# # Import cell type-specific epigenomic peaks
# #
# # Brain cell-specific epigenomic data from Nott et al. (2019).
# # @keywords internal
# # @family NOTT2019
# # @source
# # \href{https://doi.org/10.1126/science.aay0793}{Nott et al. (2019)}
# # \url{https://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&lastVirtModeType=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&position=chr2:127770344-127983251&hgsid=778249165_ySowqECRKNxURRn6bafH0yewAiuf}
# # @examples
# # \dontrun{
# # PEAKS.merged <- NOTT2019_get_epigenomic_peaks(peak.dir="/pd-omics/data/NOTT2019/peaks", narrow_peaks=TRUE, broad_peaks=F)
# # }
# NOTT2019_get_epigenomic_peaks_macs2 <- function(peak.dir="/pd-omics/data/NOTT2019/peaks",
# narrow_peaks=TRUE,
# broad_peaks=TRUE,
# nThread=1){
# bigWigFiles <- echoannot::NOTT2019_bigwig_metadata
# peak_types <- c(ifelse(narrow_peaks,".narrowPeak$", NA),
# ifelse(broad_peaks,"_broad.bed12$", NA))
# peak_types <- peak_types[!is.na(peak_types)]
# peaks.paths <- list.files(peak.dir,
# pattern = paste(peak_types, collapse = "|"),
# full.names = T,
# recursive = T)
# PEAKS <- MACS2.import_peaks(peaks.paths = peaks.paths,
# as_granges = T)
# PEAKS <- parallel::mclapply(PEAKS, function(peak){
# pk.name <- gsub(".ucsc_narrowPeak1|.ucsc_broadRegion1","",peak$name[1])
# meta <- subset(bigWigFiles, long_name==pk.name)
# peak$Cell_type <- meta$cell_type
# peak$Assay <- meta$assay
# peak$Fresh_frozen <- meta$fresh_frozen
# peak$Marker <- meta$marker
# return(peak)
# },mc.cores = nThread) |> GenomicRanges::GRangesList()
# PEAKS.merged <- unlist(PEAKS)
# PEAKS.merged$peak_type <- PEAKS.merged
# return(PEAKS.merged)
# }
#
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