@@ -1,8 +1,8 @@

 Package: StarBioTrek

 Type: Package

 Title: StarBioTrek

-Version: 1.9.0

-Date: 10-16-2017

+Version: 1.9.1

+Date: 11-09-2018

 Author: Claudia Cava,

     Isabella Castiglioni

 Maintainer: Claudia Cava <claudia.cava@ibfm.cnr.it>

@@ -141,11 +141,29 @@ xx <- as.list(x[mapped_genes])

 top3 <- matrix(0, length(xx), length(genes.by.pathway))

 rownames(top3) <- names(xx)

 colnames(top3)<- names(genes.by.pathway)

+

+

+

+for (j in  1:length(xx)){

+  for (k in  1:length(genes.by.pathway)){

+    if (length(intersect(xx[[j]],genes.by.pathway[[k]])!=0)){

+      

+     

+      

+       top3[j,k]<-names(xx[j]) 

+    }

+  }

+}

+

+

+

 for (j in  1:length(xx)){

   for (k in  1:length(genes.by.pathway)){

     if (length(intersect(xx[[j]],genes.by.pathway[[k]])!=0)){

-      top3[j,k]<-names(xx[j]) 

+

+

+     # top3[j,k]<-names(xx[j]) 

     }

   }

 }

@@ -333,7 +333,7 @@ ds_score_crtlk<-function(dataFilt,pathway){

   PEAmatrix_sd<-st_dv(dataFilt,pathway)

   df=combn(rownames(PEAmatrix_sd),2) 

   df=t(df)

-  ma<-matrix(0,nrow(df),ncol(PEAmatrix_sd)) # creo matrix che conterr? le somme delle dev st

+  ma<-matrix(0,nrow(df),ncol(PEAmatrix_sd)) # creo matrix che conterr le somme delle dev st

   colnames(ma)<-colnames(PEAmatrix_sd) # colnames conterr? il nome dei pazienti

   for ( p in 1: ncol(PEAmatrix_sd)){ # per ogni paziente

     patients <- (PEAmatrix_sd)[,p] 

@@ -444,7 +444,8 @@ y <- training$Target

 z<-subset(testing, select=-Target)

 zi<-testing$Target

-

+svm_tune <- tune(svm, train.x=x, train.y=y, 

+                 kernel="radial", ranges=list(cost=10^(-1:2), gamma=c(.5,1,2)))

 auc.df<-list()

 svm_model_after_tune_COMPL<-list()

 for( k in 2: ncol(training)){

@@ -452,11 +453,10 @@ for( k in 2: ncol(training)){

-  svm_tune <- tune(svm, train.x=x, train.y=y, 

-                   kernel="radial", ranges=list(cost=10^(-1:2), gamma=c(.5,1,2)),cross=10)

+

   #print(svm_tune)

-  svm_model_after_tune <- svm(Target ~ ., data=training[,c(1,k)], kernel="radial", cost=svm_tune$best.parameters$cost, gamma=svm_tune$best.parameters$gamma,cross=10,probability = TRUE)

+  svm_model_after_tune <- svm(Target ~ ., data=training[,c(1,k)], kernel="radial", cost=svm_tune$best.parameters$cost, gamma=svm_tune$best.parameters$gamma,probability = TRUE)

   #svm_model_after_tune <- svm(Target ~ ., data=training[,c(1,k)], kernel="radial", cost=svm_tune$best.parameters[1], gamma=svm_tune$best.parameters[2],cross=10,probability = TRUE)