Couleur barplot pca/pcoa + correction bug correlation

Rfunctions/DiagPlot.R

@@ -402,7 +402,11 @@ PCoAPlot_meta <-function (input, dds, group_init, col = c("SpringGreen","dodgerb
         s.class(dfxy = pco.counts.norm$li, fac = group, col = col, label = levels(group), add.plot = TRUE, cpoint = 0, clabel = 0, cstar = input$cexstar, cell=input$cexcircle)
       }
     }else{
-      barplot(eigen[1:7], xlab="Dimensions", ylab="Eigenvalues (%)", names.arg = 1:7, col = c(rep("black", 2), rep("grey", 5)), ylim=c(0,max(eigen)+5), cex.axis=1.2, cex.lab=1.4,cex.names=1.2)
+      v_axes = c(as.numeric(gsub("PC","",input$PCaxe1)),as.numeric(gsub("PC","",input$PCaxe2)))
+      nbBar = max(7,max(v_axes))
+      col = rep("grey",nbBar)
+      col[v_axes] = "black"
+      barplot(eigen[1:nbBar], xlab="Dimensions", ylab="Eigenvalues (%)", names.arg = 1:nbBar, col = col, ylim=c(0,max(eigen)+5), cex.axis=1.2, cex.lab=1.4,cex.names=1.2)
     }
   }
   
@@ -474,7 +478,12 @@ PCAPlot_meta <-function(input,dds, group_init, n = min(500, nrow(counts(dds))),
       text(pca$x[, as.numeric(gsub("PC","",input$PCaxe1))] - ifelse(pca$x[, as.numeric(gsub("PC","",input$PCaxe1))] > 0, abs, -abs), pca$x[,as.numeric(gsub("PC","",input$PCaxe2))] - ifelse(pca$x[,as.numeric(gsub("PC","",input$PCaxe2))] > 0, ord, -ord), colnames(counts.trans), col = col[indgrp],cex=input$cexLabelDiag)
       
     }
-    if(plot=="eigen"){eigen = pca$sdev[1:min(7,ncol(counts.trans))]^2; barplot(eigen, xlab="Dimensions", ylab="Eigenvalues (%)", names.arg = 1:min(7,ncol(counts.trans)), col = c(rep("black", 3), rep("grey", 4)), ylim=c(0,max(eigen)+5), cex.axis=1.2, cex.lab=1.4,cex.names=1.2)}
+    if(plot=="eigen"){
+      nbBar = max(7,max(c(as.numeric(gsub("PC","",input$PCaxe1)),as.numeric(gsub("PC","",input$PCaxe2)))))
+      col = rep("grey",nbBar)
+      eigen = pca$sdev[1:nbBar]^2
+      col[c(as.numeric(gsub("PC","",input$PCaxe1)),as.numeric(gsub("PC","",input$PCaxe2)))] = "black"
+      barplot(eigen, xlab="Dimensions", ylab="Eigenvalues (%)", names.arg = 1:nbBar, col = col, ylim=c(0,max(eigen)+5), cex.axis=1.2, cex.lab=1.4,cex.names=1.2)}
     
   }
 }

Rfunctions/VisuPlot.R

@@ -292,7 +292,7 @@ Plot_Visu_Scatterplot<- function(input,resDiff,export=FALSE,lmEst = FALSE,CorEst
     if(any(numInd)) data = cbind(target[,numInd],log2(t(counts)+1),div)
     if(!any(numInd)) data = cbind(log2(t(counts)+1),div)
     
-    cor.est = cor(as.matrix(data),method = input$CorMeth,na.rm=TRUE)
+    cor.est = round(cor(as.matrix(data),method = input$CorMeth),3)
     #cor.pvalue = cor.test(data,method = input$CorMeth)
     return(list(cor.est=cor.est))
   }

server.R

@@ -24,7 +24,7 @@ shinyServer(function(input, output,session) {
 
   ## Counts file
   dataInputCounts <-reactive({ 
-    print(input$TypeTaxo)
+    
     inFile <- input$fileCounts
     
     if (is.null(inFile)) return(NULL)

ui.R

@@ -346,7 +346,7 @@ body <- dashboardBody(
                     conditionalPanel(condition="input.DiagPlot=='pcoaPlot' || input.DiagPlot=='SERE' || input.DiagPlot=='clustPlot' ",
                                       selectInput("DistClust","Distance",c("euclidean", "SERE"="sere", "canberra", "bray", "kulczynski", "jaccard", 
                                                   "gower", "altGower", "morisita", "horn","mountford","raup","binomial",
-                                                  "chao","cao","mahalanobis"),selected="jaccard")
+                                                  "chao","cao","mahalanobis"),selected="canberra")
                                     )