inst/doc/data_munging.R
In rmflight/fmdatabreastcaparp1: Fondufe-Mittendorfe lab data for PARP1 correlation in MCF-7 and MDA-MB231
## ----setup---------------------------------------------------------------
library(fmdatabreastcaparp1)
library(GenomicRanges)
library(GEOquery)
raw_data_dir <- "/mlab/data/rmflight/Projects/work/fondufe-mittendorf_lab/parp1_data/"
save_dir <- "/mlab/data/rmflight/Projects/work/fondufe-mittendorf_lab/fmdatamcf7parp1/data"
## ----ln4_reads, eval=FALSE-----------------------------------------------
# ln4_files <- file.path(raw_data_dir, dir(raw_data_dir, pattern = "YFM_LN4"))
#
# parp1_ln4_reads_all <- GRanges(seqnames = "chr1", ranges = IRanges(start = 1, end = 1), strand = "*")
#
# for (i_file in ln4_files){
# tmp_reads <- read.table(i_file, sep = ",", header = TRUE)
# use_chr <- get_chr(i_file, "_")
# parp1_ln4_reads_all <- c(parp1_ln4_reads_all, GRanges(seqnames = use_chr,
# ranges = IRanges(start = tmp_reads[, "startx"], width = 1),
# strand = "*"))
# }
# parp1_ln4_reads_all <- parp1_ln4_reads_all[2:(length(parp1_ln4_reads_all))]
# save(parp1_ln4_reads_all, file = file.path(save_dir, "parp1_ln4_reads_all.RData"))
# rm(parp1_ln4_reads_all)
## ----ln5_reads, eval=FALSE-----------------------------------------------
# ln5_files <- file.path(raw_data_dir, dir(raw_data_dir, pattern = "YFM_LN5"))
#
# parp1_ln5_reads_all <- GRanges(seqnames = "chr1", ranges = IRanges(start = 1, end = 1), strand = "*")
#
# for (i_file in ln5_files){
# tmp_reads <- read.table(i_file, sep = ",", header = TRUE)
# use_chr <- get_chr(i_file, "_")
# parp1_ln5_reads_all <- c(parp1_ln5_reads_all, GRanges(seqnames = use_chr,
# ranges = IRanges(start = tmp_reads[, "startx"], width = 1),
# strand = "*"))
# }
# parp1_ln5_reads_all <- parp1_ln5_reads_all[2:(length(parp1_ln5_reads_all))]
# save(parp1_ln5_reads_all, file = file.path(save_dir, "parp1_ln5_reads_all.RData"))
# rm(parp1_ln5_reads_all)
## ----load_chrom_3--------------------------------------------------------
data(parp1_ln4_reads_all)
chr3_reads <- parp1_ln4_reads_all[seqnames(parp1_ln4_reads_all) == "chr3"]
chr3_unique <- unique(chr3_reads)
chr3_overlap <- countOverlaps(chr3_unique, chr3_reads)
mcols(chr3_unique)$overlap <- chr3_overlap
chr3_unique <- sort(chr3_unique)
## ----check_chr3_counts---------------------------------------------------
chr3_overlap_rle <- rle(sort(chr3_overlap))
chr3_counts <- chr3_overlap_rle$values
names(chr3_counts) <- chr3_overlap_rle$lengths
chr3_counts
## ----chr3_99-------------------------------------------------------------
chr3_totals <- data.frame(n_loc = chr3_overlap_rle$lengths, count_at_loc = chr3_overlap_rle$values)
chr3_totals$tot_reads <- chr3_totals$n_loc * chr3_totals$count_at_loc
chr3_totals$cum_reads <- cumsum(chr3_totals$tot_reads)
chr3_totals$perc_reads <- chr3_totals$cum_reads / sum(chr3_totals$tot_reads) * 100
head(chr3_totals)
## ----ln4_all_unique------------------------------------------------------
ln4_unique <- unique(parp1_ln4_reads_all)
ln4_overlap <- countOverlaps(ln4_unique, parp1_ln4_reads_all)
ln4_overlap_rle <- rle(sort(ln4_overlap))
ln4_counts <- data.frame(n_loc = ln4_overlap_rle$lengths, count_at_loc = ln4_overlap_rle$values)
ln4_counts$tot_reads <- ln4_counts$n_loc * ln4_counts$count_at_loc
ln4_counts$cum_reads <- cumsum(ln4_counts$tot_reads)
ln4_counts$perc_reads <- ln4_counts$cum_reads / sum(ln4_counts$tot_reads) * 100
head(ln4_counts)
## ----parp1_ln4_unique, eval=FALSE----------------------------------------
# data(parp1_ln4_reads_all)
# max_count <- 6
# parp1_ln4_unique <- unique(parp1_ln4_reads_all)
# parp1_ln4_counts <- countOverlaps(parp1_ln4_unique, parp1_ln4_reads_all)
# parp1_ln4_counts[parp1_ln4_counts > max_count] <- max_count
# mcols(parp1_ln4_unique)$n_count <- parp1_ln4_counts
# save(parp1_ln4_unique, file = file.path(save_dir, "parp1_ln4_unique.RData"))
## ----parp1_ln5_unique, eval=FALSE----------------------------------------
# data(parp1_ln5_reads_all)
# parp1_ln5_unique <- unique(parp1_ln5_reads_all)
# parp1_ln5_counts <- countOverlaps(parp1_ln5_unique, parp1_ln5_reads_all)
# parp1_ln5_counts[parp1_ln5_counts > max_count] <- max_count
# mcols(parp1_ln5_unique)$n_count <- parp1_ln5_counts
# save(parp1_ln5_unique, file = file.path(save_dir, "parp1_ln5_unique.RData"))
## ----ctcf_data, eval=FALSE-----------------------------------------------
# ctcf_names <- c("chrom", "start", "end", "name", "score", "strand", "signal", "pValue", "qValue")
# ctcf_path <- file.path(raw_data_dir, "ctcf_peaks")
# ctcf_files <- file.path(ctcf_path, dir(ctcf_path, pattern = "narrowPeak.gz"))
# rep1 <- read.table(ctcf_files[1], header = FALSE, sep = "\t", stringsAsFactors = FALSE)
#
# names(rep1) <- ctcf_names
# ctcf_rep1 <- GRanges(seqnames = rep1$chrom,
# ranges = IRanges(start = rep1$start, end = rep1$end),
# mcols = DataFrame(rep1[, c("signal", "pValue")]))
#
#
# rep2 <- read.table(ctcf_files[2], header = FALSE, sep = "\t", stringsAsFactors = FALSE)
# names(rep2) <- ctcf_names
# ctcf_rep2 <- GRanges(seqnames = rep2$chrom,
# ranges = IRanges(start = rep2$start, end = rep2$end),
# mcols = DataFrame(rep2[, c("signal", "pValue")]))
#
# save(ctcf_rep1, file = file.path(save_dir, "ctcf_rep1.RData"))
# save(ctcf_rep2, file = file.path(save_dir, "ctcf_rep2.RData"))
# rm(ctcf_rep1, ctcf_rep2)
## ----save_methylation, eval = FALSE--------------------------------------
# methyl_names <- c("bin", "chrom", "start", "end", "name", "score", "strand", "thickStart", "thickEnd", "itemRGB", "readCount", "percentMeth")
# methyl_path <- file.path(raw_data_dir, "methylation_data")
# methyl_files <- file.path(methyl_path, dir(methyl_path, pattern = "mcf7_methyl"))
#
# rep1 <- read.table(methyl_files[1], header = FALSE, sep = "\t", stringsAsFactors = FALSE)
# names(rep1) <- methyl_names
# methyl_rep1 <- GRanges(seqnames = rep1$chrom,
# strand = rep1$strand,
# ranges = IRanges(start = rep1$start, width = 1),
# mcols = DataFrame(rep1[, c("readCount", "percentMeth")]))
#
# rep2 <- read.table(methyl_files[2], header = FALSE, sep = "\t", stringsAsFactors = FALSE)
# names(rep2) <- methyl_names
# methyl_rep2 <- GRanges(seqnames = rep2$chrom,
# strand = rep2$strand,
# ranges = IRanges(start = rep2$start, width = 1),
# mcols = DataFrame(rep2[, c("readCount", "percentMeth")]))
#
# save(methyl_rep1, file = file.path(save_dir, "methyl_rep1.RData"))
# save(methyl_rep2, file = file.path(save_dir, "methyl_rep2.RData"))
# rm(methyl_rep1, methyl_rep2)
## ----read_histones, eval=FALSE-------------------------------------------
# histone_patterns <- list(H3k09me3 = "*H3k09me3",
# H3k27ac = "*H3k27ac",
# H3k27me3 = "*H3k27me3",
# H3k36me3 = "*H3k36me3",
# H3k4me3_r1 = "*H3k4me3",
# H3k4me3_r2 = "*H3k04me3")
#
# histone_dir <- file.path(raw_data_dir, "histone_marks")
# histone_names <- c("chrom", "start", "end", "name", "score", "strand", "signal", "pvalue", "qvalue", "other")
# histone_marks <- lapply(histone_patterns, function(in_pattern){
# use_file <- dir(histone_dir, pattern = in_pattern)
# tmp <- read.table(file.path(histone_dir, use_file), header = FALSE, sep = "\t")
# names(tmp) <- histone_names
# GRanges(seqnames = tmp$chrom,
# ranges = IRanges(start = tmp$start, end = tmp$end),
# mcols = DataFrame(tmp[, c("score", "signal", "pvalue")]))
# })
# histone_marks <- GRangesList(histone_marks)
# save(histone_marks, file = file.path(save_dir, "histone_marks.RData"))
# rm(histone_marks)
## ----get_expression, eval=FALSE------------------------------------------
# expr_data <- dataTable(getGEO(GEO = "GSM307014"))@table
# expr_data$VALUE <- as.numeric(expr_data$VALUE)
# expr_data[, "DETECTION P-VALUE"] <- as.numeric(expr_data[, "DETECTION P-VALUE"])
# rownames(expr_data) <- as.character(expr_data$ID_REF)
# expr_data$ID_REF <- rownames(expr_data)
# save(expr_data, file = file.path(save_dir, "expr_data.RData"))
## ----get_tss_windows, eval = FALSE---------------------------------------
# tss_file <- file.path(raw_data_dir, "ensGene_TTSS.csv")
# tss_data <- read.table(tss_file, header = TRUE, sep = ",", stringsAsFactors = FALSE)
# tss_regions <- GRanges(seqnames = tss_data$chrom,
# strand = tss_data$strand,
# ranges = IRanges(start = tss_data$txStart,
# end = tss_data$txEnd),
# names = tss_data$name)
# rm(tss_data)
#
# tss_windows <- GRanges(seqnames = seqnames(tss_regions),
# strand = strand(tss_regions),
# ranges = IRanges(start = start(tss_regions) - 1000,
# end = start(tss_regions) + 1000))
# names(tss_windows) <- mcols(tss_regions)$names
# save(tss_windows, file = file.path(save_dir, "tss_windows.RData"))
# rm(tss_windows, tss_regions)
## ---- eval = FALSE-------------------------------------------------------
# library(GenomicFiles)
# library(BSgenome.Hsapiens.UCSC.hg19)
# library(fmcorrelationbreastcaparp1)
#
# genome_tiles <- tileGenome(seqinfo(Hsapiens), tilewidth = 500, cut.last.tile.in.chrom = TRUE)
# nuc_file <- file.path(raw_data_dir, "wgEncodeSydhNsomeGm12878Sig.bigWig")
#
# genome_tiles <- genome_tiles[seqnames(genome_tiles) %in% seqnames(seqinfo(BigWigFile(nuc_file)))]
# seqlevels(genome_tiles) <- paste0("chr", c(seq(1, 22), "X", "M"))
#
# split_tiles <- split(genome_tiles, seqnames(genome_tiles))
#
# MAP <- function(RANGE, FILE, ...) {
# if (length(RANGE) > 0){
# tmp <- import.bw(FILE, selection=RANGE, as="GRanges")
# tmp_cov <- coverage(tmp, weight = "score")
# tmp_range <- binned_function(RANGE, tmp_cov, "sum", "nuc")
# } else {
# tmp_range <- GRanges()
# }
# tmp_range
# }
#
# bw_counts <- reduceByRange(split_tiles, files = nuc_file, MAP)
# nuc_signal <- GRanges()
#
# for (icount in seq(1, length(bw_counts))){
# if (length(bw_counts[[icount]][[1]]) > 0){
# nuc_signal <- append(nuc_signal, bw_counts[[icount]][[1]])
# }
# }
#
# save(nuc_signal, file = file.path(save_dir, "nuc_signal.RData"))
## ------------------------------------------------------------------------
Sys.time()
sessionInfo()
rmflight/fmdatabreastcaparp1 documentation built on May 27, 2019, 9:31 a.m.
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## ----setup---------------------------------------------------------------
library(fmdatabreastcaparp1)
library(GenomicRanges)
library(GEOquery)
raw_data_dir <- "/mlab/data/rmflight/Projects/work/fondufe-mittendorf_lab/parp1_data/"
save_dir <- "/mlab/data/rmflight/Projects/work/fondufe-mittendorf_lab/fmdatamcf7parp1/data"
## ----ln4_reads, eval=FALSE-----------------------------------------------
# ln4_files <- file.path(raw_data_dir, dir(raw_data_dir, pattern = "YFM_LN4"))
#
# parp1_ln4_reads_all <- GRanges(seqnames = "chr1", ranges = IRanges(start = 1, end = 1), strand = "*")
#
# for (i_file in ln4_files){
# tmp_reads <- read.table(i_file, sep = ",", header = TRUE)
# use_chr <- get_chr(i_file, "_")
# parp1_ln4_reads_all <- c(parp1_ln4_reads_all, GRanges(seqnames = use_chr,
# ranges = IRanges(start = tmp_reads[, "startx"], width = 1),
# strand = "*"))
# }
# parp1_ln4_reads_all <- parp1_ln4_reads_all[2:(length(parp1_ln4_reads_all))]
# save(parp1_ln4_reads_all, file = file.path(save_dir, "parp1_ln4_reads_all.RData"))
# rm(parp1_ln4_reads_all)
## ----ln5_reads, eval=FALSE-----------------------------------------------
# ln5_files <- file.path(raw_data_dir, dir(raw_data_dir, pattern = "YFM_LN5"))
#
# parp1_ln5_reads_all <- GRanges(seqnames = "chr1", ranges = IRanges(start = 1, end = 1), strand = "*")
#
# for (i_file in ln5_files){
# tmp_reads <- read.table(i_file, sep = ",", header = TRUE)
# use_chr <- get_chr(i_file, "_")
# parp1_ln5_reads_all <- c(parp1_ln5_reads_all, GRanges(seqnames = use_chr,
# ranges = IRanges(start = tmp_reads[, "startx"], width = 1),
# strand = "*"))
# }
# parp1_ln5_reads_all <- parp1_ln5_reads_all[2:(length(parp1_ln5_reads_all))]
# save(parp1_ln5_reads_all, file = file.path(save_dir, "parp1_ln5_reads_all.RData"))
# rm(parp1_ln5_reads_all)
## ----load_chrom_3--------------------------------------------------------
data(parp1_ln4_reads_all)
chr3_reads <- parp1_ln4_reads_all[seqnames(parp1_ln4_reads_all) == "chr3"]
chr3_unique <- unique(chr3_reads)
chr3_overlap <- countOverlaps(chr3_unique, chr3_reads)
mcols(chr3_unique)$overlap <- chr3_overlap
chr3_unique <- sort(chr3_unique)
## ----check_chr3_counts---------------------------------------------------
chr3_overlap_rle <- rle(sort(chr3_overlap))
chr3_counts <- chr3_overlap_rle$values
names(chr3_counts) <- chr3_overlap_rle$lengths
chr3_counts
## ----chr3_99-------------------------------------------------------------
chr3_totals <- data.frame(n_loc = chr3_overlap_rle$lengths, count_at_loc = chr3_overlap_rle$values)
chr3_totals$tot_reads <- chr3_totals$n_loc * chr3_totals$count_at_loc
chr3_totals$cum_reads <- cumsum(chr3_totals$tot_reads)
chr3_totals$perc_reads <- chr3_totals$cum_reads / sum(chr3_totals$tot_reads) * 100
head(chr3_totals)
## ----ln4_all_unique------------------------------------------------------
ln4_unique <- unique(parp1_ln4_reads_all)
ln4_overlap <- countOverlaps(ln4_unique, parp1_ln4_reads_all)
ln4_overlap_rle <- rle(sort(ln4_overlap))
ln4_counts <- data.frame(n_loc = ln4_overlap_rle$lengths, count_at_loc = ln4_overlap_rle$values)
ln4_counts$tot_reads <- ln4_counts$n_loc * ln4_counts$count_at_loc
ln4_counts$cum_reads <- cumsum(ln4_counts$tot_reads)
ln4_counts$perc_reads <- ln4_counts$cum_reads / sum(ln4_counts$tot_reads) * 100
head(ln4_counts)
## ----parp1_ln4_unique, eval=FALSE----------------------------------------
# data(parp1_ln4_reads_all)
# max_count <- 6
# parp1_ln4_unique <- unique(parp1_ln4_reads_all)
# parp1_ln4_counts <- countOverlaps(parp1_ln4_unique, parp1_ln4_reads_all)
# parp1_ln4_counts[parp1_ln4_counts > max_count] <- max_count
# mcols(parp1_ln4_unique)$n_count <- parp1_ln4_counts
# save(parp1_ln4_unique, file = file.path(save_dir, "parp1_ln4_unique.RData"))
## ----parp1_ln5_unique, eval=FALSE----------------------------------------
# data(parp1_ln5_reads_all)
# parp1_ln5_unique <- unique(parp1_ln5_reads_all)
# parp1_ln5_counts <- countOverlaps(parp1_ln5_unique, parp1_ln5_reads_all)
# parp1_ln5_counts[parp1_ln5_counts > max_count] <- max_count
# mcols(parp1_ln5_unique)$n_count <- parp1_ln5_counts
# save(parp1_ln5_unique, file = file.path(save_dir, "parp1_ln5_unique.RData"))
## ----ctcf_data, eval=FALSE-----------------------------------------------
# ctcf_names <- c("chrom", "start", "end", "name", "score", "strand", "signal", "pValue", "qValue")
# ctcf_path <- file.path(raw_data_dir, "ctcf_peaks")
# ctcf_files <- file.path(ctcf_path, dir(ctcf_path, pattern = "narrowPeak.gz"))
# rep1 <- read.table(ctcf_files[1], header = FALSE, sep = "\t", stringsAsFactors = FALSE)
#
# names(rep1) <- ctcf_names
# ctcf_rep1 <- GRanges(seqnames = rep1$chrom,
# ranges = IRanges(start = rep1$start, end = rep1$end),
# mcols = DataFrame(rep1[, c("signal", "pValue")]))
#
#
# rep2 <- read.table(ctcf_files[2], header = FALSE, sep = "\t", stringsAsFactors = FALSE)
# names(rep2) <- ctcf_names
# ctcf_rep2 <- GRanges(seqnames = rep2$chrom,
# ranges = IRanges(start = rep2$start, end = rep2$end),
# mcols = DataFrame(rep2[, c("signal", "pValue")]))
#
# save(ctcf_rep1, file = file.path(save_dir, "ctcf_rep1.RData"))
# save(ctcf_rep2, file = file.path(save_dir, "ctcf_rep2.RData"))
# rm(ctcf_rep1, ctcf_rep2)
## ----save_methylation, eval = FALSE--------------------------------------
# methyl_names <- c("bin", "chrom", "start", "end", "name", "score", "strand", "thickStart", "thickEnd", "itemRGB", "readCount", "percentMeth")
# methyl_path <- file.path(raw_data_dir, "methylation_data")
# methyl_files <- file.path(methyl_path, dir(methyl_path, pattern = "mcf7_methyl"))
#
# rep1 <- read.table(methyl_files[1], header = FALSE, sep = "\t", stringsAsFactors = FALSE)
# names(rep1) <- methyl_names
# methyl_rep1 <- GRanges(seqnames = rep1$chrom,
# strand = rep1$strand,
# ranges = IRanges(start = rep1$start, width = 1),
# mcols = DataFrame(rep1[, c("readCount", "percentMeth")]))
#
# rep2 <- read.table(methyl_files[2], header = FALSE, sep = "\t", stringsAsFactors = FALSE)
# names(rep2) <- methyl_names
# methyl_rep2 <- GRanges(seqnames = rep2$chrom,
# strand = rep2$strand,
# ranges = IRanges(start = rep2$start, width = 1),
# mcols = DataFrame(rep2[, c("readCount", "percentMeth")]))
#
# save(methyl_rep1, file = file.path(save_dir, "methyl_rep1.RData"))
# save(methyl_rep2, file = file.path(save_dir, "methyl_rep2.RData"))
# rm(methyl_rep1, methyl_rep2)
## ----read_histones, eval=FALSE-------------------------------------------
# histone_patterns <- list(H3k09me3 = "*H3k09me3",
# H3k27ac = "*H3k27ac",
# H3k27me3 = "*H3k27me3",
# H3k36me3 = "*H3k36me3",
# H3k4me3_r1 = "*H3k4me3",
# H3k4me3_r2 = "*H3k04me3")
#
# histone_dir <- file.path(raw_data_dir, "histone_marks")
# histone_names <- c("chrom", "start", "end", "name", "score", "strand", "signal", "pvalue", "qvalue", "other")
# histone_marks <- lapply(histone_patterns, function(in_pattern){
# use_file <- dir(histone_dir, pattern = in_pattern)
# tmp <- read.table(file.path(histone_dir, use_file), header = FALSE, sep = "\t")
# names(tmp) <- histone_names
# GRanges(seqnames = tmp$chrom,
# ranges = IRanges(start = tmp$start, end = tmp$end),
# mcols = DataFrame(tmp[, c("score", "signal", "pvalue")]))
# })
# histone_marks <- GRangesList(histone_marks)
# save(histone_marks, file = file.path(save_dir, "histone_marks.RData"))
# rm(histone_marks)
## ----get_expression, eval=FALSE------------------------------------------
# expr_data <- dataTable(getGEO(GEO = "GSM307014"))@table
# expr_data$VALUE <- as.numeric(expr_data$VALUE)
# expr_data[, "DETECTION P-VALUE"] <- as.numeric(expr_data[, "DETECTION P-VALUE"])
# rownames(expr_data) <- as.character(expr_data$ID_REF)
# expr_data$ID_REF <- rownames(expr_data)
# save(expr_data, file = file.path(save_dir, "expr_data.RData"))
## ----get_tss_windows, eval = FALSE---------------------------------------
# tss_file <- file.path(raw_data_dir, "ensGene_TTSS.csv")
# tss_data <- read.table(tss_file, header = TRUE, sep = ",", stringsAsFactors = FALSE)
# tss_regions <- GRanges(seqnames = tss_data$chrom,
# strand = tss_data$strand,
# ranges = IRanges(start = tss_data$txStart,
# end = tss_data$txEnd),
# names = tss_data$name)
# rm(tss_data)
#
# tss_windows <- GRanges(seqnames = seqnames(tss_regions),
# strand = strand(tss_regions),
# ranges = IRanges(start = start(tss_regions) - 1000,
# end = start(tss_regions) + 1000))
# names(tss_windows) <- mcols(tss_regions)$names
# save(tss_windows, file = file.path(save_dir, "tss_windows.RData"))
# rm(tss_windows, tss_regions)
## ---- eval = FALSE-------------------------------------------------------
# library(GenomicFiles)
# library(BSgenome.Hsapiens.UCSC.hg19)
# library(fmcorrelationbreastcaparp1)
#
# genome_tiles <- tileGenome(seqinfo(Hsapiens), tilewidth = 500, cut.last.tile.in.chrom = TRUE)
# nuc_file <- file.path(raw_data_dir, "wgEncodeSydhNsomeGm12878Sig.bigWig")
#
# genome_tiles <- genome_tiles[seqnames(genome_tiles) %in% seqnames(seqinfo(BigWigFile(nuc_file)))]
# seqlevels(genome_tiles) <- paste0("chr", c(seq(1, 22), "X", "M"))
#
# split_tiles <- split(genome_tiles, seqnames(genome_tiles))
#
# MAP <- function(RANGE, FILE, ...) {
# if (length(RANGE) > 0){
# tmp <- import.bw(FILE, selection=RANGE, as="GRanges")
# tmp_cov <- coverage(tmp, weight = "score")
# tmp_range <- binned_function(RANGE, tmp_cov, "sum", "nuc")
# } else {
# tmp_range <- GRanges()
# }
# tmp_range
# }
#
# bw_counts <- reduceByRange(split_tiles, files = nuc_file, MAP)
# nuc_signal <- GRanges()
#
# for (icount in seq(1, length(bw_counts))){
# if (length(bw_counts[[icount]][[1]]) > 0){
# nuc_signal <- append(nuc_signal, bw_counts[[icount]][[1]])
# }
# }
#
# save(nuc_signal, file = file.path(save_dir, "nuc_signal.RData"))
## ------------------------------------------------------------------------
Sys.time()
sessionInfo()
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