Revision 0b1d0035778c9fcc8374838885ba79094a91aa49 authored by Alexey Sergushichev on 27 February 2018, 15:45:02 UTC, committed by Alexey Sergushichev on 27 February 2018, 15:45:02 UTC
1 parent ce079fb
performKmeans.R
#' K-means clusterisation.
#'
#' \code{performKmeans} returns a vector of corresponding clusters for
#' each gene from a given ExpressionSet.
#'
#' @param es ExpressionSet object.
#'
#' @param columns List of specified columns' indices (optional), indices start from 0
#'
#' @param rows List of specified rows' indices (optional), indices start from 0
#'
#' @param k Expected number of clusters.
#'
#' @param replacena Method for replacing NA values
#' in series matrix (mean by default)
#'
#' @return Vector of corresponding clusters, serialized to JSON.
#'
#' @import Biobase
#'
#' @examples
#' \dontrun{
#' data(es)
#' performKmeans(es, k = 2)
#' }
performKmeans <- function(es, columns = c(), rows = c(), k,
replacena = "mean") {
assertthat::assert_that(k > 0)
es <- subsetES(es, columns=columns, rows=rows)
scaledExprs <- unname(t(scale(t(exprs(es)))))
naInd <- which(is.na(scaledExprs), arr.ind = TRUE)
if (nrow(naInd) > 0) {
replaceValues <- apply(scaledExprs, 1, replacena, na.rm=TRUE)
scaledExprs[naInd] <- replaceValues[naInd[,1]]
rowsToCluster <- is.finite(replaceValues)
} else {
rowsToCluster <- seq_len(nrow(scaledExprs))
}
km <- stats::kmeans(scaledExprs[rowsToCluster, ], k, iter.max = 100L)
res <- character(nrow(scaledExprs))
res[rowsToCluster] <- as.character(km$cluster)
return(jsonlite::toJSON(res))
}
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