predict_closest_deg/",
domain.name, "_fragment_ranges_with_sites_plus-strand_80pc_match-fdr_", fdr.matches,
"_in-regions-of_fdr", fdr.regions, "_min-l2fc", min.l2fc, "_min-baseMean", min.baseMean,
"in_tads_of0.5-1.5Mbp.bed"))
p.in.selected.tads = GenomicRanges::findOverlaps(query = p.fragment.ranges,
subject = tads.size.selected,
type = "within",
select = "all",
ignore.strand = T)
p.predictions.within.selected.tads = p.fragment.ranges[unique(queryHits(p.in.selected.tads))]
export(p.predictions.within.selected.tads,
paste0("../r_results/predict_closest_deg/",
domain.name, "_predictions_within_selected_tads_with_sites_plus-strand_80pc_match-fdr_", fdr.matches,
"_in-regions-of_fdr", fdr.regions, "_min-l2fc", min.l2fc, "_min-baseMean", min.baseMean,
"in_tads_of0.5-1.5Mbp.bed"))
return(p.predictions.within.selected.tads)
}
```
Prepare a general function to summarise predictions:
```{r, include=T}
summarise_predictions = function(predictions.df, domain.name) {
if (domain.name == "p1") {
p.deg.annot.ranges = p1.deg.annot.ranges
p.ref.genes = p1.ref.genes
} else if (domain.name == "p2") {
p.deg.annot.ranges = p2.deg.annot.ranges
p.ref.genes = p2.ref.genes
} else if (domain.name == "pM") {
p.deg.annot.ranges = pM.deg.annot.ranges
p.ref.genes = pM.ref.genes
} else {
cat("Unknown domain!")
p.deg.annot.ranges = NULL
p.ref.genes = NULL
}
degs = unique(p.deg.annot.ranges$gene_name)
pred = unique(predictions.df$targets)
n.degs = length(degs)
n.pred = length(pred)
n.refs = length(p.ref.genes)
n.prrf = length(p.ref.genes[p.ref.genes %in% pred])
cat("Predicted targets / All DEGs (expected proportion of predicted ref. genes):",
n.pred, "/", n.degs, "(", round(n.pred / n.degs * 100, 2), "% )\n")
cat("Predicted reference genes / All reference genes (observed proportion of predicted ref. genes):",
n.prrf, "/", n.refs, "(", round(n.prrf / n.refs * 100, 2), "% )\n")
cat("Enrichment of reference genes among predicted ones (observed / expected):",
round((n.prrf / n.refs) / (n.pred / n.degs), 2), "\n")
hyp.obj = hypeR(signature = p.ref.genes,
genesets = list("ALL_PREDICTED" = pred,
"NON_PREDICTED" = degs[!degs %in% pred]),
test = "hypergeometric",
background = unique(p.deg.annot.ranges$gene_name), # all DEGs
fdr = 0.05)
n.regions = nrow(predictions.df)
n.proximal.regions = nrow(predictions.df %>%
filter(region_to_target %in% c("upstream", "overlap")) %>%
filter(dist_to_target.distance <= 2000))
n.proximal.to.ref.regions = nrow(predictions.df %>%
filter(region_to_target %in% c("upstream", "overlap")) %>%
filter(dist_to_target.distance <= 2000) %>%
filter(targets %in% p.ref.genes))
cat("Total number of regions:", n.regions, "\n")
cat("Number of regions proximal to their predicted targets:", n.proximal.regions, "(", round(n.proximal.regions / n.regions * 100, 2), "% )", "\n")
cat("Number of regions proximal to their predicted reference targets:", n.proximal.to.ref.regions, "\n")
return(hyp.obj)
}
```
Prepare a function to summarise predictions within TADs:
```{r, include=T}
summarise_predictions_in_tads = function(predictions.ranges, domain.name) {
if (domain.name == "p1") {
p.deg.annot.ranges = p1.deg.annot.ranges
p.ref.genes = p1.ref.genes
} else if (domain.name == "p2") {
p.deg.annot.ranges = p2.deg.annot.ranges
p.ref.genes = p2.ref.genes
} else if (domain.name == "pM") {
p.deg.annot.ranges = pM.deg.annot.ranges
p.ref.genes = pM.ref.genes
} else {
cat("Unknown domain!")
p.deg.annot.ranges = NULL
p.ref.genes = NULL
}
degs = unique(p.deg.annot.ranges$gene_name)
pred = unique(unname(t(as.data.frame(strsplit(names(predictions.ranges), "|", fixed = T)))[, 2]))
n.degs = length(degs)
n.pred = length(pred)
n.refs = length(p.ref.genes)
n.prrf = length(p.ref.genes[p.ref.genes %in% pred])
cat("Predicted targets / All DEGs (expected proportion of predicted ref. genes):",
n.pred, "/", n.degs, "(", round(n.pred / n.degs * 100, 2), "% )\n")
cat("Predicted reference genes / All reference genes (observed proportion of predicted ref. genes):",
n.prrf, "/", n.refs, "(", round(n.prrf / n.refs * 100, 2), "% )\n")
cat("Enrichment of reference genes among predicted ones (observed / expected):",
round((n.prrf / n.refs) / (n.pred / n.degs), 2), "\n")
n.regions = length(unique(unname(t(as.data.frame(strsplit(names(predictions.ranges), "|", fixed = T)))[, 1])))
cat("Total number of regions:", n.regions, "\n")
hyp.obj = hypeR(signature = p.ref.genes,
genesets = list("ALL_PREDICTED" = pred,
"NON_PREDICTED" = degs[!degs %in% pred]),
test = "hypergeometric",
background = unique(p.deg.annot.ranges$gene_name), # all DEGs
fdr = 0.05)
return(hyp.obj)
}
```
## Form and check mm10 gene annotation
Upload the genome annotation for mm10 that was used for the expression quantification analysis:
```{r, include=T}
# These are actually transcripts, not genes, but I retained the name "genes"
# to track the provenance of this BED from the original gene.bed used in
# expression quantification with the nf-core rnaseq pipeline (gene.bed also
# contains transcripts, not genes).
mm10.tx = read.delim(file = "../results/genes_bed6.bed",
header = F)
names(mm10.tx) = c("chr", "start", "stop", "tx_name", "score", "strand")
mm10.gene2tx = read.delim(file = "../results/mm10_genes_to_transcripts.tsv",
header = F)
names(mm10.gene2tx) = c("gene_name", "tx_name")
mm10.annot.genes = mm10.tx %>%
left_join(mm10.gene2tx,
by = c("tx_name" = "tx_name"))
```
Check that all reference genes are present in the annotation:
```{r, include=T}
p1.ref.genes = readRDS("../r_results/diff_expression/tables/p1_ref_genes.rds")
p2.ref.genes = readRDS("../r_results/diff_expression/tables/p2_ref_genes.rds")
pM.ref.genes = readRDS("../r_results/diff_expression/tables/pM_ref_genes.rds")
cat("Number of p1 reference genes absent from the annotation:", length(p1.ref.genes) - sum(p1.ref.genes %in% mm10.annot.genes$gene_name), "\n")
cat("Number of p2 reference genes absent from the annotation:", length(p2.ref.genes) - sum(p2.ref.genes %in% mm10.annot.genes$gene_name), "\n")
cat("Number of pM reference genes absent from the annotation:", length(pM.ref.genes) - sum(pM.ref.genes %in% mm10.annot.genes$gene_name), "\n")
```
All reference genes are present, as expected.
## Find genomic coordinates of DEGs from WT-D11 vs WT-D7
Find the genomic coordinates of genes differentially expressed between WT-D11 and WT-D7 in p1, p2 and pMN and then find the coordinates of their TSSs:
```{r, include = T}
p1.res = as.data.frame(readRDS("../r_results/diff_expression/tables/deseq-results_p1-WT-D11-NFIAp_vs_p1-WT-D7-NFIAn.rds"))
p1.res.sign.gene_names = p1.res %>%
filter(padj < fdr.degs) %>%
rownames_to_column(var = "gene_name") %>%
pull(gene_name)
p1.deg.annot = mm10.annot.genes[mm10.annot.genes$gene_name %in% p1.res.sign.gene_names, ]
p1.deg.annot.ranges = GRanges(seqnames = p1.deg.annot$chr,
ranges = IRanges(start = p1.deg.annot$start,
end = p1.deg.annot$stop,
names = p1.deg.annot$tx_name),
strand = strand(p1.deg.annot$strand),
gene_name = p1.deg.annot$gene_name)
p1.deg.tss.ranges = generate_tss(p1.deg.annot.ranges, "../r_results/predict_closest_deg/p1_deg_tss.bed")
p1.ref.tss.ranges = p1.deg.tss.ranges[p1.deg.tss.ranges$gene_name %in% p1.ref.genes]
export(p1.ref.tss.ranges, "../r_results/predict_closest_deg/p1_ref_tss.bed")
p2.res = as.data.frame(readRDS("../r_results/diff_expression/tables/deseq-results_p2-WT-D11-NFIAp_vs_p2-WT-D7-NFIAn.rds"))
p2.res.sign.gene_names = p2.res %>%
filter(padj < fdr.degs) %>%
rownames_to_column(var = "gene_name") %>%
pull(gene_name)
p2.deg.annot = mm10.annot.genes[mm10.annot.genes$gene_name %in% p2.res.sign.gene_names, ]
p2.deg.annot.ranges = GRanges(seqnames = p2.deg.annot$chr,
ranges = IRanges(start = p2.deg.annot$start,
end = p2.deg.annot$stop,
names = p2.deg.annot$tx_name),
strand = strand(p2.deg.annot$strand),
gene_name = p2.deg.annot$gene_name)
p2.deg.tss.ranges = generate_tss(p2.deg.annot.ranges, "../r_results/predict_closest_deg/p2_deg_tss.bed")
p2.ref.tss.ranges = p2.deg.tss.ranges[p2.deg.tss.ranges$gene_name %in% p2.ref.genes]
export(p2.ref.tss.ranges, "../r_results/predict_closest_deg/p2_ref_tss.bed")
pM.res = as.data.frame(readRDS("../r_results/diff_expression/tables/deseq-results_pM-WT-D11-NFIAp_vs_pM-WT-D7-NFIAn.rds"))
pM.res.sign.gene_names = pM.res %>%
filter(padj < fdr.degs) %>%
rownames_to_column(var = "gene_name") %>%
pull(gene_name)
pM.deg.annot = mm10.annot.genes[mm10.annot.genes$gene_name %in% pM.res.sign.gene_names, ]
pM.deg.annot.ranges = GRanges(seqnames = pM.deg.annot$chr,
ranges = IRanges(start = pM.deg.annot$start,
end = pM.deg.annot$stop,
names = pM.deg.annot$tx_name),
strand = strand(pM.deg.annot$strand),
gene_name = pM.deg.annot$gene_name)
pM.deg.tss.ranges = generate_tss(pM.deg.annot.ranges, "../r_results/predict_closest_deg/pM_deg_tss.bed")
pM.ref.tss.ranges = pM.deg.tss.ranges[pM.deg.tss.ranges$gene_name %in% pM.ref.genes]
export(pM.ref.tss.ranges, "../r_results/predict_closest_deg/pM_ref_tss.bed")
```
## Predict NFIA targets in p1, p2 and pMN as closest DEGs
Predict NFIA targets, per domain, with different cutoffs for the region significance:
```{r, include=T}
region.sign.cutoffs.1 = list(fdr.regions = 0.01, min.l2fc = 2, min.baseMean = 100)
region.sign.cutoffs.2 = list(fdr.regions = 0.01, min.l2fc = 2, min.baseMean = 0)
region.sign.cutoffs.3 = list(fdr.regions = 0.01, min.l2fc = 0, min.baseMean = 0)
region.sign.cutoffs.4 = list(fdr.regions = 0.05, min.l2fc = 0, min.baseMean = 0)
# p1
p1.1.df = predict_targets(list(region.sign.cutoffs.1), domain.name = "p1")
p1.2.df = predict_targets(list(region.sign.cutoffs.2), domain.name = "p1")
p1.3.df = predict_targets(list(region.sign.cutoffs.3), domain.name = "p1")
p1.4.df = predict_targets(list(region.sign.cutoffs.4), domain.name = "p1")
p1.1.df.hyp = summarise_predictions(p1.1.df, domain.name = "p1")
p1.2.df.hyp = summarise_predictions(p1.2.df, domain.name = "p1")
p1.3.df.hyp = summarise_predictions(p1.3.df, domain.name = "p1")
p1.4.df.hyp = summarise_predictions(p1.4.df, domain.name = "p1")
hyp_show(p1.1.df.hyp)
hyp_show(p1.2.df.hyp)
hyp_show(p1.3.df.hyp)
hyp_show(p1.4.df.hyp)
saveRDS(p1.1.df, file = "../r_results/predict_closest_deg/tables/p1_stringency1_predictions.rds")
saveRDS(p1.2.df, file = "../r_results/predict_closest_deg/tables/p1_stringency2_predictions.rds")
saveRDS(p1.3.df, file = "../r_results/predict_closest_deg/tables/p1_stringency3_predictions.rds")
saveRDS(p1.4.df, file = "../r_results/predict_closest_deg/tables/p1_stringency4_predictions.rds")
write.table(p1.1.df, file = "../r_results/predict_closest_deg/tables/p1_stringency1_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
write.table(p1.2.df, file = "../r_results/predict_closest_deg/tables/p1_stringency2_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
write.table(p1.3.df, file = "../r_results/predict_closest_deg/tables/p1_stringency3_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
write.table(p1.4.df, file = "../r_results/predict_closest_deg/tables/p1_stringency4_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
# p2
p2.1.df = predict_targets(list(region.sign.cutoffs.1), domain.name = "p2")
p2.2.df = predict_targets(list(region.sign.cutoffs.2), domain.name = "p2")
p2.3.df = predict_targets(list(region.sign.cutoffs.3), domain.name = "p2")
p2.4.df = predict_targets(list(region.sign.cutoffs.4), domain.name = "p2")
p2.1.df.hyp = summarise_predictions(p2.1.df, domain.name = "p2")
p2.2.df.hyp = summarise_predictions(p2.2.df, domain.name = "p2")
p2.3.df.hyp = summarise_predictions(p2.3.df, domain.name = "p2")
p2.4.df.hyp = summarise_predictions(p2.4.df, domain.name = "p2")
hyp_show(p2.1.df.hyp)
hyp_show(p2.2.df.hyp)
hyp_show(p2.3.df.hyp)
hyp_show(p2.4.df.hyp)
saveRDS(p2.1.df, file = "../r_results/predict_closest_deg/tables/p2_stringency1_predictions.rds")
saveRDS(p2.2.df, file = "../r_results/predict_closest_deg/tables/p2_stringency2_predictions.rds")
saveRDS(p2.3.df, file = "../r_results/predict_closest_deg/tables/p2_stringency3_predictions.rds")
saveRDS(p2.4.df, file = "../r_results/predict_closest_deg/tables/p2_stringency4_predictions.rds")
write.table(p2.1.df, file = "../r_results/predict_closest_deg/tables/p2_stringency1_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
write.table(p2.2.df, file = "../r_results/predict_closest_deg/tables/p2_stringency2_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
write.table(p2.3.df, file = "../r_results/predict_closest_deg/tables/p2_stringency3_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
write.table(p2.4.df, file = "../r_results/predict_closest_deg/tables/p2_stringency4_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
# pMN
pM.1.df = predict_targets(list(region.sign.cutoffs.1), domain.name = "pM")
pM.2.df = predict_targets(list(region.sign.cutoffs.2), domain.name = "pM")
pM.3.df = predict_targets(list(region.sign.cutoffs.3), domain.name = "pM")
pM.4.df = predict_targets(list(region.sign.cutoffs.4), domain.name = "pM")
pM.1.df.hyp = summarise_predictions(pM.1.df, domain.name = "pM")
pM.2.df.hyp = summarise_predictions(pM.2.df, domain.name = "pM")
pM.3.df.hyp = summarise_predictions(pM.3.df, domain.name = "pM")
pM.4.df.hyp = summarise_predictions(pM.4.df, domain.name = "pM")
hyp_show(pM.1.df.hyp)
hyp_show(pM.2.df.hyp)
hyp_show(pM.3.df.hyp)
hyp_show(pM.4.df.hyp)
saveRDS(pM.1.df, file = "../r_results/predict_closest_deg/tables/pM_stringency1_predictions.rds")
saveRDS(pM.2.df, file = "../r_results/predict_closest_deg/tables/pM_stringency2_predictions.rds")
saveRDS(pM.3.df, file = "../r_results/predict_closest_deg/tables/pM_stringency3_predictions.rds")
saveRDS(pM.4.df, file = "../r_results/predict_closest_deg/tables/pM_stringency4_predictions.rds")
write.table(pM.1.df, file = "../r_results/predict_closest_deg/tables/pM_stringency1_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
write.table(pM.2.df, file = "../r_results/predict_closest_deg/tables/pM_stringency2_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
write.table(pM.3.df, file = "../r_results/predict_closest_deg/tables/pM_stringency3_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
write.table(pM.4.df, file = "../r_results/predict_closest_deg/tables/pM_stringency4_predictions.tsv",
quote = F, sep = "\t", row.names = F, col.names = F)
```
Select predictions only within a +/-500 kbp areas:
```{r, include=T}
max.dist = 500000 # 500 kbp
# p1
p1.1.df.lim = p1.1.df %>%
filter(dist_to_target.distance <= max.dist)
p1.2.df.lim = p1.2.df %>%
filter(dist_to_target.distance <= max.dist)
p1.3.df.lim = p1.3.df %>%
filter(dist_to_target.distance <= max.dist)
p1.4.df.lim = p1.4.df %>%
filter(dist_to_target.distance <= max.dist)
cat("Number of elements with an assigned closest gene within the limited area-1:", nrow(p1.1.df.lim), "(", round(nrow(p1.1.df.lim) / nrow(p1.1.df) * 100, 2), "% )\n")
cat("Number of elements with an assigned closest gene within the limited area-2:", nrow(p1.2.df.lim), "(", round(nrow(p1.2.df.lim) / nrow(p1.2.df) * 100, 2), "% )\n")
cat("Number of elements with an assigned closest gene within the limited area-3:", nrow(p1.3.df.lim), "(", round(nrow(p1.3.df.lim) / nrow(p1.3.df) * 100, 2), "% )\n")
cat("Number of elements with an assigned closest gene within the limited area-4:", nrow(p1.4.df.lim), "(", round(nrow(p1.4.df.lim) / nrow(p1.4.df) * 100, 2), "% )\n")
p1.1.df.hyp.lim = summarise_predictions(p1.1.df.lim, domain.name = "p1")
p1.2.df.hyp.lim = summarise_predictions(p1.2.df.lim, domain.name = "p1")
p1.3.df.hyp.lim = summarise_predictions(p1.3.df.lim, domain.name = "p1")
p1.4.df.hyp.lim = summarise_predictions(p1.4.df.lim, domain.name = "p1")
hyp_show(p1.1.df.hyp.lim)
hyp_show(p1.2.df.hyp.lim)
hyp_show(p1.3.df.hyp.lim)
hyp_show(p1.4.df.hyp.lim)
# p2
p2.1.df.lim = p2.1.df %>%
filter(dist_to_target.distance <= max.dist)
p2.2.df.lim = p2.2.df %>%
filter(dist_to_target.distance <= max.dist)
p2.3.df.lim = p2.3.df %>%
filter(dist_to_target.distance <= max.dist)
p2.4.df.lim = p2.4.df %>%
filter(dist_to_target.distance <= max.dist)
cat("Number of elements with an assigned closest gene within the limited area-1:", nrow(p2.1.df.lim), "(", round(nrow(p2.1.df.lim) / nrow(p2.1.df) * 100, 2), "% )\n")
cat("Number of elements with an assigned closest gene within the limited area-2:", nrow(p2.2.df.lim), "(", round(nrow(p2.2.df.lim) / nrow(p2.2.df) * 100, 2), "% )\n")
cat("Number of elements with an assigned closest gene within the limited area-3:", nrow(p2.3.df.lim), "(", round(nrow(p2.3.df.lim) / nrow(p2.3.df) * 100, 2), "% )\n")
cat("Number of elements with an assigned closest gene within the limited area-4:", nrow(p2.4.df.lim), "(", round(nrow(p2.4.df.lim) / nrow(p2.4.df) * 100, 2), "% )\n")
p2.1.df.hyp.lim = summarise_predictions(p2.1.df.lim, domain.name = "p2")
p2.2.df.hyp.lim = summarise_predictions(p2.2.df.lim, domain.name = "p2")
p2.3.df.hyp.lim = summarise_predictions(p2.3.df.lim, domain.name = "p2")
p2.4.df.hyp.lim = summarise_predictions(p2.4.df.lim, domain.name = "p2")
hyp_show(p2.1.df.hyp.lim)
hyp_show(p2.2.df.hyp.lim)
hyp_show(p2.3.df.hyp.lim)
hyp_show(p2.4.df.hyp.lim)
# pM
pM.1.df.lim = pM.1.df %>%
filter(dist_to_target.distance <= max.dist)
pM.2.df.lim = pM.2.df %>%
filter(dist_to_target.distance <= max.dist)
pM.3.df.lim = pM.3.df %>%
filter(dist_to_target.distance <= max.dist)
pM.4.df.lim = pM.4.df %>%
filter(dist_to_target.distance <= max.dist)
cat("Number of elements with an assigned closest gene within the limited area-1:", nrow(pM.1.df.lim), "(", round(nrow(pM.1.df.lim) / nrow(pM.1.df) * 100, 2), "% )\n")
cat("Number of elements with an assigned closest gene within the limited area-2:", nrow(pM.2.df.lim), "(", round(nrow(pM.2.df.lim) / nrow(pM.2.df) * 100, 2), "% )\n")
cat("Number of elements with an assigned closest gene within the limited area-3:", nrow(pM.3.df.lim), "(", round(nrow(pM.3.df.lim) / nrow(pM.3.df) * 100, 2), "% )\n")
cat("Number of elements with an assigned closest gene within the limited area-4:", nrow(pM.4.df.lim), "(", round(nrow(pM.4.df.lim) / nrow(pM.4.df) * 100, 2), "% )\n")
pM.1.df.hyp.lim = summarise_predictions(pM.1.df.lim, domain.name = "pM")
pM.2.df.hyp.lim = summarise_predictions(pM.2.df.lim, domain.name = "pM")
pM.3.df.hyp.lim = summarise_predictions(pM.3.df.lim, domain.name = "pM")
pM.4.df.hyp.lim = summarise_predictions(pM.4.df.lim, domain.name = "pM")
hyp_show(pM.1.df.hyp.lim)
hyp_show(pM.2.df.hyp.lim)
hyp_show(pM.3.df.hyp.lim)
hyp_show(pM.4.df.hyp.lim)
```
Select examples of assigned and unassigned reference genes in the closest-DEG results:
```{r, include=T}
p1.1.df = readRDS("../r_results/predict_closest_deg/tables/p1_stringency1_predictions.rds")
p1.2.df = readRDS("../r_results/predict_closest_deg/tables/p1_stringency2_predictions.rds")
p1.3.df = readRDS("../r_results/predict_closest_deg/tables/p1_stringency3_predictions.rds")
p1.4.df = readRDS("../r_results/predict_closest_deg/tables/p1_stringency4_predictions.rds")
p1.ref.genes = readRDS("../r_results/diff_expression/tables/p1_ref_genes.rds")
p1.1.ref.paired = p1.ref.genes[p1.ref.genes %in% unique(p1.1.df$targets)]
p1.2.ref.paired = p1.ref.genes[p1.ref.genes %in% unique(p1.2.df$targets)]
p1.3.ref.paired = p1.ref.genes[p1.ref.genes %in% unique(p1.3.df$targets)]
p1.4.ref.paired = p1.ref.genes[p1.ref.genes %in% unique(p1.4.df$targets)]
p1.1.ref.unpaired = p1.ref.genes[!p1.ref.genes %in% unique(p1.1.df$targets)]
p1.2.ref.unpaired = p1.ref.genes[!p1.ref.genes %in% unique(p1.2.df$targets)]
p1.3.ref.unpaired = p1.ref.genes[!p1.ref.genes %in% unique(p1.3.df$targets)]
p1.4.ref.unpaired = p1.ref.genes[!p1.ref.genes %in% unique(p1.4.df$targets)]
# Assigned
# Mmd2 from p1.2.ref.paired[!p1.2.ref.paired %in% p1.1.ref.paired]
# Notch2 from p1.3.ref.paired[!p1.3.ref.paired %in% p1.2.ref.paired]
# Unassigned
# Timp4 and Hoxa9 from p1.4.ref.unpaired
```
Select examples of assigned and unassigned reference genes in the closest-DEG-within-500kbp results:
```{r, include=T}
p1.1.df.lim = p1.1.df %>%
filter(dist_to_target.distance <= max.dist)
p1.2.df.lim = p1.2.df %>%
filter(dist_to_target.distance <= max.dist)
p1.3.df.lim = p1.3.df %>%
filter(dist_to_target.distance <= max.dist)
p1.4.df.lim = p1.4.df %>%
filter(dist_to_target.distance <= max.dist)
p1.1.ref.paired = p1.ref.genes[p1.ref.genes %in% unique(p1.1.df.lim$targets)]
p1.2.ref.paired = p1.ref.genes[p1.ref.genes %in% unique(p1.2.df.lim$targets)]
p1.3.ref.paired = p1.ref.genes[p1.ref.genes %in% unique(p1.3.df.lim$targets)]
p1.4.ref.paired = p1.ref.genes[p1.ref.genes %in% unique(p1.4.df.lim$targets)]
p1.1.ref.unpaired = p1.ref.genes[!p1.ref.genes %in% unique(p1.1.df.lim$targets)]
p1.2.ref.unpaired = p1.ref.genes[!p1.ref.genes %in% unique(p1.2.df.lim$targets)]
p1.3.ref.unpaired = p1.ref.genes[!p1.ref.genes %in% unique(p1.3.df.lim$targets)]
p1.4.ref.unpaired = p1.ref.genes[!p1.ref.genes %in% unique(p1.4.df.lim$targets)]
# Assigned
# Fam198b from p1.1.ref.paired
# Mmd2 from p1.2.ref.paired[!p1.2.ref.paired %in% p1.1.ref.paired]
# Unassigned
# Timp4 and Hoxa9 from p1.4.ref.unpaired
```
Select examples of assigned and unassigned elements in the closest-DEG-within-500kbp results:
```{r, include=T}
p1.1.elements = readRDS("../r_results/select_diff_regions/p1_dep_nfia_ranges_with_sites_plus-strand_80pc_match-fdr_0.05_in-regions-of_fdr0.01_min-l2fc2_min-baseMean100.rds")
# Assigned
# Interval_2084 from p1.1.df.lim$regions
# Interval_1769 from p1.2.df.lim$regions[!p1.2.df.lim$regions %in% p1.1.df.lim$regions]
# Unassigned
# Interval_1289 and Interval_3723 from head(names(p1.1.elements)[!names(p1.1.elements) %in% p1.1.df.lim$regions])
```
Predict NFIA targets within TADs in p1:
```{r, include=T}
tads = rtracklayer::import("../input/10_selected_tads_no-emply-line.bed")
tads.size.selected = tads[end(ranges(tads)) - start(ranges(tads)) >= 500000 &
end(ranges(tads)) - start(ranges(tads)) <= 1500000]
export(tads.size.selected,
"../r_results/predict_closest_deg/tads_size_selected_0.5-1.5Mbp.bed")
p1.1.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.1, p1.1.df, "p1")
p1.1.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(p1.1.predictions.within.selected.tads, "p1")
hyp_show(p1.1.predictions.within.selected.tads.hyp)
p1.2.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.2, p1.2.df, "p1")
p1.2.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(p1.2.predictions.within.selected.tads, "p1")
hyp_show(p1.2.predictions.within.selected.tads.hyp)
p1.3.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.3, p1.3.df, "p1")
p1.3.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(p1.3.predictions.within.selected.tads, "p1")
hyp_show(p1.3.predictions.within.selected.tads.hyp)
p1.4.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.4, p1.4.df, "p1")
p1.4.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(p1.4.predictions.within.selected.tads, "p1")
hyp_show(p1.4.predictions.within.selected.tads.hyp)
```
Predict NFIA targets within TADs in p2:
```{r, include=T}
p2.1.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.1, p2.1.df, "p2")
p2.1.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(p2.1.predictions.within.selected.tads, "p2")
hyp_show(p2.1.predictions.within.selected.tads.hyp)
p2.2.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.2, p2.2.df, "p2")
p2.2.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(p2.2.predictions.within.selected.tads, "p2")
hyp_show(p2.2.predictions.within.selected.tads.hyp)
p2.3.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.3, p2.3.df, "p2")
p2.3.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(p2.3.predictions.within.selected.tads, "p2")
hyp_show(p2.3.predictions.within.selected.tads.hyp)
p2.4.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.4, p2.4.df, "p2")
p2.4.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(p2.4.predictions.within.selected.tads, "p2")
hyp_show(p2.4.predictions.within.selected.tads.hyp)
```
Predict NFIA targets within TADs in pMN:
```{r, include=T}
pM.1.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.1, pM.1.df, "pM")
pM.1.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(pM.1.predictions.within.selected.tads, "pM")
hyp_show(pM.1.predictions.within.selected.tads.hyp)
pM.2.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.2, pM.2.df, "pM")
pM.2.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(pM.2.predictions.within.selected.tads, "pM")
hyp_show(pM.2.predictions.within.selected.tads.hyp)
pM.3.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.3, pM.3.df, "pM")
pM.3.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(pM.3.predictions.within.selected.tads, "pM")
hyp_show(pM.3.predictions.within.selected.tads.hyp)
pM.4.predictions.within.selected.tads = predict_targets_in_tads(region.sign.cutoffs.4, pM.4.df, "pM")
pM.4.predictions.within.selected.tads.hyp = summarise_predictions_in_tads(pM.4.predictions.within.selected.tads, "pM")
hyp_show(pM.4.predictions.within.selected.tads.hyp)
```
Make comparative bar plots:
```{r, include=T}
group_ids = c(rep("S1", 3), rep("S2", 3), rep("S3", 3), rep("S4", 3))
method_ids = rep(c("Closest", "Closest, +/-500 kbp", "Closest in TAD"), 4)
# p1
# Assigned NFIA elements
p1.elem.plot = data.frame(group_id = group_ids,
method_id = method_ids,
y = c(100, 53.32, 22.9,
100, 45.86, 23.4,
100, 48.1, 24.1,
100, 47.69, 23.6)) %>%
mutate(method_id = factor(method_id, levels = c("Closest", "Closest, +/-500 kbp", "Closest in TAD"))) %>%
ggplot(aes(x = group_id,
y = y,
fill = method_id)) +
geom_col(position = "dodge") +
theme_classic() +
ylab("% of assigned NFIA elements") +
ylim(min = 0, max = 100)
ggsave(filename = "../r_results/predict_closest_deg/plots/p1_elem_barplot.pdf",
plot = p1.elem.plot)
# Assigned reference genes
p1.ref.gene.plot = data.frame(group_id = group_ids,
method_id = method_ids,
y = c(26.67, 13.33, 11.11,
66.67, 57.78, 26.67,
84.44, 82.22, 40,
88.89, 88.89, 40)) %>%
mutate(method_id = factor(method_id, levels = c("Closest", "Closest, +/-500 kbp", "Closest in TAD"))) %>%
ggplot(aes(x = group_id,
y = y,
fill = method_id)) +
geom_col(position = "dodge") +
theme_classic() +
ylab("% of assigned reference genes") +
ylim(min = 0, max = 100)
ggsave(filename = "../r_results/predict_closest_deg/plots/p1_ref_gene_barplot.pdf",
plot = p1.ref.gene.plot)
# Enrichment of predicted genes among reference genes
p1.enrichment.plot = data.frame(group_id = group_ids,
method_id = method_ids,
y = c(1.06, 0.87, 1.68,
0.97, 1.08, 1,
1.01, 1.09, 1.1,
1.02, 1.08, 1.03)) %>%
mutate(method_id = factor(method_id, levels = c("Closest", "Closest, +/-500 kbp", "Closest in TAD"))) %>%
ggplot(aes(x = group_id,
y = y,
fill = method_id)) +
geom_col(position = "dodge") +
geom_hline(yintercept = 1) +
theme_classic() +
ylab("Enrichment of predicted genes among reference genes") +
ylim(min = 0, max = 2)
ggsave(filename = "../r_results/predict_closest_deg/plots/p1_enrichment_barplot.pdf",
plot = p1.enrichment.plot)
# p2
# Assigned NFIA elements
p2.elem.plot = data.frame(group_id = group_ids,
method_id = method_ids,
y = c(100, 57.63, 28.5,
100, 50.67, 25.6,
100, 54.09, 27.2,
100, 53.59, 27.0)) %>%
mutate(method_id = factor(method_id, levels = c("Closest", "Closest, +/-500 kbp", "Closest in TAD"))) %>%
ggplot(aes(x = group_id,
y = y,
fill = method_id)) +
geom_col(position = "dodge") +
theme_classic() +
ylab("% of assigned NFIA elements") +
ylim(min = 0, max = 100)
ggsave(filename = "../r_results/predict_closest_deg/plots/p2_elem_barplot.pdf",
plot = p2.elem.plot)
# Assigned reference genes
p2.ref.gene.plot = data.frame(group_id = group_ids,
method_id = method_ids,
y = c(38.18, 36.36, 16.36,
72.73, 69.09, 25.45,
92.73, 92.73, 36.36,
92.73, 92.73, 36.36)) %>%
mutate(method_id = factor(method_id, levels = c("Closest", "Closest, +/-500 kbp", "Closest in TAD"))) %>%
ggplot(aes(x = group_id,
y = y,
fill = method_id)) +
geom_col(position = "dodge") +
theme_classic() +
ylab("% of assigned reference genes") +
ylim(min = 0, max = 100)
ggsave(filename = "../r_results/predict_closest_deg/plots/p2_ref_gene_barplot.pdf",
plot = p2.ref.gene.plot)