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#' cluster the samples
#'
#' This function clusters the samples into x clusters.
#' @usage Groups_Sup(me_TOP, me, number_of_k,TRw)
#'
#' @param me_TOP the matrix with the n top genes, usually the from
#' output of the function TopPAM
#' @param me the original expression matrix. (with genes in rows and samples in columns).
#' @param number_of_k the number of clusters
#' @param TRw threshold for the elemenation of the samples with a Silhouette width lower than TRw.
#' Default value is -1.
#'
#' @examples
#'
#' ## load data
#' library(org.Hs.eg.db)
#' data(rna)
#' me_x=rna
#' res<-AutoPipe::TopPAM(me_x,max_clusters = 8, TOP=100)
#' me_TOP=res[[1]]
#' number_of_k=res[[3]]
#' File_genes=Groups_Sup(me_TOP, me=me_x, number_of_k,TRw=-1)
#' groups_men=File_genes[[2]]
#' me_x=File_genes[[1]]
#'
#' @export Groups_Sup
Groups_Sup=function(me_TOP, me, number_of_k,TRw=-1){
number_of_k=number_of_k
pamx <- cluster::pam(t(me_TOP), k=number_of_k)
si <- cluster::silhouette(pamx)
graphics::barplot(si[,3])
me_TOP_s=me_TOP[,rownames(si[si[,3]>TRw, ])]
dim(me_TOP_s)
groups_men=as.data.frame(si[si[,3]>TRw, ])
graphics::barplot(groups_men$sil_width)
(rownames(groups_men) %in% colnames(me) )
Exp=(me[, rownames(groups_men)])
dim(Exp)
return(list(Exp, groups_men))
}
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