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#' wrapup_for_iSEE
#'
#' Combine data from a typical DESeq2 run
#'
#' Combines the DESeqDataSet input and DESeqResults into a SummarizedExperiment
#' object, which can be readily explored with iSEE.
#'
#' A typical usage would be after running the DESeq2 pipeline as specified in
#' one of the workflows which include this package, e.g. in the context of the
#' ideal package.
#'
#' @param dds A \code{\link{DESeqDataSet}} object.
#' @param res A \code{\link{DESeqResults}} object.
#'
#' @return A SummarizedExperiment object, with raw counts, normalized counts, and
#' variance-stabilizing transformed counts in the assay slots; and with colData
#' and rowData extracted from the corresponding input parameters
#'
#' @export
#'
#' @examples
#' # with simulated data...
#' library(DESeq2)
#' dds <- DESeq2::makeExampleDESeqDataSet(n=10000, m=8)
#' dds <- DESeq(dds)
#' res <- results(dds)
#' se <- wrapup_for_iSEE(dds, res)
#' # library(iSEE)
#' # iSEE(se)
#'
#' \dontrun{
#' # or with the well known airway package...
#' library(airway)
#' data(airway)
#' airway
#' dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
#' colData = colData(airway),
#' design=~cell+dex)
#' dds_airway <- DESeq2::DESeq(dds_airway)
#' res_airway <- DESeq2::results(dds_airway)
#' se_airway <- wrapup_for_iSEE(dds_airway, res_airway)
#' # iSEE(se_airway)
#' }
wrapup_for_iSEE <- function(dds, res) {
# sanity checks on the objects
stopifnot(all(rownames(dds) == rownames(res)))
# dds to vst
vst <- vst(dds)
# initialize the container
se <- SummarizedExperiment(
assays = List(
counts = counts(dds),
normcounts = counts(dds,normalized = TRUE),
vst_counts = assay(vst)
)
)
# adding colData, taken directly from the DESeqDataSet object
colData(se) <- colData(dds)
# extract contrast info
this_contrast <- sub(".*p-value: (.*)","\\1",mcols(res, use.names=TRUE)["pvalue","description"])
# getting the rowData from the dds itself
rdd <- rowData(dds)
# modifying in advance the DESeqResults object
res$log10_baseMean <- log10(res$baseMean)
res$log10_pvalue <- -log10(res$pvalue)
# and for the rowData
rdd$log10_dispersion <- log10(rdd$dispersion)
# adding rowData to se
rowData(se)[[paste0("DESeq2_",gsub(" ","_",this_contrast))]] <- res
# merging in the existing rowData slot
rowData(se) <- cbind(rowData(se), rdd)
return(se)
}
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