Debug relative abundance and add KL and JSD distance

LoadPackages.R

@@ -149,6 +149,10 @@ if (!require(phytools)) {
   library(phytools)
 }
 
+if(!require(philentropy)){
+  devtools::install_github("HajkD/philentropy", build_vignettes = TRUE, dependencies = TRUE)
+  library(philentropy)
+}
 
 
 # if(!require(plotly)){

Rfunctions/DiagPlot.R

@@ -411,9 +411,19 @@ PCoAPlot_meta <-function (input, dds, group_init, CT,tree,col = c("SpringGreen",
     counts.norm = counts.norm[,ind_kept]
     # print(head(counts.norm))
     ## Get the distance
-    if(input$DistClust!="sere" && input$DistClust!="Unifrac") dist.counts.norm = vegdist(t(counts.norm), method = input$DistClust)
     if(input$DistClust=="sere") dist.counts.norm = as.dist(SEREcoef(counts.norm))
-    if(input$DistClust=="Unifrac")
+    else if(input$DistClust=="jsd"){
+      #dist_jsd = JSD(sweep(counts.norm,2,colSums(counts.norm)))
+      dist_jsd = JSD(t(sweep(counts.norm,2,colSums(counts.norm),`/`)))
+      dist_jsd[is.na(dist_jsd)]=0
+      dist.counts.norm = as.dist(dist_jsd)
+    }
+    else if(input$DistClust=="kl"){
+      dist_kl = KL(t(sweep(counts.norm,2,colSums(counts.norm),`/`)))
+      dist_kl[is.na(dist_kl)]=0
+      dist.counts.norm = as.dist(dist_kl)
+    }
+    else if(input$DistClust=="Unifrac")
     {
       tmp = UniFracDist(CT,tree)
       if(is.null(tree) || is.null(tmp)) dist.counts.norm = NULL
@@ -427,7 +437,10 @@ PCoAPlot_meta <-function (input, dds, group_init, CT,tree,col = c("SpringGreen",
       }
     
     }
-   
+    else{
+      dist.counts.norm = vegdist(t(counts.norm), method = input$DistClust)
+    }
+    
     if(!is.null(dist.counts.norm))
     {
       ## Do PCoA
@@ -672,9 +685,18 @@ Get_pcoa_table <-function (input, dds, group_init,CT,tree)
     counts.norm = counts.norm[,ind_kept]
     
     ## Get the distance
-    if(input$DistClust!="sere" && input$DistClust!="Unifrac") dist.counts.norm = vegdist(t(counts.norm), method = input$DistClust)
     if(input$DistClust=="sere") dist.counts.norm = as.dist(SEREcoef(counts.norm))
-    if(input$DistClust=="Unifrac") {
+    else if(input$DistClust=="jsd"){
+      dist_jsd = JSD(t(sweep(counts.norm,2,colSums(counts.norm),`/`)))
+      dist_jsd[is.na(dist_jsd)]=0.0
+      dist.counts.norm = as.dist(dist_jsd)
+    }
+    else if(input$DistClust=="kl"){
+      dist_kl = KL(t(sweep(counts.norm,2,colSums(counts.norm),`/`)))
+      dist_kl[is.na(dist_kl)]=0.0
+      dist.counts.norm = as.dist(dist_kl)
+    }
+    else if(input$DistClust=="Unifrac") {
       tmp = UniFracDist(CT,tree)
       if(is.null(tree) || is.null(tmp)) dist.counts.norm = NULL
       if(!is.null(tree)) {dist.counts.norm = switch(input$DistClustUnifrac,
@@ -684,6 +706,9 @@ Get_pcoa_table <-function (input, dds, group_init,CT,tree)
       )
       }
     }
+    else {
+      dist.counts.norm = vegdist(t(counts.norm), method = input$DistClust)
+    }
     
     if(!is.null(dist.counts.norm)){ 
       ## To get always the same result 

Rfunctions/VisuPlot.R

@@ -571,6 +571,7 @@ GetDataToPlot <- function(input,resDiff,VarInt,ind_taxo,aggregate=TRUE,rarefy=FA
       counts_tmp = counts_tmp[,colnames(counts_tmp)%in%rownames(targetInt)]
       
       ## Proportions over all the taxonomies
+      ## Proportion verified
       prop_all = t(counts)/rowSums(t(counts))
       prop_all = as.data.frame(prop_all[,Taxonomy%in%ind_taxo])
       prop_all = as.matrix(prop_all[rownames(prop_all)%in%rownames(targetInt),])
@@ -586,6 +587,7 @@ GetDataToPlot <- function(input,resDiff,VarInt,ind_taxo,aggregate=TRUE,rarefy=FA
       }
       if(!aggregate && nrow(counts_tmp)>0 && nrow(targetInt)>0)
       {  
+        ## Proportion verified
         counts_tmp_combined = t(counts_tmp)
         prop_tmp_combined = counts_tmp_combined/colSums(counts_tmp)
         rownames(counts_tmp_combined) = targetInt$AllVar

server.R

@@ -756,7 +756,7 @@ shinyServer(function(input, output,session) {
   ## Export in .csv
   output$ExportRelative <- downloadHandler(
     filename = function() { 'RelativeAb.csv' },
-    content = function(file){write.csv(dataMergeCounts()$counts/colSums(dataMergeCounts()$counts), file)}
+    content = function(file){write.csv(sweep(dataMergeCounts()$counts,2,colSums(dataMergeCounts()$counts),`/`), file)}
   )
   
   ## Export size factors
@@ -1551,16 +1551,16 @@ shinyServer(function(input, output,session) {
     ErrorTree = dataInputTree()$Error
     TaxoSelect = input$TaxoSelect
     
-    res = selectInput("DistClust","Distance",c("euclidean", "SERE"="sere", "canberra", "bray", "kulczynski", "jaccard", 
-                                         "gower", "altGower", "morisita", "horn","mountford","raup","binomial",
-                                         "chao","cao","mahalanobis"),selected="canberra")
+    res = selectInput("DistClust","Distance", c("altGower", "binomial", "bray", "canberra", "cao", "chao", "euclidean","gower", "horn",
+                                                "jaccard","jsd","kl", "kulczynski",  "mahalanobis", "morisita", "mountford","raup",
+                                                "SERE"="sere"),selected="bray")
     
     ## Add the unifrac distance
     if(!is.null(tree) && !is.null(input$fileTree) && is.null(ErrorTree)  && TaxoSelect == "OTU/Gene")
     {
-      res = selectInput("DistClust","Distance",c("euclidean", "SERE"="sere", "canberra", "bray", "kulczynski", "jaccard", 
-                                                 "gower", "altGower", "morisita", "horn","mountford","raup","binomial",
-                                                 "chao","cao","mahalanobis","Unifrac"),selected="canberra")
+      res = selectInput("DistClust","Distance",c("altGower", "binomial", "bray", "canberra", "cao", "chao", "euclidean","gower", "horn",
+                                                 "jaccard","jsd","kl", "kulczynski",  "mahalanobis", "morisita", "mountford","raup",
+                                                 "SERE"="sere","Unifrac"),selected="bray")
     }
     
     return(res)