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run_DESeq <- function(counts, conds, cutoff, n, runID) {
# Preparing variables for DESeq run
cds <- newCountDataSet(counts, conds)
cds <- estimateSizeFactors(cds)
if (n > 1) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "per-condition",
sharingMode = "maximum", fitType = "local"), error = function(e) NULL)
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "pooled",
sharingMode = "maximum", fitType = "local"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "per-condition",
sharingMode = "maximum", fitType = "parametric"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cds <- estimateDispersions(cds, method = "pooled",
sharingMode = "maximum", fitType = "parametric")
}
cds <- cdsnew
} else {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "fit-only", fitType = "parametric"),
error = function(e) NULL)
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "maximum", fitType = "parametric"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "fit-only", fitType = "local"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cds <- estimateDispersions(cds, method = "blind",
sharingMode = "maximum", fitType = "local")
}
cds <- cdsnew
}
res <- nbinomTest(cds, "N", "T")
res[is.na(res)] <- 0
resSig <- res[res$pval < cutoff, ]
# Preparing results to return
result <- new("Result")
result@data <- res[order(as.numeric(res$pval)), ]
result@id <- result@data$id
result@pval <- result@data$pval
return(result)
}
run_DESeq_uqn <- function(counts, conds, cutoff, n, runID) {
# Preparing variables for DESeq run
cds <- newCountDataSet(UQNnormalization(counts)$normCounts, conds)
sizeFactors(cds) <- c(rep(1, length(conds)))
if (n > 1) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "per-condition",
sharingMode = "maximum", fitType = "local"), error = function(e) NULL)
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "pooled",
sharingMode = "maximum", fitType = "local"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "per-condition",
sharingMode = "maximum", fitType = "parametric"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cds <- estimateDispersions(cds, method = "pooled",
sharingMode = "maximum", fitType = "parametric")
}
cds <- cdsnew
} else {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "fit-only", fitType = "parametric"),
error = function(e) NULL)
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "maximum", fitType = "parametric"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "fit-only", fitType = "local"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cds <- estimateDispersions(cds, method = "blind",
sharingMode = "maximum", fitType = "local")
}
cds <- cdsnew
}
res <- nbinomTest(cds, "N", "T")
res[is.na(res)] <- 0
resSig <- res[res$pval < cutoff, ]
# Preparing results to return
result <- new("Result")
result@data <- res[order(as.numeric(res$pval)), ]
result@id <- result@data$id
result@pval <- result@data$pval
return(result)
}
run_DESeq_Mode <- function(counts, conds, cutoff, n, runID, winSize) {
# Preparing variables for DESeq run
cds <- newCountDataSet(normalizeData(counts, conds, runID,
winSize)$normCounts, conds)
sizeFactors(cds) <- c(rep(1, length(conds)))
# cds <- newCountDataSet(counts,conds); sizeFactors(cds) <-
# computeNormalization(runID, winSizePercentage, minReads);
if (n > 1) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "per-condition",
sharingMode = "maximum", fitType = "local"), error = function(e) NULL)
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "pooled",
sharingMode = "maximum", fitType = "local"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "per-condition",
sharingMode = "maximum", fitType = "parametric"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cds <- estimateDispersions(cds, method = "pooled",
sharingMode = "maximum", fitType = "parametric")
}
cds <- cdsnew
} else {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "fit-only", fitType = "parametric"),
error = function(e) NULL)
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "maximum", fitType = "parametric"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "fit-only", fitType = "local"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cds <- estimateDispersions(cds, method = "blind",
sharingMode = "maximum", fitType = "local")
}
cds <- cdsnew
}
res <- nbinomTest(cds, "N", "T")
res[is.na(res)] <- 0
resSig <- res[res$pval < cutoff, ]
# Preparing results to return
result <- new("Result")
result@data <- res[order(as.numeric(res$pval)), ]
result@id <- result@data$id
result@pval <- result@data$pval
return(result)
}
run_DESeq_nde <- function(counts, DElist, conds, cutoff, n, runID) {
# Preparing variables for DESeq run
cds <- newCountDataSet(normalizeNDE(counts, DElist, runID)$normCounts,
conds)
sizeFactors(cds) <- c(rep(1, n * 2))
# cds <- newCountDataSet(counts,conds); sizeFactors(cds) <-
# computeNormalization(runID, winSizePercentage, minReads);
if (n > 1) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "per-condition",
sharingMode = "maximum", fitType = "local"), error = function(e) NULL)
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "pooled",
sharingMode = "maximum", fitType = "local"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "per-condition",
sharingMode = "maximum", fitType = "parametric"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cds <- estimateDispersions(cds, method = "pooled",
sharingMode = "maximum", fitType = "parametric")
}
cds <- cdsnew
} else {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "fit-only", fitType = "parametric"),
error = function(e) NULL)
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "maximum", fitType = "parametric"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cdsnew <- tryCatch(estimateDispersions(cds, method = "blind",
sharingMode = "fit-only", fitType = "local"),
error = function(e) NULL)
}
if (is.null(cdsnew)) {
cds <- estimateDispersions(cds, method = "blind",
sharingMode = "maximum", fitType = "local")
}
cds <- cdsnew
}
res <- nbinomTest(cds, "N", "T")
res[is.na(res)] <- 0
resSig <- res[res$pval < cutoff, ]
# Preparing results to return
result <- new("Result")
result@data <- res[order(as.numeric(res$pval)), ]
result@id <- result@data$id
result@pval <- result@data$pval
return(result)
}
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