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chip_qc_development.R
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#!/usr/bin/env Rscript
##Based on Joe Boyd's FRIP QC script
##with modifications by Mike Mariani
##for general chip QC. This is a work
##in progress.
##Frietze Lab UVM 2021
library(seqsetvis)
library(data.table)
library(GenomicRanges)
library(ggplot2)
##config_dt = data.table(
## mark_keys = c("ATAD",
## "[0-9]ac_",
## "ATAD2_",
## "ATAD2B_",
## "BRD4_",
## "H3K4me3",
## "H4K12ac",
## "H4K16ac",
## "H4K20ac",
## "H4K5ac",
## "H4K8ac"),
## file_tags = c("all_ATAD",
## "all_acetyl",
## "ATAD2",
## "ATAD2B",
## "BRD4",
## "H3K4me3",
## "H4K12ac",
## "H4K16ac",
## "H4K20ac",
## "H4K5ac",
## "H4K8ac"),
## min_fractions = c(.5),
## min_number = 2,
## view_sizes = c(2e3),
## modes = c("seacr",
## "broad",
## rep("seacr", 3),
## rep("broad", 6))
##)
##mode_vars = list(
## broad = list(
## peak_dir_FUN = function(search_dirs){
## file.path(search_dirs, "macs2.broad.all.frag.aug18")
## },
## peak_file_key = ".+peaks.broadPeak$",
## peak_load_FUN = easyLoad_broadPeak
## ),
## narrow = list(
## peak_dir_FUN = function(search_dirs){
## file.path(search_dirs, "macs2.narrow.all.frag.aug18")
## },
## peak_file_key = ".+peaks.narrowPeak$",
## peak_load_FUN = easyLoad_narrowPeak
## ),
## seacr = list(
## peak_dir_FUN = function(search_dirs){
## file.path(search_dirs, "seacr.aug12.all.frag")
## },
## peak_file_key = ".stringent.bed$",
## peak_load_FUN = easyLoad_seacr
## )
##)
make_dt = function(files, group_depth = ifelse(grepl("bam$", files[1]), 3, 2)){
p_dt = data.table(file = files)
p_dt[, name := sub("\\..+", "", basename(file))]
p_dt[, name := sub("(?<=rep[0-9])_.+", "", name, perl = TRUE)]
if(group_depth == 1){
p_dt[, group := basename(dirname(file))]
}else if(group_depth == 2){
p_dt[, group := basename(dirname(dirname(file)))]
}else if(group_depth == 3){
p_dt[, group := basename(dirname(dirname(dirname(file))))]
}
p_dt[, batch := LETTERS[as.numeric(factor(group, levels = group_lev))]]
p_dt[, name := paste0(name, ".", batch)]
dupe_num = 1
if(any(duplicated(p_dt$name))){
p_dt[, dupe_num := seq(.N)-1, .(name)]
p_dt[dupe_num > 0, name := paste0(name, ".", dupe_num) ]
p_dt$dupe_num = NULL
}
# p_dt[, condition := ifelse(grepl("DF", name), "DF", "WT")]
# p_dt[, xlink := ifelse(grepl("[T4]x_", name), "xlinked", "not")]
p_dt[]
}
peaks.files <- list.files(path="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_macs2_output/hg38/narrow",
patter=".narrowPeak",
full.names = TRUE)
bws.files <- list.files(path='/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws',
pattern=".bw$",
full.names = TRUE)
bams.files <- list.files(path="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38",
pattern=".bam$",
full.names = TRUE)
fastq.files <- list.files(path="/slipstream/home/mmariani/projects/vzv_interactions/vzv_ctcf_cohrs_chip_data/trimmomatic_output",
pattern=".fastq.gz",
full.names=TRUE)
length(peaks.files)
length(bws.files)
length(bams.files)
length(fastq.files)
base_dir = "/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_macs2_output/hg38/narrow"
group_lev = factor(basename(base_dir))
##qc_dir = "~/R/qc_cutnrun/qc_output_KQ.high_consensus"
##search_dirs = c("~/cutruntools/workdir_102920_KQ_MCF7.hg38")
## ".neg" indicates input file(s)
##bam_dirs <- file.path(search_dirs, "aligned.aug10/sorted")
##bw_dirs <- file.path(search_dirs, "macs2.narrow.all.frag.aug18")
##bw_dirs.neg <- file.path(search_dirs, "macs2.narrow.all.frag.aug18")
##group_lev = factor(basename(search_dirs))
##add fastq reads
##add total peaks
##stopifnot(config_dt$modes %in% names(mode_vars))
##negative_key = "IgG_"
##negative_tag = "IgG"
##i = 1
##for(i in seq(7, nrow(config_dt))){
## mark_key <- config_dt$mark_keys[i]
## file_tag <- config_dt$file_tags[i]
## min_fraction <- config_dt$min_fractions[i]
## min_number <- config_dt$min_number[i]
## view_size <- config_dt$view_sizes[i]
## mode_var <- mode_vars[[config_dt$modes[i]]]
## peak_dirs <- mode_var$peak_dir_FUN(search_dirs)
## peak_key <- mode_var$peak_file_key
## message(file_tag)
## o_dir = file.path(qc_dir, paste0("qc_", file_tag))
## dir.create(o_dir, showWarnings = FALSE, recursive = TRUE)
## setwd(o_dir)
## ## peak_dirs = file.path(search_dirs, "macs2.narrow.all.frag.aug18")
message("find files...")
## peak_files = dir(peak_dirs, pattern = "[kK]27[aA][cC].+peaks.narrowPeak", full.names = TRUE)
##peak_files = dir(peak_dirs, pattern = paste0(mark_key, ".+", peak_key), full.names = TRUE)
##bam_files = dir(bam_dirs, pattern = paste0(mark_key, ".+bam$"), full.names = TRUE)
##bw_files = dir(bw_dirs, pattern = paste0(mark_key, ".+bw$"), full.names = TRUE)
##fastq_files = c(
## dir(search_dirs, paste0(mark_key, '.+R1_001.fastq.gz$'), full.names = TRUE),
## dir(file.path(search_dirs, 'fastq_done'), paste0(mark_key, '.+R1_001.fastq.gz$'), full.names = TRUE)
##)
peak_files <- peaks.files[c(2,5,6)]
bw_files <- bws.files[c(1,3,5,6,9,10)]
bam_files <- bams.files[c(1,3,5,6,9,10)]
fastq_files <- fastq.files[c(1,3,5,6,9,10)]
peak_files.neg <- peaks.files[c(1,3,4)]
bam_files.neg <- bams.files[c(2,4,7,8,11,12)]
bw_files.neg <- bws.files[c(2,4,7,8,11,12)]
fastq_files.neg <- fastq.files[c(2,4,7,8,11,12)]
##peak_files <- bam_dirs
##bam_files <- bw_dirs
##bw_files <- bw_dirs
##fastq_files <-
##peak_files.neg = dir(peak_dirs, pattern = paste0(negative_key, ".+", peak_key), full.names = TRUE)
##bam_files.neg = dir(bam_dirs, pattern = paste0(negative_key, ".+bam$"), full.names = TRUE)
##bw_files.neg = dir(bw_dirs, pattern = paste0(negative_key, ".+bw$"), full.names = TRUE)
##fastq_files.neg = c(
## dir(search_dirs, paste0(negative_key, '.+R1_001.fastq.gz$'), full.names = TRUE),
## dir(file.path(search_dirs, 'fastq_done'), paste0(negative_key, '.+R1_001.fastq.gz$'), full.names = TRUE)
##)
##list the fastq files:
system("ls -d /slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/* | grep '.bam$'")
bam.files.check <- system("ls -d /slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/* | grep '.bam$'", intern=TRUE)
message("count fastq reads...")
fq_dt = rbindlist(
pbmcapply::pbmclapply(
bam.files.check,
##c(fastq_files, fastq_files.neg),
mc.cores = 10,
function(f){
cnt_f = paste0(f, ".cnt")
## if(TRUE){
if(!file.exists(cnt_f)){
cnt = system(paste0("gunzip -c ", f, "| wc -l"), intern = TRUE)
cnt_dt = data.table(f, as.numeric(cnt)/4)
fwrite(cnt_dt, cnt_f, sep = "\t", col.names = FALSE)
cnt_dt
}else{
fread(cnt_f, sep = "\t")
}
}
)
)
setnames(fq_dt, c("fastq", "count"))
fq_dt.tmp = make_dt(fq_dt$fastq, group_depth = 1)
fq_dt.tmp$fastq_count = fq_dt$count
fq_dt = fq_dt.tmp
fq_dt$file
fq_dt$name <- c("ctcf_mock_rep_1",
"input_mock_rep_1",
"ctcf_infected_rep_1",
"input_infected_rep_1",
"ctcf_mock_rep_2",
"ctcf_mock_rep_3",
"input_mock_rep_2",
"input_mock_rep_3",
"ctcf_infected_rep_2",
"ctcf_infected_rep_3",
"input_infected_rep_2",
"input_infected_rep_3")
fq_dt$batch <- 1
file_tag <- "infected"
negative_tag <- "mock"
p_dt.pos = make_dt(peak_files)
p_dt.pos$treatment = file_tag
p_dt.pos$batch <- "1"
p_dt.pos$name <- c("ctcf_infected_rep_1",
"ctcf_infected_rep_2",
"ctcf_infected_rep_3")
qdt.bam = make_dt(bam_files)
qdt.bam$treatment = file_tag
qdt.bam$name <- c("ctcf_mock_rep_1",
"ctcf_infected_rep_1",
"ctcf_mock_rep_2",
"ctcf_mock_rep_3",
"ctcf_infected_rep_2",
"ctcf_infected_rep_3")
qdt.bam$batch <- 1
qdt.bam$treatment <- c("mock",
"infected",
"mock",
"mock",
"infected",
"infected")
qdt.bw = make_dt(bw_files)
##qdt.bw$treatment = file_tag
qdt.bw$treatment = c("mock",
"infected",
"mock",
"mock",
"infected",
"infected")
qdt.bw$name <- c("ctcf_mock_rep_1",
"ctcf_infected_rep_1",
"ctcf_mock_rep_2",
"ctcf_mock_rep_3",
"ctcf_infected_rep_2",
"ctcf_infected_rep_3")
p_dt.neg = make_dt(peak_files.neg)
p_dt.neg$treatment = negative_tag
p_dt.neg$batch <- 1
p_dt.neg$name <- c("ctcf_mock_rep_1",
"ctcf_mock_rep_2",
"ctcf_mock_rep_3")
qdt.bw.neg = make_dt(bw_files.neg)
qdt.bw.neg$treatment = negative_tag
qdt.bw.neg$name <- c("input_mock_rep_1",
"input_infected_rep_1",
"input_mock_rep_2",
"input_mock_rep_3",
"input_infected_rep_2",
"input_infected_rep_3")
qdt.bam.neg = make_dt(bam_files.neg)
qdt.bam.neg$treatment = negative_tag
qdt.bam.neg$name <- c("input_mock_rep_1",
"input_infected_rep_1",
"input_mock_rep_2",
"input_mock_rep_3",
"input_infected_rep_2",
"input_infected_rep_3")
qdt.bam.neg$batch <- 1
qdt.bam.neg$treatment <- c("mock",
"infected",
"mock",
"mock",
"infected",
"infected")
stopifnot(!any(duplicated(p_dt.pos$name)))
stopifnot(!any(duplicated(p_dt.neg$name)))
message("load peaks...")
##peak_load_FUN = easyLoad_narrowPeak
peak_grs = seqsetvis::easyLoad_narrowPeak(peak_files,
file_names = c("ctcf_infected_rep_1",
"ctcf_infected_rep_2",
"ctcf_infected_rep_3"))
##peak_grs = mode_var$peak_load_FUN(p_dt.pos$file)
names(peak_grs) = factor(p_dt.pos$name, levels=p_dt.pos$name)
lengths(peak_grs)
##S4Vectors::mcols(peak_grs)
peak_dt.pos = ssvFeatureBars(peak_grs, return_data = TRUE)
setnames(peak_dt.pos, c("count", "group"), c("peak_count", "name"))
peak_dt.pos$name <- c("ctcf_infected_rep_1",
"ctcf_infected_rep_2",
"ctcf_infected_rep_3")
peak_dt.pos$name <- factor(peak_dt.pos$name, levels=peak_dt.pos$name)
##peak_grs.neg = mode_var$peak_load_FUN(p_dt.neg$file)
peak_grs.neg = seqsetvis::easyLoad_narrowPeak(peak_files.neg,
file_names = c("ctcf_mock_rep_1",
"ctcf_mock_rep_2",
"ctcf_mock_rep_3"))
names(peak_grs.neg) = factor(p_dt.neg$name, levels = p_dt.neg$name)
lengths(peak_grs.neg)
peak_dt.neg = ssvFeatureBars(peak_grs.neg, return_data = TRUE)
setnames(peak_dt.neg, c("count", "group"), c("peak_count", "name"))
peak_dt.neg$name <- c("ctcf_mock_rep_1",
"ctcf_mock_rep_2",
"ctcf_mock_rep_3")
peak_dt.neg$name <- factor(peak_dt.neg$name, levels=peak_dt.neg$name)
peak_o = rev(order(lengths(peak_grs)))
message("overlap peaks...")
olaps_all = ssvOverlapIntervalSets(peak_grs[peak_o])
p_peak_counts_all = ssvFeatureBars(peak_grs[peak_o],
bar_colors = "black",
show_counts = FALSE,
counts_text_colors = "gray60") +
guides(fill = "none") +
scale_y_continuous(labels = function(x)x/1e3) +
labs(y = "peak count (k)", title = "all peaks", subtitle = paste("counts:", formatC(max(lengths(peak_grs)), big.mark = ",", format = "d"), "max"), x = "")
p_peak_overlaps_all = ssvFeatureBinaryHeatmap(olaps_all,
raster_approximation = TRUE) +
labs(title = "all peaks",
subtitle = paste("overlaps:",
formatC(length(olaps_all),
big.mark = ",",
format = "d"),
"regions"))
##Joe's recommendations for parameters:
##Joe says 0.5 is the same as min_number=3
min_fraction = c(0.5)
min_number = 3
view_size = c(2e3)
message("consensus peaks...")
olaps = ssvConsensusIntervalSets(peak_grs[peak_o],
min_fraction = min_fraction,
min_number = min(min_number,
length(peak_o)))
p_peak_counts_consenus = ssvFeatureBars(olaps,
bar_colors = "black",
show_counts = FALSE,
counts_text_colors = "gray60" ) +
guides(fill = "none") +
scale_y_continuous(labels = function(x)x/1e3) +
labs(y = "peak count (k)",
title = "consensus peaks only",
subtitle = paste("counts:",
formatC(max(colSums(as.data.frame(mcols(olaps)))),
big.mark = ",",
format = "d"), "max"))
p_peak_overlaps_consensus = ssvFeatureBinaryHeatmap(olaps,
raster_approximation = TRUE) +
labs(title = "consensus peaks only",
subtitle = paste("overlaps:",
formatC(length(olaps),
big.mark = ",",
format = "d"),
"regions")
)
pg_peaks = cowplot::plot_grid(p_peak_counts_all +
theme(axis.text.x = element_blank()),
p_peak_overlaps_all +
theme(axis.text.x = element_blank()),
p_peak_counts_consenus,
p_peak_overlaps_consensus,
rel_heights = c(2, 3))
output.dir <- "/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip"
ggsave(paste0(output.dir,
"/peak_numbers.",
file_tag,
".pdf"),
pg_peaks,
width = 8,
height = 9)
olaps = ssvRecipes::sampleCap(olaps, 5e3)
#filter for canonical and no chrM
olaps = subset(olaps, grepl("chr[0-9XY]+$", seqnames ))
options(mc.cores = 20)
qdt.bw$batch <- 1
qdt.bw[, facet := gsub("_", "\n", name)]
message("center query regions...")
query_gr.uncentered = resize(olaps, 5e3, fix = "center")
bw_dt.uncentered = ssvFetchBigwig(qdt.bw,
query_gr.uncentered,
win_size = 100,
win_method = "summary",
return_data.table = TRUE,
n_region_splits = 20)
query_gr = unique(resize(centerGRangesAtMax(bw_dt.uncentered,
query_gr.uncentered),
view_size,
fix = 'center')
)
message("fetch query regions...")
bw_dt = ssvFetchBigwig(bw_files,
##qdt.bw,
query_gr,
win_size = 100,
win_method = "summary",
return_data.table = TRUE,
n_region_splits = 20)
bw_dt = append_ynorm(bw_dt)
##unique(bw_dt$sample)
bw_dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_Control_HFL_AntiCTCF_S31_L005_R1_001.trimmed.R1.sorted_norm_unstranded.bw",name:="ctcf_mock_rep_1",]
bw_dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_VZV_AntiCTCF_S33_L005_R1_001.trimmed.R1.sorted_norm_unstranded.bw",name:="ctcf_infected_rep_1",]
bw_dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/HFL_con_CTCF_set2_S2_L001_R1_001.trimmed.R1.sorted_norm_unstranded.bw",name:="ctcf_mock_rep_2",]
bw_dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/HFL_con_CTCF_set3_S6_L001_R1_001.trimmed.R1.sorted_norm_unstranded.bw",name:="ctcf_mock_rep_3",]
bw_dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/HFL_VZV_CTCF_set2_S4_L001_R1_001.trimmed.R1.sorted_norm_unstranded.bw",name:="ctcf_infected_rep_2",]
bw_dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/HFL_VZV_CTCF_set3_S8_L001_R1_001.trimmed.R1.sorted_norm_unstranded.bw",name:="ctcf_infected_rep_3",]
## set.seed(0)
## clust_dt = ssvSignalClustering(bw_dt, fill_ = "y_norm", max_cols = Inf, facet_ = "facet")
##negative
qdt.bw.neg$batch = 1
qdt.bw.neg[, facet := gsub("_", "\n", name)]
bw_dt.neg = ssvFetchBigwig(bw_files.neg,
##qdt.bw.neg,
query_gr,
win_size = 100,
win_method = "summary",
return_data.table = TRUE,
n_region_splits = 20)
bw_dt.neg = append_ynorm(bw_dt.neg)
##unique(bw_dt.neg$sample)
bw_dt.neg[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_Control_HFL_Input_S30_L005_R1_001.trimmed.R1.sorted_norm_unstranded.bw", name:="input_mock_rep_1",]
bw_dt.neg[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_VZV_Input_S32_L005_R1_001.trimmed.R1.sorted_norm_unstranded.bw", name:="input_infected_rep_1",]
bw_dt.neg[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/HFL_con_in_set2_S1_L001_R1_001.trimmed.R1.sorted_norm_unstranded.bw", name:="input_mock_rep_2",]
bw_dt.neg[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/HFL_con_in_set3_S5_L001_R1_001.trimmed.R1.sorted_norm_unstranded.bw", name:="input_mock_rep_3",]
bw_dt.neg[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/HFL_VZV_in_set2_S3_L001_R1_001.trimmed.R1.sorted_norm_unstranded.bw", name:="input_infected_rep_2",]
bw_dt.neg[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_bowtie2_alignment/hg38/joe_boyd_norm_unstranded_bws/HFL_VZV_in_set3_S7_L001_R1_001.trimmed.R1.sorted_norm_unstranded.bw", name:="input_infected_rep_3",]
bw_dt.all = rbind(bw_dt.neg, bw_dt)
bw_dt.all[, y_relative := y / max(y), .(id)]
#count reads
# Rsamtools::countBam(bam_files[1], which = olaps[1:10])
qdt.bam.all = rbind(qdt.bam, qdt.bam.neg)
qdt.bam.all$batch <- 1
##qdt.bam.all$file
##qdt.bam.all$name <- c("ctcf_mock_rep_1",
## "ctcf_infected_rep_1",
## "ctcf_mock_rep_2",
## "ctcf_mock_rep_3",
## "ctcf_infected_rep_2",
## "ctcf_infected_rep_3",
## "input_mock_rep_1",
## "input_infected_rep_1",
## "input_mock_rep_2",
## "input_mock_rep_3",
## "input_infected_rep_2",
## "input_infected_rep_3")
##Check fq_dt before merging
intersect(fq_dt$name, qdt.bam.all$name)
fq_dt.check = merge(fq_dt, qdt.bam.all[, .(name, treatment)], by = "name")
fq_dt = merge(fq_dt, qdt.bam.all[, .(name, treatment)], by = "name")
##fq_dt$file
peak_dt.pos <- rbind(peak_dt.pos, peak_dt.neg)
peak_dt.neg <- peak_dt.pos
peak_dt.pos$name <- c("ctcf_infected_rep_1",
"ctcf_infected_rep_2",
"ctcf_infected_rep_3",
"ctcf_mock_rep_1",
"ctcf_mock_rep_2",
"ctcf_mock_rep_3")
peak_dt.neg$name <- c("input_infected_rep_1",
"input_infected_rep_2",
"input_infected_rep_3",
"input_mock_rep_1",
"input_mock_rep_2",
"input_mock_rep_3")
peak_dt = merge(rbind(peak_dt.pos,peak_dt.neg),
qdt.bam.all[, .(name, treatment)],
by = "name")
message("fetch read counts...")
##dt = ssvFetchBamPE(qdt.bam.all,
## query_gr,
## return_unprocessed = TRUE,
## n_region_splits = 50)
dt.in = ssvFetchBam(c(bam_files,bam_files.neg),
query_gr,
return_unprocessed = TRUE,
n_region_splits = 50)
dt <- dt.in
##I need to manually add name and treatment fields to dt
basename(as.character(unique(dt.in$sample)))
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_Control_HFL_AntiCTCF_S31_L005_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "ctcf_mock_rep_1",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_VZV_AntiCTCF_S33_L005_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "ctcf_infected_rep_1",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_con_CTCF_set2_S2_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "ctcf_mock_rep_2",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_con_CTCF_set3_S6_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "ctcf_mock_rep_3",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_VZV_CTCF_set2_S4_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "ctcf_infected_rep_2",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_VZV_CTCF_set3_S8_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "ctcf_infected_rep_3",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_Control_HFL_AntiCTCF_S31_L005_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "mock",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_VZV_AntiCTCF_S33_L005_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "infected",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_con_CTCF_set2_S2_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "mock",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_con_CTCF_set3_S6_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "mock",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_VZV_CTCF_set2_S4_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "infected",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_VZV_CTCF_set3_S8_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "infected",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_Control_HFL_Input_S30_L005_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "input_mock_rep_1",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_VZV_Input_S32_L005_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "input_infected_rep_1",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_con_in_set2_S1_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "input_mock_rep_2",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_con_in_set3_S5_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "input_mock_rep_3",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_VZV_in_set2_S3_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "input_infected_rep_2",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_VZV_in_set3_S7_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", name := "input_infected_rep_3",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_Control_HFL_Input_S30_L005_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "mock",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/Frietze_Cohrs_11497_180409_SNL128_0174_ACC3JAACXX_VZV_Input_S32_L005_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "infected",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_con_in_set2_S1_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "mock",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_con_in_set3_S5_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "mock",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_VZV_in_set2_S3_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "infected",]
dt[sample=="/slipstream/home/mmariani/projects/vzv_interactions/vzv_cohrs_ctcf_chip/vzv_cohrs_ctcf_chip_marked_duplicates/hg38/HFL_VZV_in_set3_S7_L001_R1_001.trimmed.R1.sorted.marked_duplicates.bam", treatment := "infected",]
##dt.box = dt[, .(N = length(unique(qname))), .(id, name, treatment, sample)]
dt.box = dt[, .(N = length(unique(read_id))), .(id, name, treatment, sample)]
unique(dt.box[, .(name, treatment, sample)])
unique(dt.box$name)
dt.box.casted <- dcast(dt.box,
id~name,
value.var = "N",
fill = 0)
dt.box_filled = melt(dt.box.casted,
id.vars = "id",
value.name = "N",
variable.name = "name")
dt.box = merge(dt.box_filled, unique(dt.box[, .(name, treatment, sample)]), by = "name")
unique(dt.box$sample)
unique(dt.box$treatment)
unique(dt.box$name)
##mapped_counts = sapply(rbind(qdt.bam, qdt.bam.neg)$file, function(f){
mapped_counts = sapply(rbind(bam_files, bam_files.neg), function(f){
stats = Rsamtools::idxstatsBam(f)
stats = subset(stats, grepl("chr[0-9XY]+$", seqnames))
sum(stats[,3])
})
dt.box$mapped_reads = mapped_counts[dt.box$sample]
dt.box[, frip := N/mapped_reads]
##name_lev = dt.box[, median(N) , .(name)][rev(order(V1))]$name
name_lev <- c("ctcf_mock_rep_1",
"ctcf_mock_rep_2",
"ctcf_mock_rep_3",
"ctcf_infected_rep_1",
"ctcf_infected_rep_2",
"ctcf_infected_rep_3",
"input_mock_rep_1",
"input_mock_rep_2",
"input_mock_rep_3",
"input_infected_rep_1",
"input_infected_rep_2",
"input_infected_rep_3")
dt.box$name = factor(dt.box$name, levels = name_lev)
peak_dt$name = factor(peak_dt$name, levels = name_lev)
fq_dt$name = factor(fq_dt$name, levels = name_lev)
message("make plots...")
p_fq1 = ggplot(fq_dt,
aes(x = name, y = fastq_count, fill = treatment)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
scale_y_continuous(labels = function(x)x/1e6) +
labs(y = "FASTQ reads (M)", x = "")
p_input1 = ggplot(unique(dt.box[, .(name, mapped_reads, treatment)]),
aes(x = name, y = mapped_reads, fill = treatment)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
scale_y_continuous(labels = function(x)x/1e6) +
labs(y = "BAM mapped reads (M)", x = "")
p_peaks1 = ggplot(peak_dt, aes(x = name, y = peak_count, fill = treatment)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
scale_y_continuous(labels = function(x)x/1e3) +
labs(y = "peaks (k)", x = "")
p_reads1 = ggplot(dt.box,
aes(x = name, y = N, color = treatment)) +
geom_boxplot(outlier.shape = NA) +
coord_cartesian(ylim = quantile(dt.box$N, c(0.1, 0.96))) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
labs(y = "reads per peak", x = "")
p_frip1 = ggplot(dt.box,
aes(x = name, y = frip, color = treatment)) +
geom_boxplot(outlier.shape = NA) +
coord_cartesian(ylim = quantile(dt.box$frip, c(0.1, 0.96))) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
labs(y = "FRIP per peak", x = "")
tmp = dt.box[, .(N = sum(N), mapped_reads = unique(mapped_reads)), .(sample, treatment, name)]
tmp[, frip := N / mapped_reads]
p_fripSum1 = ggplot(tmp,
aes(x = name, y = frip, fill = treatment)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
labs(subtitle = paste(sum(width((query_gr)))/3.2e9,
"of genome covered by peaks"),
x = "",
y = "FRIP")
##name_lev_frip = as.character(dt.box[, median(frip) , .(name)][rev(order(V1))]$name)
name_lev_frip <- c("ctcf_mock_rep_1",
"ctcf_mock_rep_2",
"ctcf_mock_rep_3",
"ctcf_infected_rep_1",
"ctcf_infected_rep_2",
"ctcf_infected_rep_3",
"input_mock_rep_1",
"input_mock_rep_2",
"input_mock_rep_3",
"input_infected_rep_1",
"input_infected_rep_2",
"input_infected_rep_3")
dt.box$name = factor(dt.box$name, levels = name_lev_frip)
fq_dt$name = factor(fq_dt$name, levels = name_lev_frip)
peak_dt$name = factor(peak_dt$name, levels = name_lev_frip)
p_fq2 = ggplot(fq_dt,
aes(x = name, y = fastq_count, fill = treatment)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
scale_y_continuous(labels = function(x)x/1e6) +
labs(y = "FASTQ reads (M)", x = "")
p_input2 = ggplot(unique(dt.box[, .(name, mapped_reads, treatment)]),
aes(x = name, y = mapped_reads, fill = treatment)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
scale_y_continuous(labels = function(x)x/1e6) +
labs(y = "BAM mapped reads (M)", x = "")
p_peaks2 = ggplot(peak_dt, aes(x = name, y = peak_count, fill = treatment)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
scale_y_continuous(labels = function(x)x/1e3) +
labs(y = "peaks (k)", x = "")
p_reads2 = ggplot(dt.box, aes(x = name, y = N, color = treatment)) +
geom_boxplot(outlier.shape = NA) +
coord_cartesian(ylim = quantile(dt.box$N, c(0.1, 0.96))) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
labs(y = "reads per peak", x = "")
p_frip2 = ggplot(dt.box, aes(x = name, y = frip, color = treatment)) +
geom_boxplot(outlier.shape = NA) +
coord_cartesian(ylim = quantile(dt.box$frip, c(0.1, 0.96))) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
labs(y = "FRIP per peak", x = "")
tmp = dt.box[, .(N = sum(N), mapped_reads = unique(mapped_reads)), .(sample, treatment, name)]
tmp[, frip := N / mapped_reads]
p_fripSum2 = ggplot(tmp,
aes(x = name, y = frip, fill = treatment)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
labs(subtitle = paste(sum(width((query_gr)))/3.2e9, "of genome covered by peaks"), x = "", y = "FRIP")
message("assemble plots...")
col1 = list(p_fq1, p_input1, p_reads1, p_peaks1, p_frip1, p_fripSum1)
col2 = list(p_fq2, p_input2, p_reads2, p_peaks2, p_frip2, p_fripSum2)
col1 = ssvRecipes::sync_width(col1)
col2 = ssvRecipes::sync_width(col2)
pg = cowplot::plot_grid(ncol = 2,
cowplot::plot_grid(plotlist = col1, ncol = 1),
cowplot::plot_grid(plotlist = col2, ncol = 1)
)
ggsave(paste0(output.dir,"/frip_boxes.", file_tag, ".pdf"),
pg,
width = 13.3,
height = 18)
unique(bw_dt$sample)
bw_dt.all$name = factor(bw_dt.all$name, levels = name_lev_frip)
bw_dt.all$facet = bw_dt.all$name
levels(bw_dt.all$facet) = gsub("_", "\n", levels(bw_dt.all$facet))
set.seed(0)
clust_dt = ssvSignalClustering(bw_dt.all,
fill_ = "y_relative",
max_cols = Inf,
facet_ = "facet",
max_rows = Inf)
toplot_id = ssvRecipes::sampleCap(unique(clust_dt$id), 500)
p_heat = ssvSignalHeatmap(clust_dt[id %in% toplot_id],
fill_ = "y_relative",
max_cols = Inf,
facet_ = "facet", show_cluster_bars = FALSE)
p_heat = p_heat +
labs(x = paste(view_size, "bp view size"), fill = "relative pileup") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "bottom")
ref_gr = rtracklayer::import.gff("/slipstream/home/joeboyd/gencode.v35.annotation.gtf.gz", format = "gtf")
head(ref_gr)
table(ref_gr$type)
gene_gr = reduce(subset(ref_gr, type == "gene"))
exon_gr = reduce(subset(ref_gr, type == "exon" & tag == "basic"))
intron_gr = setdiff(gene_gr, exon_gr)
tx_gr = subset(ref_gr, type == "transcript" & tag == "basic")
tss_gr = flank(tx_gr, 1e3, start = TRUE, both = TRUE)
tts_gr = flank(tx_gr, 1e3, start = FALSE, both = TRUE)
artifact_gr = rtracklayer::import.bed("/slipstream/home/joeboyd/R/qc_cutnrun/reference/blacklist_hg38.bed")
anno_grs = rev(list(artifact = artifact_gr, tss = tss_gr, tts = tts_gr, exon = exon_gr, intron = intron_gr, genebody = gene_gr))
anno_clust_dt = ssvFetchGRanges(anno_grs, resize(query_gr[toplot_id], view_size, fix = "center"), return_data.table = TRUE)
anno_clust_dt$id = factor(anno_clust_dt$id, levels = levels(clust_dt$id))
anno_clust_dt$sample = factor(anno_clust_dt$sample, levels = rev(names(anno_grs)))
p_heat_anno = ggplot(anno_clust_dt, aes(x = x, y = id, fill = y>0)) +
geom_raster() +
facet_wrap(~sample, nrow = 1) +
theme(axis.text.y = element_blank(), axis.ticks.y = element_blank()) +
scale_fill_manual(values = c("FALSE" = "gray80", "TRUE" = "gray20")) +
scale_x_continuous(labels = function(x)x/1e3) +
labs(fill = "feature overlap", x= "kbp", y = "peak region")
clust_sizes = unique(clust_dt[, .(cluster_id, id)])[, .N, .(cluster_id)][rev(order(cluster_id))]
clust_sizes[, xmin := 0]
clust_sizes[, xmax := 1]
clust_sizes[, ymin := c(0, cumsum(N)[-length(N)])]
clust_sizes[, ymax := cumsum(N)]
clust_sizes[, col := as.character(cluster_id%%2)]
clust_sizes$facet = ""
p_clust_boxes = ggplot(clust_sizes, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax)) +
geom_rect(aes(fill = col), color = "black") +
geom_text(aes(label = cluster_id, x = (xmin+xmax)/2, y = (ymin+ymax)/2)) +
scale_fill_manual(values = c("0" = "gray70", "1" = 'gray90')) +
guides(fill = 'none') +
coord_cartesian(expand = FALSE) +
theme_void() +
facet_grid(~facet)
p_heat_l = ssvRecipes::sync_height(list(p_clust_boxes, p_heat, p_heat_anno))
pg_heat_top = cowplot::plot_grid(plotlist = p_heat_l, rel_widths = c(1, length(peak_grs)+2, length(anno_grs)+1), nrow = 1)
dt.heat = copy(dt.box)
assign_dt = unique(clust_dt[, .(id, cluster_id)])
dt.heat = merge(dt.heat, assign_dt, by = "id")
tmp = dt.heat[, .(N = sum(N), mapped_reads = unique(mapped_reads)), .(sample, treatment, name, cluster_id)]
tmp[, frip := N / mapped_reads]
w_frac = lapply(split(assign_dt$id, assign_dt$cluster_id), function(x){
sum(width(query_gr[x])) /3.2e9
})
w_frac = unlist(w_frac)
tmp[, cluster_label := paste0("cluster", cluster_id, "\n", w_frac[cluster_id])]
p_fripHeat = ggplot(tmp,
aes(x = name, y = frip, fill = treatment)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), plot.margin = margin(.01, .01, .01, .1, unit = "npc")) +
labs(subtitle = paste(sum(width((query_gr)))/3.2e9, "of genome covered by peaks"), x = "", y = "FRIP") +
facet_grid(cluster_label~., scales = "free_y", switch = "y") +
theme(strip.text.y = element_text(angle = 0), strip.placement = "outside")
# p_fripHeat
# g_fripHeat = ssvRecipes::sync_width(list(p_fripHeat, p_heat))[[1]]
peak_dt
fq_dt
mapped_dt = unique(dt.box[, .(name, mapped_reads)])
meta_dt = merge(fq_dt, peak_dt[, .(name, peak_count)], by = "name")
meta_dt = merge(meta_dt, mapped_dt[, .(name, mapped_reads)], by = "name")
fwrite(meta_dt, paste0(output.dir,"/sample_info.", file_tag, ".csv"), sep = ",")
region_frip_dt = dcast(dt.box[, .(id, name, N, treatment, frip)], id~name, value.var = "frip")
setkey(region_frip_dt, id)
assign_dt
setkey(assign_dt, id)
bed_frip_towrite = query_gr
# bed_frip_towrite$id = NULL
bed_peak_towrite = query_gr
# bed_peak_towrite$id = NULL
mcols(bed_frip_towrite) = region_frip_dt[.(names(bed_frip_towrite))]
# bed_frip_towrite$id = NULL
message("write files...")
bed_frip_towrite$cluster_id = assign_dt[.(names(bed_frip_towrite))]$cluster_id
# rtracklayer::export.bed(bed_frip_towrite, paste0("consensus_regions_with_FRIP.", file_tag, ".bed"))
bed_frip_towrite_dt = as.data.table(bed_frip_towrite)
bed_frip_towrite_dt$strand = "."
bed_frip_towrite_dt$score = 0
extra_cols = setdiff(colnames(mcols(bed_frip_towrite)), "id")
extra_cols = gsub("-", ".", extra_cols)
fwrite(bed_frip_towrite_dt[, c("seqnames", "start", "end", "id", "score", "strand", extra_cols), with = FALSE],
file = paste0(output.dir,"/consensus_regions_with_FRIP.", file_tag, ".txt"),
sep = "\t")
bed_peak_towrite$cluster_id = assign_dt[.(names(bed_peak_towrite))]$cluster_id
bed_peak_towrite_dt = as.data.table(bed_peak_towrite)
bed_peak_towrite_dt$strand = "."
bed_peak_towrite_dt$score = 0
extra_cols = setdiff(colnames(mcols(bed_peak_towrite)), "id")
extra_cols = gsub("-", ".", extra_cols)
fwrite(bed_peak_towrite_dt[, c("seqnames", "start", "end", "id", "score", "strand", extra_cols), with = FALSE],
file = paste0(output.dir,"/consensus_regions_with_peak_call.", file_tag, ".txt"),
sep = "\t")
# rtracklayer::export.bed(bed_peak_towrite, paste0("consensus_regions_with_peak_call.", file_tag, ".bed"))
message("features overlap")
to_overlap_grs = c(peak_grs, peak_grs.neg)
peak_grs.anno = lapply(to_overlap_grs, function(x){
x$anno = "intergenic"
for(i in seq_along(anno_grs)){
olaps = findOverlaps(x, anno_grs[[i]])
x$anno[queryHits(olaps)] = names(anno_grs)[i]
}
x
# table(x$anno)
})
peak_grs.anno_cnt = lapply(peak_grs.anno, function(x){
tab = table(x$anno)
data.table(feature = names(tab), count = as.numeric(tab))
})
anno_cnt = rbindlist(peak_grs.anno_cnt, idcol = "sample")
anno_cnt[, sample_cnt := paste0(sample, "\n", sum(count)), .(sample)]
anno_cnt[, fraction := count / sum(count), .(sample)]
anno_cnt$sample = factor(anno_cnt$sample, levels = name_lev_frip)
anno_cnt = anno_cnt[order(sample)]
anno_cnt$sample_cnt = factor(anno_cnt$sample_cnt,
levels = unique(anno_cnt$sample_cnt))
p_features = ggplot(anno_cnt,
aes(x = sample_cnt,
y = fraction,
fill = feature)) +
geom_bar(stat = "identity") +
labs(x = "sample\npeak count") +
theme(panel.background = element_blank(),
panel.grid.major.y = element_line(color = "black"),
panel.grid.minor.y = element_line(color = "black"),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank()) +
scale_fill_brewer(palette = "Set1") +
labs(title = "Feature overlaps for peaks") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
dt.heat_summary = dt.heat[, .(mean_frip = mean(frip), median_frip = median(frip)), .(name, cluster_id)]
dt.heat_summary[, txt_mean := round(mean_frip*1e6, 1)]
dt.heat_summary[, txt_median := round(median_frip*1e6, 1)]
dt.heat_summary[, txt := paste0(round(mean_frip*1e6, 1), "\n", round(median_frip*1e6, 1))]
p_clust_text = ggplot(dt.heat_summary, aes(x = name, y = factor(cluster_id), label = txt)) +
geom_text(size = 5, color = NA) +
geom_text(data = dt.heat_summary, aes(x = name, cluster_id+.1, label = txt_mean), color = "red", vjust = 0) +
geom_text(data = dt.heat_summary, aes(x = name, cluster_id-.1, label = txt_median), color = "blue", vjust = 1) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), panel.background = element_blank(), panel.grid = element_blank()) +
labs(y = "cluster", x= "", title = "mean FRIP e6", subtitle = "median FRIP e6") +
theme(plot.title = element_text(size = 14, color = "red"), plot.subtitle = element_text(size = 14, color = "blue"))
pg_heat = cowplot::plot_grid(ncol = 1,
pg_heat_top,
cowplot::plot_grid(nrow = 1, p_fripHeat, p_clust_text))
ggsave(paste0(output.dir,"/frip_heatmap.", file_tag, ".pdf"), pg_heat, width = 8+length(peak_grs), height = 14)
ggsave(paste0(output.dir,"/feature_overlap.", file_tag, ".pdf"), p_features, width = 2+length(peak_grs)*.6, height = 6)
fwrite(anno_cnt,
file = paste0(output.dir,"/feature_counts.", file_tag, ".csv"),
sep = ",")
# utr_gr = subset(ref_gr, gene_name == "IKZF1" & type == "UTR")
# split(utr_gr, utr_gr$transcript_name)
message("done")
##setwd(base_dir)