Browse code

Added first version of a GSVA shiny app.

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/GSVA@130609 bc3139a8-67e5-0310-9ffc-ced21a209358

Robert Castelo authored on 22/06/2017 12:53:26
Showing 4 changed files

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@@ -1,10 +1,12 @@
1 1
 Package: GSVA
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-Version: 1.25.2
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+Version: 1.25.3
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 Title: Gene Set Variation Analysis for microarray and RNA-seq data
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 Authors@R: c(person("Justin", "Guinney", role=c("aut", "cre"), email="justin.guinney@sagebase.org"),
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-             person("Robert", "Castelo", role="aut", email="robert.castelo@upf.edu"))
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+             person("Robert", "Castelo", role="aut", email="robert.castelo@upf.edu"),
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+             person("Joan", "Fernandez", role="ctb", email="joanfernandez1331@gmail.com"))
6 7
 Depends: R (>= 2.13.0)
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-Imports: methods, BiocGenerics, Biobase, GSEABase (>= 1.17.4)
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+Imports: methods, BiocGenerics, Biobase, GSEABase (>= 1.17.4),
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+         geneplotter, shiny, shinythemes
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 Suggests: limma, RColorBrewer, genefilter, mclust,
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           edgeR, snow, parallel, GSVAdata
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 Description: Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner.
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@@ -2,6 +2,7 @@ useDynLib(GSVA)
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 import(methods)
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 import(BiocGenerics)
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+import(shiny)
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 importClassesFrom(Biobase, ExpressionSet)
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 importClassesFrom(GSEABase, GeneSetCollection)
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@@ -17,12 +18,19 @@ importFrom(graphics, plot)
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 importFrom(stats, ecdf,
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                   na.omit)
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 importFrom(utils, setTxtProgressBar,
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-                  txtProgressBar)
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-importFrom(Biobase, exprs)
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-importFrom(Biobase, annotation)
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-importFrom(GSEABase, AnnoOrEntrezIdentifier)
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-importFrom(GSEABase, mapIdentifiers)
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+                  txtProgressBar,
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+                  read.csv,
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+                  write.csv)
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+importFrom(Biobase, exprs,
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+                    annotation)
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+importFrom(GSEABase, AnnoOrEntrezIdentifier,
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+                     mapIdentifiers,
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+                     getGmt)
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+importFrom(geneplotter, multidensity)
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+importFrom(shinythemes, shinytheme)
25 31
 
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-exportMethods("gsva",
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-              "filterGeneSets",
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-              "computeGeneSetsOverlap")
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+exportMethods(gsva,
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+              filterGeneSets,
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+              computeGeneSetsOverlap)
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+
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+export(igsva)
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new file mode 100644
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@@ -0,0 +1,366 @@
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+#'
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+#' @importFrom shiny HTML actionButton animationOptions checkboxGroupInput column div downloadHandler downloadLink eventReactive fileInput fluidPage fluidRow h2 h3 h4 headerPanel htmlOutput mainPanel need numericInput NS observe observeEvent p plotOutput reactiveValues renderPlot renderUI selectInput shinyApp sliderInput stopApp tabPanel tabsetPanel textOutput uiOutput updateSelectInput validate wellPanel withProgress conditionalPanel reactive outputOptions tableOutput tags radioButtons downloadButton
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+#' @importFrom shinythemes shinytheme
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+#' @importFrom utils head
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+#' @importFrom geneplotter multidensity
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+#' @importFrom stats median
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+#' @importFrom graphics plot
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+#' @export
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+#'
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+
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+selectDataInput <- function(id) {
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+  # Create a namespace function using the provided id
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+  ns <- NS(id)
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+  
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+  #UI declaration
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+  column(
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+    3,
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+    h3("Select data source:"),
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+    #Select data source
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+    wellPanel(fluidRow(
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+      column(
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+        12,
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+        #Select matrix
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+        radioButtons("matrixSourceType", "Select matrix:",
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+                     c("From file" = "fileMatrix",
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+                       "From workspace" = "varMatrix"))
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+        ,
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+        #If the selected data source is a file
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+        conditionalPanel(
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+          condition = "input.matrixSourceType == 'fileMatrix'",
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+          fileInput("matrixFile", "Choose matrix file:",
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+                    accept = c(
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+                      "text/csv",
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+                      "text/comma-separated-values,text/plain",
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+                      ".csv",".ods",".xls",".xlt")
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+          )
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+        ),
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+        #If the selected data source is a workspace object
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+        conditionalPanel(
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+          condition = "input.matrixSourceType == 'varMatrix'",
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+          selectInput("matrixVar", "Choose matrix var:",
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+                      ls(envir=.GlobalEnv))
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+        ),
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+        fluidRow(column(12,
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+                        HTML("<br>"))),
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+        #Select geneset
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+        radioButtons("genesetSourceType", "Select GeneSet:",
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+                     c("From file" = "fileGeneset",
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+                       "From workspace" = "varGeneset"))
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+        ,
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+        #If the selected data source is a file
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+        conditionalPanel(
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+          condition = "input.genesetSourceType == 'fileGeneset'",
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+          fileInput("genesetFile", "Choose GeneSet file:",
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+                    accept = ".gmt")
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+        ),
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+        #If the selected data source is a workspace object
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+        conditionalPanel(
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+          condition = "input.genesetSourceType == 'varGeneset'",
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+          selectInput("genesetVar", "Choose GeneSet var:",
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+                      ls(envir=.GlobalEnv))
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+        ),
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+        HTML("<br>"),
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+        radioButtons("arg", "Control arguments:",
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+                     c("No" = "no",
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+                       "Yes" = "yes"))
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+      )
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+    ),
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+    actionButton("button", "Run"))
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+  )
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+}
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+
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+mainDataInput <- function(id) {
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+  # Create a namespace function using the provided id
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+  ns <- NS(id)
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+  
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+  #UI Definition
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+  mainPanel(width = 6,
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+            h2("Generated GSVA data:"),
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+            textOutput("information"),
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+            plotOutput("plot"),
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+            tableOutput("result"),
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+            uiOutput("download"))
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+}
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+
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+argumentsDataInput <- function(id) {
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+  # Create a namespace function using the provided id
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+  ns <- NS(id)
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+  
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+  #UI Definition
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+  column(
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+    3,
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+    conditionalPanel(
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+      condition = "input.arg == 'yes'",
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+      h3("Select arguments:"),
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+      wellPanel(fluidRow(
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+        column(
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+          12,
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+          selectInput("method", "Choose method:",
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+                      c("gsva","ssgsea","zscore","plage")),
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+          radioButtons("rnaseq", "Rnaseq:",
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+                       c("False" = FALSE,
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+                         "True" = TRUE)),
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+          radioButtons("absRanking", "abs.ranking:",
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+                       c("False" = FALSE,
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+                         "True" = TRUE)),
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+          numericInput("minSz","min.sz:",value = 1),
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+          numericInput("maxSz","max.sz (Write 0 for infinite):",value = 0),
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+          numericInput("noBootstraps","no.bootstraps:",value = 0),
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+          numericInput("bootstrapPercent","bootstrap.percent:",value = .632),
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+          numericInput("parallelSz","parallel.sz:",value = 0),
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+          selectInput("parallelType", "parallel.type:",
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+                      c("SOCK","MPI","NWS")),
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+          radioButtons("mxDiff", "mx.diff:",
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+                       c("True" = TRUE,
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+                         "False" = FALSE)),
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+          conditionalPanel(
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+            condition = "input.method == 'gsva'",
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+            numericInput("tau1","tau:",value = 1)
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+          ),
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+          conditionalPanel(
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+            condition = "input.method == 'ssgsea'",
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+            numericInput("tau2","tau:",value = 0.25)
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+          ),
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+          conditionalPanel(
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+            condition = "input.method == 'zscore' || input.method == 'plage'"
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+          ),
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+          radioButtons("kernel", "kernel:",
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+                       c("True" = TRUE,
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+                         "False" = FALSE)),
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+          radioButtons("ssgseaNorm", "ssgsea.norm:",
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+                       c("True" = TRUE,
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+                         "False" = FALSE)),
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+          radioButtons("verbose", "verbose:",
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+                       c("True" = TRUE,
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+                         "False" = FALSE))
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+        )))
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+    )
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+  )
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+}
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+
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+gsva_validation <- function(input, output, session) {
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+  success <- FALSE #Variable to control if the GSVA variables are assigned correctly
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+  if(input$matrixSourceType == "fileMatrix")
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+  {
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+    if (is.null(input$matrixFile))
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+    {
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+      paste("No matrix file selected!")
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+      success <- FALSE
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+    }
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+    else
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+    {
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+      #Matrix file selected
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+      if(input$genesetSourceType == "fileGeneset")
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+      {
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+        if (is.null(input$genesetFile))
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+        {
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+          paste("No geneSet file selected!")
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+          success <- FALSE
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+        }
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+        else
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+        {
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+          #User selects matrix file and geneSet file
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+          inFile <- input$matrixFile
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+          newY <- as.matrix(read.csv(inFile$datapath, header = TRUE, sep = ",")) #Reading file as matrix
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+          rownames(newY) <- newY[,1] #Taking the first column as rownames
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+          newY <- newY[,-1] #Deleting the first column
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+          inGenesetFile <- input$genesetFile
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+          genes <- getGmt(inGenesetFile$datapath)
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+          if(input$maxSz == 0) {
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+            varMaxsz <- Inf
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+          }else {
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+            varMaxsz <- input$maxSz
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+          }
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+          success <- TRUE
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+        }
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+      }
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+      else
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+      {
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+        #User selects matrix file and geneset var
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+        inFile <- input$matrixFile
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+        newY <- as.matrix(read.csv(inFile$datapath, header = TRUE, sep = ",")) #Reading file as matrix
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+        rownames(newY) <- newY[,1] #Taking the first column as rownames
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+        newY <- newY[,-1] #Deleting the first column
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+        assign("genes",get(input$genesetVar))
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+        if(input$maxSz == 0) {
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+          varMaxsz <- Inf
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+        }else {
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+          varMaxsz <- input$maxSz
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+        }
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+        success <- TRUE
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+      }
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+    }
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+  }
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+  else
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+  {
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+    #User selects matrix varand geneset file
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+    if(input$genesetSourceType == "fileGeneset")
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+    {
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+      if (is.null(input$genesetFile))
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+      {
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+        paste("No geneSet file selected!")
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+        success <- FALSE
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+      }
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+      else
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+      {
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+        assign("newY",get(input$matrixVar))
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+        inGenesetFile <- input$genesetFile
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+        genes <- getGmt(inGenesetFile$datapath)
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+        if(input$maxSz == 0) {
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+          varMaxsz <- Inf
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+        }else {
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+          varMaxsz <- input$maxSz
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+        }
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+        success <- TRUE
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+      }
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+    }
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+    else
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+    {
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+      #User selects matrix var selected and geneset var
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+      assign("newY",get(input$matrixVar))
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+      assign("genes",get(input$genesetVar))
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+      if(input$maxSz == 0) {
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+        varMaxsz <- Inf
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+      }else {
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+        varMaxsz <- input$maxSz
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+      }
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+      success <- TRUE
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+    }
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+  }
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+  if(success==TRUE)
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+  {
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+    gsva_generation(input, output, session, newY, genes,varMaxsz)
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+    gsva_information(input,output,session)
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+  }
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+}
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+
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+gsva_generation <- function(input, output, session, newY, genes,varMaxsz) {
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+  x <- input$method
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+  selectedTau <- NULL
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+  switch (x,
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+          "gsva" = {
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+            selectedTau <- input$tau1
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+          },
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+          "ssgsea" = {
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+            selectedTau <- input$tau2
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+          },
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+          "zscore" = {
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+            selectedTau <- NULL
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+          },
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+          "plage" = {
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+            selectedTau <- NULL
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+          }
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+  )
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+  #GSVA Generation
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+  withProgress(message = 'Runing GSVA', value = 0, {
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+    incProgress(1, detail = "This may take a while...")
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+    generated_gsva <<- gsva(newY, genes, method = input$method, rnaseq = as.logical(input$rnaseq), abs.ranking = as.logical(input$absRanking),
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+                            min.sz = input$minSz, max.sz = varMaxsz, no.bootstraps = input$noBootstraps, bootstrap.percent = input$bootstrapPercent, 
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+                            parallel.sz = input$parallelSz, parallel.type = input$parallelType, mx.diff = as.logical(input$mxDiff), tau = selectedTau, kernel = as.logical(input$kernel),
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+                            ssgsea.norm = as.logical(input$ssgseaNorm), verbose = as.logical(input$verbose)) #Result asignation
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+  })
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+}
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+
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+gsva_information <- function(input, output, session) {
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+  if(class(generated_gsva) == "matrix")
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+  {
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+    resultInformation <- matrix(data = c(input$matrixVar,input$genesetVar,ncol(generated_gsva),nrow(generated_gsva)), nrow = 1, ncol = 4)
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+    colnames(resultInformation) <- c("Matrix used","GeneSet used", "Col num", "Row num")
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+    output$result <- renderTable(resultInformation)
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+    output$plot <- renderPlot(multidensity(as.list(as.data.frame(generated_gsva)), legend=NA, las=1, xlab=sprintf("%s scores", input$method), main="", lwd=2))
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+    tagList(
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+      downloadButton('downloadData', 'Download'),
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+      actionButton('closeSave','Save & Close')
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+    )
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+  }
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+  else
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+  {
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+
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+    resultInformation <- matrix(data = c(input$matrixVar,input$genesetVar,ncol(generated_gsva$es.obs),nrow(generated_gsva$es.obs)), nrow = 1, ncol = 4)
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+    colnames(resultInformation) <- c("Matrix used","GeneSet used", "Col num", "Row num")
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+    output$result <- renderTable(resultInformation)
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+    if(class(generated_gsva$es.obs) == "ExpressionSet") #If the generated gsva is an ExpressionSet
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+    {
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+      expressionSetObs <- exprs(generated_gsva$es.obs)
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+      output$plot <- renderPlot(multidensity(as.list(as.data.frame(expressionSetObs)), legend=NA, las=1, xlab=sprintf("%s scores", input$method), main="", lwd=2)) 
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+    }
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+    else
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+    {
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+      output$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))
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+    }
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+    tagList(
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+      downloadButton('downloadData', 'Download'),
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+      actionButton('closeSave','Save & Close')
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+    )
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+  }
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+}
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+
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+download_handler <- function(input, output, session) {
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+  #Controls the Download button
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+  output$downloadData <- downloadHandler(
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+    filename = function() {
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+      paste("gsva_es-", Sys.Date(), ".csv", sep="")
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+    },
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+    content = function(file) {
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+      if(class(generated_gsva) == "matrix") #If the whole object is a matrix
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+      {
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+        dataFrameObs <- as.data.frame(generated_gsva)
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+        write.csv(dataFrameObs, file)
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+      }
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+      else
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+      {
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+        if(class(generated_gsva$es.obs) == "ExpressionSet") #If the generated gsva es.obs is an ExpressionSet
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+        {
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+          expressionSetObs <- exprs(generated_gsva$es.obs)
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+          dataFrameObs <- as.data.frame(expressionSetObs)
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+          write.csv(dataFrameObs, file)
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+        }
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+        else
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+        {
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+          dataFrameObs <- as.data.frame(generated_gsva$es.obs)
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+          write.csv(dataFrameObs, file)
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+        } 
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+      }
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+    }
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+  )
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+}
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+
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+igsva <- function() {
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+  app <- list(ui = NULL, server = NULL)
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+  app$ui <- fluidPage(theme = shinytheme("simplex"),
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+                      fluidRow(
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+                        selectDataInput("dataInput"),
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+                        mainDataInput("mainInput")
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+                        ,
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+                        fluidRow(
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+                          argumentsDataInput("argumentsInput")
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+                        )
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+                      )
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+  )
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+  
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+  app$server <- function(input, output, session) {
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+    v <- reactiveValues(action = FALSE)
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+    
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+    observeEvent(input$button, {
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+      v$action <- input$button
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+    })
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+    
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+    output$download <- renderUI({
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+      if(v$action)
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+      {
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+        #Isolates the Run event, that allows the program to run the generation only if the user clicks the button.
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+        isolate({
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+          gsva_validation(input,output,session)
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+        })
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+      }
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+    })
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+    download_handler(input,output,session)
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+    
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+    #Observe the Save & Close button
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+    observeEvent(input$closeSave, {
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+      stopApp(generated_gsva) #Stops the app and returns the generated_gsva object
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+    })
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+  }
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+  runApp(app)
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+}
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new file mode 100644
... ...
@@ -0,0 +1,52 @@
1
+\name{igsva}
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+\alias{igsva}
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+
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+\encoding{latin1}
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+
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+\title{
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+Gene Set Variation Analysis
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+}
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+\description{
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+Starts an interactive GSVA shiny web app.
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+}
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+\usage{
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+igsva()
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+}
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+\details{
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+GSVA assesses the relative enrichment of gene sets across samples using
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+a non-parametric approach.  Conceptually, GSVA transforms a p-gene by n-sample
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+gene expression matrix into a g-geneset by n-sample pathway enrichment matrix.
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+This facilitates many forms of statistical analysis in the 'space' of pathways
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+rather than genes, providing a higher level of interpretability.
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+
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+The \code{igsva()} function starts an interactive shiny web app that allows the
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+user to configure the arguments of the \code{\link{gsva}()} function and runs it
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+on the computer. Please see the manual page of the \code{\link{gsva}()} function
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+for a description of the arguments and their default and alternative values.
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+
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+The input data may be loaded from the users workspace or by selecting a CSV file
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+for the expression data, and a GMT file for the gene sets data.
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+}
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+\value{
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+A gene-set by sample matrix of GSVA enrichment scores after
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+pressing the button 'Save & Close'. This result can be also downloaded as a
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+CSV file with the 'Download' button.
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+}
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+\references{
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+\enc{H´┐Żnzelmann}{Hanzelmann}, S., Castelo, R. and Guinney, J.
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+GSVA: Gene set variation analysis for microarray and RNA-Seq data.
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+\emph{BMC Bioinformatics}, 14:7, 2013.
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+}
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+\author{J. \enc{Fern´┐Żndez}{Fernandez} and R. Castelo}
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+\seealso{
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+  \code{\link{gsva}}
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+}
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+\examples{
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+
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+\dontrun{
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+res <- igsva() ## this will open your browser with the GSVA shiny web app
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+}
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+
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+}
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+\keyword{GSVA}
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+\keyword{shiny}