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@@ -234,6 +234,14 @@ eucdistcrtlk <- function(dataFilt,pathway_exp){
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#heatmap(as.matrix(score_euc_dista_t),scale="column",margins =c(12,9))
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+#listpathgenees<-GE_matrix(dataFilt,pathway_exp)
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+#listpathgenees<-listpathgenees[!sapply(listpathgenees, is.null)]
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+#listpathgenees<-lapply(listpathgenees,function(x) apply(x,2,sd))
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+
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+
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+#listpathgenees<-listpathgenees[!sapply(listpathgenees, function(x) all(is.na(x)))]
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+#gsmatrix_sd<-do.call("rbind",listpathgenees)
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+
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#' @title For TCGA data get human pathway data and creates a measure of standard deviations among pathways
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#' @description stdv creates a matrix with standard deviation for pathways
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@@ -255,9 +263,6 @@ stdv<-function(gslist){
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#' @title For TCGA data get human pathway data and creates a measure of discriminating score among pathways
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#' @description dsscorecrtlk creates a matrix with discriminating score for pathways
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#' @param dataFilt TCGA matrix
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@@ -269,6 +274,13 @@ stdv<-function(gslist){
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dsscorecrtlk<-function(dataFilt,pathway_exp){
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listpathgenees<-GE_matrix(dataFilt,pathway_exp)
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PEAmatrix<-average(listpathgenees)
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+
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+ # standard deviation
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+ PEAmatrix_sd<-stdv(listpathgenees)
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+ r<-intersect(rownames(PEAmatrix),rownames(PEAmatrix_sd))
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+ PEAmatrix<-PEAmatrix[r,]
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+ PEAmatrix_sd<-PEAmatrix_sd[r,]
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+
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#step 5 distance
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# EUCLIDEA DISTANCE
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df=combn(rownames(PEAmatrix),2) # possibili relazioni tra i pathway
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@@ -281,7 +293,7 @@ dsscorecrtlk<-function(dataFilt,pathway_exp){
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distance<-dist(patients) # calcolo distanza EUCLIDEA tra le possibile combinazioni
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ma_d[,p]<-distance
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}
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- PEAmatrix_sd<-stdv(listpathgenees)
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+
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df=combn(rownames(PEAmatrix_sd),2)
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df=t(df)
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ma<-matrix(0,nrow(df),ncol(PEAmatrix_sd)) # creo matrix che conterr le somme delle dev st
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@@ -300,8 +312,9 @@ dsscorecrtlk<-function(dataFilt,pathway_exp){
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for( i in 1: ncol(pp2)){
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pp2[,i] <- as.numeric(as.character(pp2[,i]))
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-return(pp2)
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-}
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+
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+ }
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+ return(pp2)
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}
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