R/dataset.merging.R

In consensusOV: Gene expression-based subtype classification for high-grade serous ovarian cancer

#' Merging all individual esets and merging them into a big eset
#'
#' @param esets The list containing all GSE file that need to be merged.
#' @param method either "unique" or "intersect" is use to for selecting geneid
#' @param standardization choose between "quantile", "robust.scaling", "scaling"
#'  or "none"
#' @param nthread number of threads (1 by default)
#' @return The merging eset
#' @importFrom limma normalizeBetweenArrays
#' @import Biobase
dataset.merging <-
    function (esets,
              method = c("union", "intersect"),
              standardization = c("quantile", "robust.scaling", "scaling", "none"),
              nthread = 1) {
        ## function merging all individual esets and merging them into a big eset
        #
        # Args:
        #   esets: The list containing all GSE file that need to be merged.
        #   duplication.checker: A marker, either TRUE or FALSE if you you want
        #                        to verify whether or not you have duplicate
        #                        samples into your master gene expression matrix.
        #   survdata: For t.fs and e.fs
        #   time.cens: maximum follow up (years)
        #   Method: either "unique" or "intersect" is use to for selecting geneid
        #

        method <- match.arg(method)
        standardization <- match.arg(standardization)

        ## all unique Entrez gene ids
        ## gene ids
        ugid <- lapply(esets, function(x) {
            return(Biobase::fData(x))
        })
        ugid <- do.call(rbind, ugid)
        ugid <-
            ugid[!is.na(ugid[, "EntrezGene.ID"]) &
                     !duplicated(ugid[, "EntrezGene.ID"]), , drop = FALSE]
        rownames(ugid) <-
            gsub(sprintf("(%s).", paste(names(esets), collapse = "|")), "",
                 rownames(ugid))
        switch (method,
                "union" = {
                    feature.merged <- ugid
                },
                "intersect" = {
                    feature.merged <-
                        lapply(esets, function(x) {
                            return(base::trimws(
                                as.character(
                                    Biobase::fData(x)[, "EntrezGene.ID"]
                                )
                            ))
                        })
                    feature.merged <- table(unlist(feature.merged))
                    feature.merged <-
                        names(feature.merged)[feature.merged == length(esets)]
                    feature.merged <-
                        ugid[match(feature.merged, base::trimws(
                            as.character(ugid[, "EntrezGene.ID"]))),
                            ,
                            drop = FALSE]
                },
                {
                    stop("Unknown method")
                })
        ## expression data
        exprs.merged <- lapply(esets, function (x, y) {
            ee <- Biobase::exprs(x)[is.element(rownames(exprs(x)),
                                               rownames(feature.merged)),]
            #print(dim(ee))
            eem <-
                matrix(
                    NA,
                    nrow = length(y),
                    ncol = ncol(ee),
                    dimnames = list(y, colnames(ee))
                )
            #print(dim(eem))
            #print(length(intersect(rownames(ee),rownames(eem))))
            eem[rownames(ee), colnames(ee)] <- ee
            return (eem)
        }, y = rownames(feature.merged))
        exprs.merged <- do.call(cbind, exprs.merged)
        ## clinical info
        ucid <-
            lapply(esets, function(x) {
                return(colnames(Biobase::pData(x)))
            })
        ucid <- table(unlist(ucid))
        ucid <- names(ucid)[ucid == length(esets)]
        clinicinfo.merged <- lapply(esets, function (x , y) {
            ee <- Biobase::pData(x)[, y, drop = FALSE]
        }, y = ucid)
        clinicinfo.merged <- do.call(gdata::combine, clinicinfo.merged)
        # if a data.source column already exists, remove it
        clinicinfo.merged$data.source <- NULL
        colnames(clinicinfo.merged)[
            which(colnames(clinicinfo.merged) == "source")
        ] <- "data.source"
        rownames(clinicinfo.merged) <- colnames(exprs.merged)
        #   rownames(clinicinfo.merged) <- gsub(   sprintf("(%s).",
        # paste(names(esets), collapse="|")), "", rownames(clinicinfo.merged)
        #   ## create a merged expressionSet object
        eset.merged <-
            ExpressionSet(
                assayData = exprs.merged,
                phenoData = AnnotatedDataFrame(data = clinicinfo.merged),
                featureData = AnnotatedDataFrame(data = feature.merged)
            )
        experimentData(eset.merged)@preprocessing <-
            list("normalization" = "mixed",
                 package = "unspecified",
                 version = "0")
        annotation(eset.merged) <- "mixed"
        ## subtyping
        # Note that experimentData does not subset according to other
        # ExpressionSet fields. For now, this section is commented out.
        #  sbtn <- lapply(esets, function (x) {
        #    return (colnames(getSubtype(eset=x, method="fuzzy")))
        #  })
        #  if (!all(sapply(sbtn, is.null))) {
        #    sbtn <- table(unlist(sbtn))
        #    if (!all(sbtn == length(esets))) {
        #       stop("Different subtyping across esets")
        #    }
        #    sclass <- lapply(esets, getSubtype, method="class")
        ##     nn <- unlist(sapply(sclass, names))
        ##     sclass <- unlist(sclass)
        ##     names(sclass) <- nn
        ##     names(sclass) <- names(esets)
        #    sclass <- unlist(sclass)
        #    names(sclass) <- unlist(sapply(esets, sampleNames))
        #    sfuzzy <- do.call(rbind, lapply(esets, getSubtype, method="fuzzy"))
        #    rownames(sfuzzy) <- sampleNames(eset.merged)
        #    scrisp <- do.call(rbind, lapply(esets, getSubtype, method="crisp"))
        #    message("going to set the subtype")
        #    eset.merged <- setSubtype(eset=eset.merged, subtype.class=sclass,
        #    subtype.fuzzy=sfuzzy, subtype.crisp=scrisp)
        #    rownames(fData(eset.merged)) <- fData(eset.merged)[,"EntrezGene.ID"]
        #    message("set the subtype complete for merged")
        #  }

        ## standardization
        switch(
            standardization,
            "none" = {
                ## do nothing
            },
            "quantile" = {
                ## robust scaling followed by quantile normalization
                ee <- exprs(eset.merged)
                # ee <- apply(ee, 2, genefu::rescale)
                splitix <-
                    parallel::splitIndices(nx = ncol(ee), ncl = nthread)
                mcres <-
                    parallel::mclapply(splitix, function(x, data) {
                        res <- apply(data[, x, drop = FALSE], 2, function (dx) {
                            return ((genefu::rescale(
                                dx, q = 0.05, na.rm = TRUE
                            ) - 0.5) * 2)
                        })
                        return (res)
                    }, data = ee, mc.cores = nthread)
                ee <- do.call(cbind, mcres)
                ## quantile normalization
                ee <-
                    normalizeBetweenArrays(object = ee, method = "quantile")
                exprs(eset.merged) <- ee
            },
            "robust.scaling" = {
                ## robust scaling
                ee <- exprs(eset.merged)
                # ee <- apply(ee, 2, genefu::rescale)
                splitix <-
                    parallel::splitIndices(nx = ncol(ee), ncl = nthread)
                mcres <-
                    parallel::mclapply(splitix, function(x, data) {
                        res <- apply(data[, x, drop = FALSE], 2, function (dx) {
                            return ((genefu::rescale(
                                dx, q = 0.05, na.rm = TRUE
                            ) - 0.5) * 2)
                        })
                        return (res)
                    }, data = ee, mc.cores = nthread)
                ee <- do.call(cbind, mcres)
                exprs(eset.merged) <- ee
            },
            "scaling" = {
                ## traditional scaling
                # robust scaling
                ee <- exprs(eset.merged)
                # ee <- apply(ee, 2, genefu::rescale)
                splitix <-
                    parallel::splitIndices(nx = ncol(ee), ncl = nthread)
                mcres <-
                    parallel::mclapply(splitix, function(x, data) {
                        return(apply(data[, x, drop = FALSE], 2, scale))
                    }, data = ee, mc.cores = nthread)
                ee <- do.call(cbind, mcres)
                exprs(eset.merged) <- ee
            },
            {
                stop("Unknown data standardization method")
            }
        )

        return (eset.merged)
    }