latest files

R/runPAM.R

@@ -5,55 +5,55 @@
 #' @param moic.res An object returned by `getMOIC()` with one specified algorithm or `get\%algorithm_name\%` or `getConsensusMOIC()` with a list of multiple algorithms.
 #' @param test.expr A matrix of normalized expression testing data with rows for genes and columns for samples; FPKM or TPM without log2 transformation is recommended.
 #' @param gene.subset A string vector to indicate a subset of genes to be used.
-#' #' @return A list with the following components:
+#' @return A list with the following components:
 #'
 #'         \code{IGP}        a named numeric vector storing the in-group proportion (see \link[clusterRepro]{IGP.clusterRepro}).
 #'
 #'         \code{clust.res}  similar to `clust.res` returned by `getMOIC()` or `get%algorithm_name%` or `getConsensusMOIC()`.
 #'
 #'         \code{mo.method}  a string value indicating the method used for prediction.
 #' @details This function first trains a partition around medoids (PAM) classifier in the discovery (training) cohort
-#'  to predict the subtype for patients in the external validation (testing) cohort, 
-#'  and each sample in the validation cohort was assigned to a subtype label whose centroid had the highest Pearson correlation with the sample. 
+#'  to predict the subtype for patients in the external validation (testing) cohort,
+#'  and each sample in the validation cohort was assigned to a subtype label whose centroid had the highest Pearson correlation with the sample.
 #'  Finally, the in-group proportion (IGP) statistic will be performed to evaluate the similarity and reproducibility of the acquired subtypes between discovery and validation cohorts.
 #' @export
 #' @importFrom pamr pamr.train
 #' @importFrom clusterRepro IGP.clusterRepro
-#' @importFrom grDevices colorRamp 
+#' @importFrom grDevices colorRamp
 #' @references Tibshirani R, Hastie T, Narasimhan B and Chu G (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci, 99,6567–6572.
-#' 
+#'
 #' Kapp A V, Tibshirani R. (2007). Are clusters found in one dataset present in another dataset?. Biostatistics, 8(1):9-31.
 #' @examples # There is no example and please refer to vignette.
 runPAM <- function(train.expr  = NULL,
                    moic.res    = NULL,
                    test.expr   = NULL,
                    gene.subset = NULL) {
-  
-  # check sample 
+
+  # check sample
   comsam <- intersect(moic.res$clust.res$samID, colnames(train.expr))
   if(length(comsam) == nrow(moic.res$clust.res)) {
     message("--all samples matched.")
   } else {
     message(paste0("--",(nrow(moic.res$clust.res)-length(comsam))," samples mismatched from current subtypes."))
   }
-  
+
   moic.res$clust.res <- moic.res$clust.res[comsam, , drop = FALSE]
   train.expr <- train.expr[,comsam]
-  
+
   # check if using subset of genes
   if(is.null(gene.subset)) {
     comgene <- intersect(rownames(train.expr), rownames(test.expr))
     message(paste0("--a total of ",length(comgene)," genes shared and used."))
-    
+
   } else {
     comgene <- intersect(intersect(rownames(train.expr), rownames(test.expr)), gene.subset)
     message(paste0("--a total of ",length(comgene)," genes shared in the gene subset and used."))
   }
-  
+
   train.expr <- train.expr[comgene,]
   test.expr <- test.expr[comgene,]
   train.subt <- paste0("CS",moic.res$clust.res$clust)
-  
+
   # check data
   if(max(train.expr) < 25 | (max(train.expr) >= 25 & min(train.expr) < 0)) {
     message("--training expression profile seems to have been standardised (z-score or log transformation), no more action will be performed.")
@@ -64,7 +64,7 @@ runPAM <- function(train.expr  = NULL,
     train.expr <- log2(train.expr + 1)
   }
   train.expr <- as.data.frame(t(scale(t(train.expr))))
-  
+
   if(max(test.expr) < 25 | (max(test.expr) >= 25 & min(test.expr) < 0)) {
     message("--testing expression profile seems to have been standardised (z-score or log transformation), no more action will be performed.")
     test.expr <- test.expr
@@ -74,24 +74,24 @@ runPAM <- function(train.expr  = NULL,
     test.expr <- log2(test.expr + 1)
   }
   test.expr <- as.data.frame(t(scale(t(test.expr))))
-  
+
   # train a partition around medoids (PAM) classifier in training dataset
   mylist <- list(x = as.matrix(train.expr), # x should be the expression matrix
                  y = as.vector(train.subt)) # y should be a vector of classification
   pamr.classifier <- quiet(pamr.train(mylist))
-  
+
   # classify samples using centroids and calculates the in-group proportions
-  # pamr.pred.train <- IGP.clusterRepro(Centroids = pamr.classifier$centroids, 
+  # pamr.pred.train <- IGP.clusterRepro(Centroids = pamr.classifier$centroids,
   #                                     Data      = as.matrix(train.expr))
-  pamr.pred.test  <- IGP.clusterRepro(Centroids = pamr.classifier$centroids, 
+  pamr.pred.test  <- IGP.clusterRepro(Centroids = pamr.classifier$centroids,
                                       Data      = as.matrix(test.expr))
-  
+
   IGP <- pamr.pred.test$IGP; names(IGP) <- paste0("CS", 1:length(IGP))
-  
+
   ex.moic.res <- data.frame(samID = names(pamr.pred.test$Class),
                             clust = as.character(pamr.pred.test$Class),
                             row.names = names(pamr.pred.test$Class),
                             stringsAsFactors = FALSE)
-  
+
   return(list(IGP = IGP, clust.res = ex.moic.res, mo.method = "PAM"))
 }