@@ -1,5 +1,5 @@

 Package: GSVA

-Version: 1.25.5

+Version: 1.25.6

 Title: Gene Set Variation Analysis for microarray and RNA-seq data

 Authors@R: c(person("Justin", "Guinney", role=c("aut", "cre"), email="justin.guinney@sagebase.org"),

              person("Robert", "Castelo", role="aut", email="robert.castelo@upf.edu"),

@@ -9,19 +9,20 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="list"),

           function(expr, gset.idx.list, annotation,

   method=c("gsva", "ssgsea", "zscore", "plage"),

   kcdf=c("Gaussian", "Poisson", "none"),

-  rnaseq=FALSE,

+  rnaseq=FALSE, ## deprecated

   abs.ranking=FALSE,

   min.sz=1,

   max.sz=Inf,

-  no.bootstraps=0, 

-  bootstrap.percent = .632, 

+  no.bootstraps=0, ## deprecated

+  bootstrap.percent = .632, ## deprecated

   parallel.sz=0, 

   parallel.type="SOCK",

   mx.diff=TRUE,

   tau=switch(method, gsva=1, ssgsea=0.25, NA),

-  kernel=TRUE,

+  kernel=TRUE, ## deprecated

   ssgsea.norm=TRUE,

-  verbose=TRUE)

+  verbose=TRUE,

+  return.old.value=FALSE) ## transient argument for deprecating 'no.bootstraps' and 'bootstrap.percent'

 {

   method <- match.arg(method)

   kcdf <- match.arg(kcdf)

@@ -32,6 +33,9 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="list"),

   if (!missing(kernel))

     warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.")

+  if (no.bootstraps > 0)

+    warning("The argument 'no.bootstraps' is deprecated and will be removed in the next release of GSVA. This implies that the 'gsva()' function with the default argument 'method=\"gsva\"' only returns a matrix of GSVA enrichment scores. To obtain the same output in the form of a list as in previous versions you can set 'return.old.value=TRUE' during this release but this argument will not be available anymore in the next release.")

+

   ## filter out genes with constant expression values

   sdGenes <- Biobase::esApply(expr, 1, sd)

   if (any(sdGenes == 0) || any(is.na(sdGenes))) {

@@ -81,28 +85,31 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="list"),

   eScoEset <- new("ExpressionSet", exprs=eSco$es.obs, phenoData=phenoData(expr),

                   experimentData=experimentData(expr), annotation="")

-	return(list(es.obs=eScoEset,

-				      bootstrap=eSco$bootstrap,

-              p.vals.sign=eSco$p.vals.sign))

+  rval <- eScoEset

+  if (return.old.value) ## to be removed in the next release

+    rval <- list(es.obs=eScoEset, bootstrap=eSco$bootstrap, p.vals.sign=eSco$p.vals.sign)

+

+  rval

 })

 setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="GeneSetCollection"),

           function(expr, gset.idx.list, annotation,

   method=c("gsva", "ssgsea", "zscore", "plage"),

   kcdf=c("Gaussian", "Poisson", "none"),

-  rnaseq=FALSE,

+  rnaseq=FALSE, ## deprecated

   abs.ranking=FALSE,

   min.sz=1,

   max.sz=Inf,

-  no.bootstraps=0, 

-  bootstrap.percent = .632, 

+  no.bootstraps=0, ## deprecated

+  bootstrap.percent = .632, ## deprecated

   parallel.sz=0, 

   parallel.type="SOCK",

   mx.diff=TRUE,

   tau=switch(method, gsva=1, ssgsea=0.25, NA),

-  kernel=TRUE,

+  kernel=TRUE, ## deprecated

   ssgsea.norm=TRUE,

-  verbose=TRUE)

+  verbose=TRUE,

+  return.old.value=FALSE) ## transient argument for deprecating 'no.bootstraps' and 'bootstrap.percent'

 {

   method <- match.arg(method)

   kcdf <- match.arg(kcdf)

@@ -113,6 +120,9 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="GeneSetCollecti

   if (!missing(kernel))

     warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.")

+  if (no.bootstraps > 0)

+    warning("The argument 'no.bootstraps' is deprecated and will be removed in the next release of GSVA. This implies that the 'gsva()' function with the default argument 'method=\"gsva\"' only returns a matrix of GSVA enrichment scores. To obtain the same output in the form of a list as in previous versions you can set 'return.old.value=TRUE' during this release but this argument will not be available anymore in the next release.")

+

   ## filter out genes with constant expression values

   sdGenes <- Biobase::esApply(expr, 1, sd)

   if (any(sdGenes == 0) || any(is.na(sdGenes))) {

@@ -166,28 +176,31 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="GeneSetCollecti

   eScoEset <- new("ExpressionSet", exprs=eSco$es.obs, phenoData=phenoData(expr),

                   experimentData=experimentData(expr), annotation="")

-	return(list(es.obs=eScoEset,

-				      bootstrap=eSco$bootstrap,

-              p.vals.sign=eSco$p.vals.sign))

+  rval <- eScoEset

+  if (return.old.value) ## to be removed in the next release

+    rval <- list(es.obs=eScoEset, bootstrap=eSco$bootstrap, p.vals.sign=eSco$p.vals.sign)

+

+  rval

 })

 setMethod("gsva", signature(expr="matrix", gset.idx.list="GeneSetCollection"),

           function(expr, gset.idx.list, annotation,

   method=c("gsva", "ssgsea", "zscore", "plage"),

   kcdf=c("Gaussian", "Poisson", "none"),

-  rnaseq=FALSE,

+  rnaseq=FALSE, ## deprecated

   abs.ranking=FALSE,

   min.sz=1,

   max.sz=Inf,

-  no.bootstraps=0, 

-  bootstrap.percent = .632, 

+  no.bootstraps=0, ## deprecated

+  bootstrap.percent = .632,  ## deprecated

   parallel.sz=0, 

   parallel.type="SOCK",

   mx.diff=TRUE,

   tau=switch(method, gsva=1, ssgsea=0.25, NA),

-  kernel=TRUE,

+  kernel=TRUE, ## deprecated

   ssgsea.norm=TRUE,

-  verbose=TRUE)

+  verbose=TRUE,

+  return.old.value=FALSE) ## transient argument for deprecating 'no.bootstraps' and 'bootstrap.percent'

 {

   method <- match.arg(method)

   kcdf <- match.arg(kcdf)

@@ -198,6 +211,9 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="GeneSetCollection"),

   if (!missing(kernel))

     warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.")

+  if (no.bootstraps > 0)

+    warning("The argument 'no.bootstraps' is deprecated and will be removed in the next release of GSVA. This implies that the 'gsva()' function with the default argument 'method=\"gsva\"' only returns a matrix of GSVA enrichment scores. To obtain the same output in the form of a list as in previous versions you can set 'return.old.value=TRUE' during this release but this argument will not be available anymore in the next release.")

+

   ## filter out genes with constant expression values

   sdGenes <- apply(expr, 1, sd)

   if (any(sdGenes == 0) || any(is.na(sdGenes))) {

@@ -248,28 +264,34 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="GeneSetCollection"),

       kernel <- FALSE

   }

-  .gsva(expr, mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking,

-        no.bootstraps, bootstrap.percent, parallel.sz, parallel.type,

-        mx.diff, tau, kernel, ssgsea.norm, verbose)

+  rval <- .gsva(expr, mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking,

+                no.bootstraps, bootstrap.percent, parallel.sz, parallel.type,

+                mx.diff, tau, kernel, ssgsea.norm, verbose)

+

+  if (method == "gsva" && !return.old.value) ## to be removed in the next release

+    rval <- rval$es.obs

+

+  rval

 })

 setMethod("gsva", signature(expr="matrix", gset.idx.list="list"),

           function(expr, gset.idx.list, annotation,

   method=c("gsva", "ssgsea", "zscore", "plage"),

   kcdf=c("Gaussian", "Poisson", "none"),

-  rnaseq=FALSE,

+  rnaseq=FALSE, ## deprecated

   abs.ranking=FALSE,

   min.sz=1,

   max.sz=Inf,

-  no.bootstraps=0, 

-  bootstrap.percent = .632, 

+  no.bootstraps=0, ## deprecated

+  bootstrap.percent = .632, ## deprecated

   parallel.sz=0, 

   parallel.type="SOCK",

   mx.diff=TRUE,

   tau=switch(method, gsva=1, ssgsea=0.25, NA),

-  kernel=TRUE,

+  kernel=TRUE, ## deprecated

   ssgsea.norm=TRUE,

-  verbose=TRUE)

+  verbose=TRUE,

+  return.old.value=FALSE) ## transient argument for deprecating 'no.bootstraps' and 'bootstrap.percent'

 {

   method <- match.arg(method)

   kcdf <- match.arg(kcdf)

@@ -280,6 +302,9 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="list"),

   if (!missing(kernel))

     warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.")

+  if (no.bootstraps > 0)

+    warning("The argument 'no.bootstraps' is deprecated and will be removed in the next release of GSVA. This implies that the 'gsva()' function with the default argument 'method=\"gsva\"' only returns a matrix of GSVA enrichment scores. To obtain the same output in the form of a list as in previous versions you can set 'return.old.value=TRUE' during this release but this argument will not be available anymore in the next release.")

+

   ## filter out genes with constant expression values

   sdGenes <- apply(expr, 1, sd)

   if (any(sdGenes == 0) || any(is.na(sdGenes))) {

@@ -318,9 +343,14 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="list"),

       kernel <- FALSE

   }

-  .gsva(expr, mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking, no.bootstraps,

-        bootstrap.percent, parallel.sz, parallel.type,

-        mx.diff, tau, kernel, ssgsea.norm, verbose)

+  rval <- .gsva(expr, mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking, no.bootstraps,

+                bootstrap.percent, parallel.sz, parallel.type,

+                mx.diff, tau, kernel, ssgsea.norm, verbose)

+

+  if (method == "gsva" && !return.old.value) ## to be removed in the next release

+    rval <- rval$es.obs

+

+  rval

 })

 .gsva <- function(expr, gset.idx.list,

@@ -452,7 +482,7 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="list"),

 			n.cycles <- floor(no.bootstraps / parallel.sz)

 			for(i in 1:n.cycles){

 				if(verbose) cat("bootstrap cycle ", i, "\n")

-				r <- clEvalQ(cl, GSVA:::compute.geneset.es(expr, gset.idx.list, 

+				r <- clEvalQ(cl, compute.geneset.es(expr, gset.idx.list, 

 								sample(n.samples, bootstrap.nsamples, replace=T),

 								rnaseq=rnaseq, abs.ranking=abs.ranking, mx.diff=mx.diff,

                 tau=tau, kernel=kernel, verbose=FALSE, parallel.sz=1))

@@ -271,17 +271,17 @@ gsva_information <- function(input, output, session) {

   else

   {

-    resultInformation <- matrix(data = c(input$matrixVar,input$genesetVar,ncol(generated_gsva$es.obs),nrow(generated_gsva$es.obs)), nrow = 1, ncol = 4)

+    resultInformation <- matrix(data = c(input$matrixVar,input$genesetVar,ncol(generated_gsva),nrow(generated_gsva)), nrow = 1, ncol = 4)

     colnames(resultInformation) <- c("Matrix used","GeneSet used", "Col num", "Row num")

     output$result <- renderTable(resultInformation)

-    if(class(generated_gsva$es.obs) == "ExpressionSet") #If the generated gsva is an ExpressionSet

+    if(class(generated_gsva) == "ExpressionSet") #If the generated gsva is an ExpressionSet

     {

-      expressionSetObs <- exprs(generated_gsva$es.obs)

+      expressionSetObs <- exprs(generated_gsva)

       output$plot <- renderPlot(multidensity(as.list(as.data.frame(expressionSetObs)), legend=NA, las=1, xlab=sprintf("%s scores", input$method), main="", lwd=2)) 

     }

     else

     {

-      output$plot <- renderPlot(multidensity(as.list(as.data.frame(generated_gsva$es.obs)), legend=NA, las=1, xlab=sprintf("%s scores", input$method), main="", lwd=2))

+      output$plot <- renderPlot(multidensity(as.list(as.data.frame(generated_gsva)), legend=NA, las=1, xlab=sprintf("%s scores", input$method), main="", lwd=2))

     }

     tagList(

       downloadButton('downloadData', 'Download'),

@@ -304,15 +304,15 @@ download_handler <- function(input, output, session) {

       }

       else

       {

-        if(class(generated_gsva$es.obs) == "ExpressionSet") #If the generated gsva es.obs is an ExpressionSet

+        if(class(generated_gsva) == "ExpressionSet") #If the generated gsva result value is an ExpressionSet

         {

-          expressionSetObs <- exprs(generated_gsva$es.obs)

+          expressionSetObs <- exprs(generated_gsva)

           dataFrameObs <- as.data.frame(expressionSetObs)

           write.csv(dataFrameObs, file)

         }

         else

         {

-          dataFrameObs <- as.data.frame(generated_gsva$es.obs)

+          dataFrameObs <- as.data.frame(generated_gsva)

           write.csv(dataFrameObs, file)

         } 

       }

Binary files a/inst/extdata/cache4vignette_leukemia_es.RData and b/inst/extdata/cache4vignette_leukemia_es.RData differ

@@ -29,7 +29,8 @@ Estimates GSVA enrichment scores.

     tau=switch(method, gsva=1, ssgsea=0.25, NA),

     kernel=TRUE,

     ssgsea.norm=TRUE,

-    verbose=TRUE)

+    verbose=TRUE,

+    return.old.value=FALSE)

 \S4method{gsva}{ExpressionSet,GeneSetCollection}(expr, gset.idx.list, annotation,

     method=c("gsva", "ssgsea", "zscore", "plage"),

     kcdf=c("Gaussian", "Poisson", "none"),

@@ -45,7 +46,8 @@ Estimates GSVA enrichment scores.

     tau=switch(method, gsva=1, ssgsea=0.25, NA),

     kernel=TRUE,

     ssgsea.norm=TRUE,

-    verbose=TRUE)

+    verbose=TRUE,

+    return.old.value=FALSE)

 \S4method{gsva}{matrix,GeneSetCollection}(expr, gset.idx.list, annotation,

     method=c("gsva", "ssgsea", "zscore", "plage"),

     kcdf=c("Gaussian", "Poisson", "none"),

@@ -61,7 +63,8 @@ Estimates GSVA enrichment scores.

     tau=switch(method, gsva=1, ssgsea=0.25, NA),

     kernel=TRUE,

     ssgsea.norm=TRUE,

-    verbose=TRUE)

+    verbose=TRUE,

+    return.old.value=FALSE)

 \S4method{gsva}{matrix,list}(expr, gset.idx.list, annotation,

     method=c("gsva", "ssgsea", "zscore", "plage"),

     kcdf=c("Gaussian", "Poisson", "none"),

@@ -77,7 +80,8 @@ Estimates GSVA enrichment scores.

     tau=switch(method, gsva=1, ssgsea=0.25, NA),

     kernel=TRUE,

     ssgsea.norm=TRUE,

-    verbose=TRUE)

+    verbose=TRUE,

+    return.old.value=FALSE)

 }

 \arguments{

   \item{expr}{Gene expression data which can be given either as an \code{ExpressionSet}

@@ -118,8 +122,10 @@ Estimates GSVA enrichment scores.

             enriched on either extreme (high or low) will be regarded as 'highly' activated.}

   \item{min.sz}{Minimum size of the resulting gene sets.}

   \item{max.sz}{Maximum size of the resulting gene sets.}

-  \item{no.bootstraps}{Number of bootstrap iterations to perform.}

-  \item{bootstrap.percent}{.632 is the ideal percent samples bootstrapped.}

+  \item{no.bootstraps}{Number of bootstrap iterations to perform. This argument has been deprecated and will

+                       be removed in the next release.}

+  \item{bootstrap.percent}{.632 is the ideal percent samples bootstrapped. This argument has been deprecated and

+                           will be removed in the next release.}

   \item{parallel.sz}{Number of processors to use when doing the calculations in parallel.

                      This requires to previously load either the \code{parallel} or the

                      \code{snow} library. If \code{parallel} is loaded and this argument

@@ -145,6 +151,11 @@ Estimates GSVA enrichment scores.

                      the minimum and the maximum, as described in their paper. When \code{ssgsea.norm=FALSE}

                      this last normalization step is skipped.}

   \item{verbose}{Gives information about each calculation step. Default: \code{FALSE}.}

+  \item{return.old.value}{Logical, set to \code{FALSE} (default) has no effect but when \code{return.old.value=TRUE},

+                          then the return value takes form of a \code{list} object as in previous versions of

+                          GSVA. This argument will be present only in this release for backward compability

+                          purposes during the deprecation of the arguments \code{no.bootstraps} and \code{bootstrap.percent}

+                          and will dissappear in the next release.}

 }

 \details{

@@ -216,7 +227,7 @@ fit <- eBayes(fit)

 topTable(fit, coef="sampleGroup2vs1")

 ## estimate GSVA enrichment scores for the three sets

-gsva_es <- gsva(y, geneSets, mx.diff=1)$es.obs

+gsva_es <- gsva(y, geneSets, mx.diff=1)

 ## fit the same linear model now to the GSVA enrichment scores

 fit <- lmFit(gsva_es, design)

@@ -172,8 +172,8 @@ X <- matrix(rnorm(p*n), nrow=p, dimnames=list(1:p, 1:n))

 dim(X)

 gs <- as.list(sample(min.sz:max.sz, size=nGS, replace=TRUE)) ## sample gene set sizes

 gs <- lapply(gs, function(n, p) sample(1:p, size=n, replace=FALSE), p) ## sample gene sets

-es.max <- gsva(X, gs, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)$es.obs

-es.dif <- gsva(X, gs, mx.diff=TRUE, verbose=FALSE, parallel.sz=1)$es.obs

+es.max <- gsva(X, gs, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)

+es.dif <- gsva(X, gs, mx.diff=TRUE, verbose=FALSE, parallel.sz=1)

 @

 \begin{center}

@@ -409,7 +409,7 @@ GSVA enrichment scores, we leave deliberately unchanged the default argument

 <<>>=

 cache(leukemia_es <- gsva(leukemia_filtered_eset, c2BroadSets,

-                           min.sz=10, max.sz=500, verbose=TRUE)$es.obs,

+                           min.sz=10, max.sz=500, verbose=TRUE),

                            dir=cacheDir, prefix=cachePrefix)

 @

 We test whether there is a difference between the GSVA enrichment scores from each

@@ -572,7 +572,7 @@ GSVA enrichment scores for the gene sets contained in \Robject{brainTxDbSets}

 are calculated, in this case using \Robject{mx.diff=FALSE},  as follows:

 <<>>=

-gbm_es <- gsva(gbm_eset, brainTxDbSets, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)$es.obs

+gbm_es <- gsva(gbm_eset, brainTxDbSets, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)

 @

 Figure \ref{gbmSignature} shows the GSVA enrichment scores obtained for the

 up-regulated gene sets across the samples of the four GBM subtypes. As expected,

@@ -664,7 +664,7 @@ runSim <- function(p, n, gs.sz, S2N, fracDEgs) {

   geneSets <- list(H1GeneSet=paste0("g", 1:(gs.sz)),

                    H0GeneSet=paste0("g", (gs.sz+1):(2*gs.sz)))

-  es.gsva <- gsva(M, geneSets, verbose=FALSE, parallel.sz=1)$es.obs

+  es.gsva <- gsva(M, geneSets, verbose=FALSE, parallel.sz=1)

   es.ss <- gsva(M, geneSets, method="ssgsea", verbose=FALSE, parallel.sz=1)

   es.z <- gsva(M, geneSets, method="zscore", verbose=FALSE, parallel.sz=1)

   es.plage <- gsva(M, geneSets, method="plage", verbose=FALSE, parallel.sz=1)

@@ -849,10 +849,10 @@ argument should remain unchanged.

 <<<>>=

 esmicro <- gsva(huangArrayRMAnoBatchCommon_eset, canonicalC2BroadSets, min.sz=5, max.sz=500,

-                mx.diff=TRUE, verbose=FALSE, parallel.sz=1)$es.obs

+                mx.diff=TRUE, verbose=FALSE, parallel.sz=1)

 dim(esmicro)

 esrnaseq <- gsva(pickrellCountsArgonneCQNcommon_eset, canonicalC2BroadSets, min.sz=5, max.sz=500,

-                 kcdf="Poisson", mx.diff=TRUE, verbose=FALSE, parallel.sz=1)$es.obs

+                 kcdf="Poisson", mx.diff=TRUE, verbose=FALSE, parallel.sz=1)

 dim(esrnaseq)

 @

 To compare expression values from both technologies we are going to transform