Nothing
R/exportResults.R
In exomePeak2: Bias Awared Peak Calling and Quantification for MeRIP-Seq
#' @title Export the (Differential) Modification Peaks/Sites and their associated Statistics
#' @param sep a \code{\link{SummarizedExomePeak}} object.
#' @param format a \code{character} for the exported format, could be a vector that contains \code{c("CSV", "BED", "RDS")}.
#'
#' \describe{
#' \item{\strong{\code{CSV}}}{
#' export a comma separated values (CSV) table with the genomic location and LFC statistics.
#' }
#'
#' \item{\strong{\code{BED}}}{
#' export a BEDGraph file with the score column = -log2(adjusted p value).
#' }
#'
#' \item{\strong{\code{RDS}}}{
#' export the RDS file of the \code{\link{SummarizedExperiment}} object.
#' }
#' }
#'
#' @param save_dir a \code{character} for the name of the directory being saved; Default \code{= "exomePeak2_output"}.
#'
#' @param cut_off_pvalue a \code{numeric} value for the p value cutoff in the exported result; Default \code{= NULL}.
#'
#' @param cut_off_padj a \code{numeric} value for the adjusted p value cutoff in the exported result; Default \code{= 0.05}.
#'
#' @param cut_off_log2FC a \code{numeric} value for the log2 fold change cutoff of the exported result,
#' only the sites with abs(LFC) larger than this value are kept; Default \code{= 0}.
#'
#' @param min_num_of_positive a \code{numeric} value for the minimum number of reported sites.
#' If the number of remaining sites is less than this number after filtering, additional sites will be reported by the increasing order of the p value to meet this number.
#'
#' @param expected_direction a \code{character} for the expected differential modification direction, could be one in \code{c("hyper", "hypo", "both")}.
#'
#' \describe{
#' \item{\strong{\code{hyper}}}{
#' only report the peaks/sites with interactive LFC > 0.
#' }
#'
#' \item{\strong{\code{hypo}}}{
#' only report the peaks/sites with interactive LFC < 0.
#' }
#'
#' \item{\strong{\code{both}}}{
#' report the peaks/sites in both directions.
#' }
#' }
#'
#' This argument is useful when the treated group involves the perturbation of a known writer or eraser protein; Default "both".
#'
#' @param inhibit_filter a \code{logical} of whether to remove all the filters, this option is useful when quantification on single based site annotation; Default \code{= FALSE}.
#'
#' @param table_style a \code{character} for the style of the CSV table being exported, could be one in \code{c("bed","granges")}.
#'
#' \describe{
#' \item{\strong{\code{bed}}}{
#' the genomic locations in the table are represented by BEDgraph style.
#' }
#'
#' \item{\strong{\code{granges}}}{
#' the genomic locations in the table are represented by GRanges style.
#' }
#' }
#'
#' @param reads_count a \code{logical} of whether to export the reads count for each sample; Default \code{ = TRUE}.
#'
#' @param GC_sizeFactors a \code{logical} of whether to export the GC content correction size factors; Default \code{ = TRUE}.
#'
#' @examples
#'
#' ### Load the example SummarizedExomPeak object
#' f1 = system.file("extdata", "sep_ex_mod.rds", package="exomePeak2")
#'
#' sep <- readRDS(f1)
#'
#' ### Save the modification peaks/sites statistics on the current directory.
#' exportResults(sep)
#'
#' @importFrom rtracklayer export
#' @import GenomicRanges
#'
#' @aliases exportResults
#'
#' @rdname exportResults-methods
#'
#' @return none, a folder will be saved on the disk.
#'
#' @export
#'
setMethod("exportResults",
"SummarizedExomePeak",
function(sep,
format = c("CSV", "BED", "RDS"),
table_style = c("bed", "granges"),
save_dir = "exomePeak2_output",
cut_off_pvalue = NULL,
cut_off_padj = 0.1,
cut_off_log2FC = 0,
min_num_of_positive = 100,
expected_direction = c("both", "hyper", "hypo"),
inhibit_filter = FALSE,
reads_count = TRUE,
GC_sizeFactors = TRUE) {
#Check the input
stopifnot(cut_off_pvalue <= 1 & cut_off_pvalue >= 0)
stopifnot(cut_off_log2FC >= 0)
stopifnot(all(format %in% c("CSV", "BED", "RDS")))
table_style <- match.arg(table_style)
expected_direction <- match.arg(expected_direction)
#Create the saving directory
if (!dir.exists(save_dir)) {
dir.create(save_dir)
}
#Create the folder for the additional information
if (!dir.exists(file.path(save_dir,"ADDInfo"))) {
dir.create(file.path(save_dir,"ADDInfo"))
}
#Decide the file names
if (!any(grepl("Diff",colnames(exomePeak2Results( sep ))))) {
file_name <- "Mod"
} else{
file_name <- "DiffMod"
}
#Check if the users have calculated the log2FCs and their associated p-values
if (nrow(exomePeak2Results(sep)) == 0) {
if (any(sep$design_Treatment)) {
sep <- glmDM(sep)
} else{
sep <- glmM(sep)
}
}
#Generate row index for the exported modification in SummarizedExomePeak
if (inhibit_filter) {
#Do not filter the rows on modification
index_keep <- T
} else {
if (!any(grepl("Diff",colnames(exomePeak2Results( sep ))))) {
#Decide the filter on modification
decision_mod <- decision_deseq2(
Inf_RES = exomePeak2Results(sep),
log2FC_cut = cut_off_log2FC,
P_cut = cut_off_pvalue,
Padj_cut = cut_off_padj,
Min_mod = min(min_num_of_positive, nrow(exomePeak2Results(sep)))
)
#P.S. If no sites are reported, export all the p values that are not NA
index_keep <-
which(
(exomePeak2Results(sep)[[decision_mod$Cut_By_expected]] <= decision_mod$Cut_Val_expected) &
(exomePeak2Results(sep)$log2FoldChange > cut_off_log2FC)
)
} else {
#Decide the filter on differential modification
decision_dm <- decision_deseq2(
Inf_RES = exomePeak2Results(sep),
log2FC_cut = cut_off_log2FC,
P_cut = cut_off_pvalue,
Padj_cut = cut_off_padj,
Min_mod = min_num_of_positive,
Exp_dir = expected_direction
)
if (expected_direction == "both") {
indx_es <-
(abs(exomePeak2Results(sep)$log2FoldChange) > cut_off_log2FC)
} else {
if (expected_direction == "hyper") {
indx_es <- (exomePeak2Results(sep)$log2FoldChange > cut_off_log2FC)
} else {
indx_es <- (exomePeak2Results(sep)$log2FoldChange < -1 * cut_off_log2FC)
}
}
index_keep <-
which(exomePeak2Results(sep)[[decision_dm$Cut_By_expected]] < decision_dm$Cut_Val_expected & indx_es)
if (length(index_keep) == min_num_of_positive) {
warning(paste0("The number of significantly differentially modified sites < ",min_num_of_positive,", the insignificantly differentially modified sites are reported to reach the minimum site number of ",min_num_of_positive,"; to report the differential modification status on all modification sites, please set 'inhibit_filter = TRUE.'\n"))
}
}
}
#Subset the SummarizedExomePeak object according to user defined filters
sep <- sep[grepl("peak_", rownames(sep)),][index_keep,]
exomePeak2Results(sep) <- exomePeak2Results(sep)[grepl("peak_", rownames(sep)),][index_keep,]
id_num <- as.numeric(gsub("^.*_", "", rownames(sep)))
id_index <- order(id_num)
sep <- sep[id_index,]
exomePeak2Results(sep) <- exomePeak2Results(sep)[id_index,]
rownames(sep) <- paste0("peak_", rep(seq_along(id_num), table(id_num[id_index])))
rm(id_num, id_index)
if (any(format == "RDS")) saveRDS(sep, file.path(save_dir, paste0(file_name, ".rds")))
result_grl <- rowRanges(sep)
result_stat <- exomePeak2Results(sep)
#Correct the names in the resulting files
result_gr <- unlist(result_grl)
result_gr$gene_id <- gsub("\\.[0-9]*$", "", result_gr$gene_id)
rownames(result_stat) = NULL
#Generate the GRangesList file that is ready to export
indx_split <- names(result_gr)
names(result_gr) <- NULL
result_gr$gene_id[is.na(result_gr$gene_id)] <- " "
result_grl <- split(result_gr, indx_split)
#reorder the list
result_grl <- result_grl[order(as.numeric(gsub("^.*_", "", names(result_grl))))]
mcols(result_grl) <- result_stat
rm(result_gr,result_stat,indx_split)
id_num <- as.numeric(gsub("^.*_", "", names(result_grl)))
id_index <- order(id_num)
renamed_id <- paste0("peak_", rep(seq_along(id_num), table(id_num[id_index])))
result_grl <- result_grl[id_index,]
names(result_grl) <- renamed_id
#Generate the tables according to the style choices
if (table_style == "granges") {
result_df <- as.data.frame(result_grl)
result_df <-
result_df[, colnames(result_df) != "group"]
colnames(result_df)[colnames(result_df) == "group_name"] = "peak_name"
colnames_range_info <- colnames(result_df)
result_df <-
cbind(result_df, as.data.frame(mcols(result_grl))[rep(seq_along(result_grl), elementNROWS(result_grl)),])
site_widths <- sum(width(result_grl[rep(seq_along(result_grl), elementNROWS(result_grl))]))
} else {
scores <- -1 * log2(mcols(result_grl)$padj)
scores[is.na(scores)] <- 0
mcols(result_grl)$score <- scores
export(
object = result_grl,
con = file.path(save_dir, paste0(file_name, ".bed")),
format = "BED"
)
result_df <- read.table(file.path(save_dir, paste0(file_name, ".bed")),
header = F,
sep = "\t")
colnames(result_df) <- c(
"chr",
"chromStart",
"chromEnd",
"name",
"score",
"strand",
"thickStart",
"thickEnd",
"itemRgb",
"blockCount",
"blockSizes",
"blockStarts"
)
result_df$geneID <- vapply( result_grl, function(x) x$gene_id[1] , character(1))
colnames_range_info <- colnames(result_df)
mcols(result_grl) <- mcols(result_grl)[,!colnames(mcols(result_grl)) %in% "score"]
site_widths <- sum(width(result_grl))
result_df <- cbind(result_df , as.data.frame(mcols(result_grl)))
}
#Retrieve the index for columns
indx_range_info <- colnames(result_df) %in% colnames_range_info
indx_reads_count <- grepl("ReadsCount",colnames(result_df))
indx_major_stat <- colnames(result_df) %in% c("log2FoldChange","pvalue","padj")
#Export the estimates and statistics of all GLM design
write.csv(
result_df[,!(indx_reads_count|indx_major_stat|indx_range_info)],
file = file.path(save_dir, "ADDInfo", "ADDInfo_GLM_allDesigns.csv")
)
#Report a matrix of RPKM
rpkm_df <- result_df[,indx_reads_count]
rpkm_df <- t(t((rpkm_df/(site_widths/1e3)))/colSums(rpkm_df))
colnames(rpkm_df) <- gsub("ReadsCount","RPKM",colnames(rpkm_df))
write.csv(
rpkm_df,
file = file.path(save_dir, "ADDInfo", "ADDInfo_RPKM.csv")
)
if (!any(grepl("Diff", colnames(exomePeak2Results(sep))))){
result_df <- result_df[,indx_range_info|indx_reads_count|indx_major_stat]
} else {
result_df <- result_df[,indx_range_info|indx_reads_count|indx_major_stat|colnames(result_df) %in% c("log2fcMod.Control.MLE","log2fcMod.Treated.MLE")]
colnames( result_df )[colnames(result_df) %in% c("log2fcMod.Control.MLE",
"log2fcMod.Treated.MLE",
"log2FoldChange")] <- c("ModLog2FC_control",
"ModLog2FC_treated",
"DiffModLog2FC")
}
if (any(format == "CSV")) write.csv(x = result_df,
file = file.path(save_dir,
paste0(file_name, ".csv")),
row.names = FALSE
)
scores <- -1 * log2(mcols(result_grl)$padj)
scores[is.na(scores)] <- 0
mcols(result_grl)$score <- scores
if (any(format == "BED")) export(
object = result_grl,
con = file.path(save_dir, paste0(file_name, ".bed")),
format = "BED"
)
if (reads_count) write.csv(x = assays(sep)[[1]],
file = file.path(save_dir,"ADDInfo","ADDInfo_ReadsCount.csv")
)
if (!is.null(GCsizeFactors(sep))&
GC_sizeFactors) write.csv(
x = assays(sep)[[2]],
file = file.path(save_dir,"ADDInfo","ADDInfo_SizeFactors.csv")
)
message(paste0("Result files have saved under the directory: '", file.path(save_dir)), "'.")
})
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#' @title Export the (Differential) Modification Peaks/Sites and their associated Statistics
#' @param sep a \code{\link{SummarizedExomePeak}} object.
#' @param format a \code{character} for the exported format, could be a vector that contains \code{c("CSV", "BED", "RDS")}.
#'
#' \describe{
#' \item{\strong{\code{CSV}}}{
#' export a comma separated values (CSV) table with the genomic location and LFC statistics.
#' }
#'
#' \item{\strong{\code{BED}}}{
#' export a BEDGraph file with the score column = -log2(adjusted p value).
#' }
#'
#' \item{\strong{\code{RDS}}}{
#' export the RDS file of the \code{\link{SummarizedExperiment}} object.
#' }
#' }
#'
#' @param save_dir a \code{character} for the name of the directory being saved; Default \code{= "exomePeak2_output"}.
#'
#' @param cut_off_pvalue a \code{numeric} value for the p value cutoff in the exported result; Default \code{= NULL}.
#'
#' @param cut_off_padj a \code{numeric} value for the adjusted p value cutoff in the exported result; Default \code{= 0.05}.
#'
#' @param cut_off_log2FC a \code{numeric} value for the log2 fold change cutoff of the exported result,
#' only the sites with abs(LFC) larger than this value are kept; Default \code{= 0}.
#'
#' @param min_num_of_positive a \code{numeric} value for the minimum number of reported sites.
#' If the number of remaining sites is less than this number after filtering, additional sites will be reported by the increasing order of the p value to meet this number.
#'
#' @param expected_direction a \code{character} for the expected differential modification direction, could be one in \code{c("hyper", "hypo", "both")}.
#'
#' \describe{
#' \item{\strong{\code{hyper}}}{
#' only report the peaks/sites with interactive LFC > 0.
#' }
#'
#' \item{\strong{\code{hypo}}}{
#' only report the peaks/sites with interactive LFC < 0.
#' }
#'
#' \item{\strong{\code{both}}}{
#' report the peaks/sites in both directions.
#' }
#' }
#'
#' This argument is useful when the treated group involves the perturbation of a known writer or eraser protein; Default "both".
#'
#' @param inhibit_filter a \code{logical} of whether to remove all the filters, this option is useful when quantification on single based site annotation; Default \code{= FALSE}.
#'
#' @param table_style a \code{character} for the style of the CSV table being exported, could be one in \code{c("bed","granges")}.
#'
#' \describe{
#' \item{\strong{\code{bed}}}{
#' the genomic locations in the table are represented by BEDgraph style.
#' }
#'
#' \item{\strong{\code{granges}}}{
#' the genomic locations in the table are represented by GRanges style.
#' }
#' }
#'
#' @param reads_count a \code{logical} of whether to export the reads count for each sample; Default \code{ = TRUE}.
#'
#' @param GC_sizeFactors a \code{logical} of whether to export the GC content correction size factors; Default \code{ = TRUE}.
#'
#' @examples
#'
#' ### Load the example SummarizedExomPeak object
#' f1 = system.file("extdata", "sep_ex_mod.rds", package="exomePeak2")
#'
#' sep <- readRDS(f1)
#'
#' ### Save the modification peaks/sites statistics on the current directory.
#' exportResults(sep)
#'
#' @importFrom rtracklayer export
#' @import GenomicRanges
#'
#' @aliases exportResults
#'
#' @rdname exportResults-methods
#'
#' @return none, a folder will be saved on the disk.
#'
#' @export
#'
setMethod("exportResults",
"SummarizedExomePeak",
function(sep,
format = c("CSV", "BED", "RDS"),
table_style = c("bed", "granges"),
save_dir = "exomePeak2_output",
cut_off_pvalue = NULL,
cut_off_padj = 0.1,
cut_off_log2FC = 0,
min_num_of_positive = 100,
expected_direction = c("both", "hyper", "hypo"),
inhibit_filter = FALSE,
reads_count = TRUE,
GC_sizeFactors = TRUE) {
#Check the input
stopifnot(cut_off_pvalue <= 1 & cut_off_pvalue >= 0)
stopifnot(cut_off_log2FC >= 0)
stopifnot(all(format %in% c("CSV", "BED", "RDS")))
table_style <- match.arg(table_style)
expected_direction <- match.arg(expected_direction)
#Create the saving directory
if (!dir.exists(save_dir)) {
dir.create(save_dir)
}
#Create the folder for the additional information
if (!dir.exists(file.path(save_dir,"ADDInfo"))) {
dir.create(file.path(save_dir,"ADDInfo"))
}
#Decide the file names
if (!any(grepl("Diff",colnames(exomePeak2Results( sep ))))) {
file_name <- "Mod"
} else{
file_name <- "DiffMod"
}
#Check if the users have calculated the log2FCs and their associated p-values
if (nrow(exomePeak2Results(sep)) == 0) {
if (any(sep$design_Treatment)) {
sep <- glmDM(sep)
} else{
sep <- glmM(sep)
}
}
#Generate row index for the exported modification in SummarizedExomePeak
if (inhibit_filter) {
#Do not filter the rows on modification
index_keep <- T
} else {
if (!any(grepl("Diff",colnames(exomePeak2Results( sep ))))) {
#Decide the filter on modification
decision_mod <- decision_deseq2(
Inf_RES = exomePeak2Results(sep),
log2FC_cut = cut_off_log2FC,
P_cut = cut_off_pvalue,
Padj_cut = cut_off_padj,
Min_mod = min(min_num_of_positive, nrow(exomePeak2Results(sep)))
)
#P.S. If no sites are reported, export all the p values that are not NA
index_keep <-
which(
(exomePeak2Results(sep)[[decision_mod$Cut_By_expected]] <= decision_mod$Cut_Val_expected) &
(exomePeak2Results(sep)$log2FoldChange > cut_off_log2FC)
)
} else {
#Decide the filter on differential modification
decision_dm <- decision_deseq2(
Inf_RES = exomePeak2Results(sep),
log2FC_cut = cut_off_log2FC,
P_cut = cut_off_pvalue,
Padj_cut = cut_off_padj,
Min_mod = min_num_of_positive,
Exp_dir = expected_direction
)
if (expected_direction == "both") {
indx_es <-
(abs(exomePeak2Results(sep)$log2FoldChange) > cut_off_log2FC)
} else {
if (expected_direction == "hyper") {
indx_es <- (exomePeak2Results(sep)$log2FoldChange > cut_off_log2FC)
} else {
indx_es <- (exomePeak2Results(sep)$log2FoldChange < -1 * cut_off_log2FC)
}
}
index_keep <-
which(exomePeak2Results(sep)[[decision_dm$Cut_By_expected]] < decision_dm$Cut_Val_expected & indx_es)
if (length(index_keep) == min_num_of_positive) {
warning(paste0("The number of significantly differentially modified sites < ",min_num_of_positive,", the insignificantly differentially modified sites are reported to reach the minimum site number of ",min_num_of_positive,"; to report the differential modification status on all modification sites, please set 'inhibit_filter = TRUE.'\n"))
}
}
}
#Subset the SummarizedExomePeak object according to user defined filters
sep <- sep[grepl("peak_", rownames(sep)),][index_keep,]
exomePeak2Results(sep) <- exomePeak2Results(sep)[grepl("peak_", rownames(sep)),][index_keep,]
id_num <- as.numeric(gsub("^.*_", "", rownames(sep)))
id_index <- order(id_num)
sep <- sep[id_index,]
exomePeak2Results(sep) <- exomePeak2Results(sep)[id_index,]
rownames(sep) <- paste0("peak_", rep(seq_along(id_num), table(id_num[id_index])))
rm(id_num, id_index)
if (any(format == "RDS")) saveRDS(sep, file.path(save_dir, paste0(file_name, ".rds")))
result_grl <- rowRanges(sep)
result_stat <- exomePeak2Results(sep)
#Correct the names in the resulting files
result_gr <- unlist(result_grl)
result_gr$gene_id <- gsub("\\.[0-9]*$", "", result_gr$gene_id)
rownames(result_stat) = NULL
#Generate the GRangesList file that is ready to export
indx_split <- names(result_gr)
names(result_gr) <- NULL
result_gr$gene_id[is.na(result_gr$gene_id)] <- " "
result_grl <- split(result_gr, indx_split)
#reorder the list
result_grl <- result_grl[order(as.numeric(gsub("^.*_", "", names(result_grl))))]
mcols(result_grl) <- result_stat
rm(result_gr,result_stat,indx_split)
id_num <- as.numeric(gsub("^.*_", "", names(result_grl)))
id_index <- order(id_num)
renamed_id <- paste0("peak_", rep(seq_along(id_num), table(id_num[id_index])))
result_grl <- result_grl[id_index,]
names(result_grl) <- renamed_id
#Generate the tables according to the style choices
if (table_style == "granges") {
result_df <- as.data.frame(result_grl)
result_df <-
result_df[, colnames(result_df) != "group"]
colnames(result_df)[colnames(result_df) == "group_name"] = "peak_name"
colnames_range_info <- colnames(result_df)
result_df <-
cbind(result_df, as.data.frame(mcols(result_grl))[rep(seq_along(result_grl), elementNROWS(result_grl)),])
site_widths <- sum(width(result_grl[rep(seq_along(result_grl), elementNROWS(result_grl))]))
} else {
scores <- -1 * log2(mcols(result_grl)$padj)
scores[is.na(scores)] <- 0
mcols(result_grl)$score <- scores
export(
object = result_grl,
con = file.path(save_dir, paste0(file_name, ".bed")),
format = "BED"
)
result_df <- read.table(file.path(save_dir, paste0(file_name, ".bed")),
header = F,
sep = "\t")
colnames(result_df) <- c(
"chr",
"chromStart",
"chromEnd",
"name",
"score",
"strand",
"thickStart",
"thickEnd",
"itemRgb",
"blockCount",
"blockSizes",
"blockStarts"
)
result_df$geneID <- vapply( result_grl, function(x) x$gene_id[1] , character(1))
colnames_range_info <- colnames(result_df)
mcols(result_grl) <- mcols(result_grl)[,!colnames(mcols(result_grl)) %in% "score"]
site_widths <- sum(width(result_grl))
result_df <- cbind(result_df , as.data.frame(mcols(result_grl)))
}
#Retrieve the index for columns
indx_range_info <- colnames(result_df) %in% colnames_range_info
indx_reads_count <- grepl("ReadsCount",colnames(result_df))
indx_major_stat <- colnames(result_df) %in% c("log2FoldChange","pvalue","padj")
#Export the estimates and statistics of all GLM design
write.csv(
result_df[,!(indx_reads_count|indx_major_stat|indx_range_info)],
file = file.path(save_dir, "ADDInfo", "ADDInfo_GLM_allDesigns.csv")
)
#Report a matrix of RPKM
rpkm_df <- result_df[,indx_reads_count]
rpkm_df <- t(t((rpkm_df/(site_widths/1e3)))/colSums(rpkm_df))
colnames(rpkm_df) <- gsub("ReadsCount","RPKM",colnames(rpkm_df))
write.csv(
rpkm_df,
file = file.path(save_dir, "ADDInfo", "ADDInfo_RPKM.csv")
)
if (!any(grepl("Diff", colnames(exomePeak2Results(sep))))){
result_df <- result_df[,indx_range_info|indx_reads_count|indx_major_stat]
} else {
result_df <- result_df[,indx_range_info|indx_reads_count|indx_major_stat|colnames(result_df) %in% c("log2fcMod.Control.MLE","log2fcMod.Treated.MLE")]
colnames( result_df )[colnames(result_df) %in% c("log2fcMod.Control.MLE",
"log2fcMod.Treated.MLE",
"log2FoldChange")] <- c("ModLog2FC_control",
"ModLog2FC_treated",
"DiffModLog2FC")
}
if (any(format == "CSV")) write.csv(x = result_df,
file = file.path(save_dir,
paste0(file_name, ".csv")),
row.names = FALSE
)
scores <- -1 * log2(mcols(result_grl)$padj)
scores[is.na(scores)] <- 0
mcols(result_grl)$score <- scores
if (any(format == "BED")) export(
object = result_grl,
con = file.path(save_dir, paste0(file_name, ".bed")),
format = "BED"
)
if (reads_count) write.csv(x = assays(sep)[[1]],
file = file.path(save_dir,"ADDInfo","ADDInfo_ReadsCount.csv")
)
if (!is.null(GCsizeFactors(sep))&
GC_sizeFactors) write.csv(
x = assays(sep)[[2]],
file = file.path(save_dir,"ADDInfo","ADDInfo_SizeFactors.csv")
)
message(paste0("Result files have saved under the directory: '", file.path(save_dir)), "'.")
})
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