Add more distance

Rfunctions/DiagPlot.R

@@ -412,35 +412,26 @@ PCoAPlot_meta <-function (input, dds, group_init, CT,tree,col = c("SpringGreen",
     # print(head(counts.norm))
     ## Get the distance
     if(input$DistClust=="sere") dist.counts.norm = as.dist(SEREcoef(counts.norm))
-    else if(input$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")
-    {
+    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,
-                                                    "WU" = as.dist(tmp[, , "d_1"]), 
-                                                    "UWU" = as.dist(tmp[, , "d_UW"]),
-                                                    "VAWU" = as.dist(tmp[, , "d_VAW"])
-                                )
+                                  "WU" = as.dist(tmp[, , "d_1"]), 
+                                  "UWU" = as.dist(tmp[, , "d_UW"]),
+                                  "VAWU" = as.dist(tmp[, , "d_VAW"])
+        )
       }
-    
+      
     }
-    else{
-      dist.counts.norm = vegdist(t(counts.norm), method = input$DistClust)
+    else if(input$DistClust  %in% getDistMethods()){
+      dist = as.dist(distance(t(sweep(counts.norm,2,colSums(counts.norm),`/`)), method=input$DistClust))
+      dist[is.na(dist)]=0.0
+      dist.counts.norm = dist
     }
-    
+    else  dist.counts.norm = vegdist(t(counts.norm), method = input$DistClust)
+    #"additive_symm"
     if(!is.null(dist.counts.norm))
     {
       ## Do PCoA
@@ -486,8 +477,8 @@ PCoAPlot_meta <-function (input, dds, group_init, CT,tree,col = c("SpringGreen",
         title(main='Principal Coordinates Analysis ',cex.main=1.5)
         ## Add a subtitle
         par(font.main=3)
-        if(input$DistClust!="Unifrac") title(main=paste("\n","\n",input$DistClust,"distance",sep=" "),cex.main=1)
         if(input$DistClust=="Unifrac") title(main=paste("\n","\n",input$DistClustUnifrac,"distance",sep=" "),cex.main=1)
+        else title(main=paste("\n","\n",input$DistClust,"distance",sep=" "),cex.main=1)
         
         # Set different shapes
         if(input$labelPCOA == "Group"){
@@ -649,7 +640,7 @@ Get_pcoa_table <-function (input, dds, group_init,CT,tree)
   time_set = 0
   # Set of shape
   shape=c(19,17,15,18)
-  
+
   ## Var of interest
   VarInt  = input$VarInt
   
@@ -686,16 +677,6 @@ Get_pcoa_table <-function (input, dds, group_init,CT,tree)
     
     ## Get the distance
     if(input$DistClust=="sere") dist.counts.norm = as.dist(SEREcoef(counts.norm))
-    else if(input$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
@@ -706,6 +687,11 @@ Get_pcoa_table <-function (input, dds, group_init,CT,tree)
       )
       }
     }
+    else if(input$DistClust  %in% getDistMethods()){
+      dist = as.dist(distance(t(sweep(counts.norm,2,colSums(counts.norm),`/`)), method=input$DistClust))
+      dist[is.na(dist)]=0.0
+      dist.counts.norm = dist
+    }
     else {
       dist.counts.norm = vegdist(t(counts.norm), method = input$DistClust)
     }

server.R

@@ -1551,16 +1551,19 @@ shinyServer(function(input, output,session) {
     ErrorTree = dataInputTree()$Error
     TaxoSelect = input$TaxoSelect
     
-    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")
+    dist_phyl = getDistMethods()[!getDistMethods() %in% c("additive_symm", "jensen-shannon", "jensen_difference", "minkowski", "topsoe")]
+    
+    
+    res = selectInput("DistClust","Distance", unique(sort(c("altGower", "binomial", "bray", "canberra", "cao", "chao", "euclidean","gower", "horn",
+                                                "jaccard", "kulczynski",  "mahalanobis", "morisita", "mountford","raup",
+                                                "SERE"="sere", dist_phyl))),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("altGower", "binomial", "bray", "canberra", "cao", "chao", "euclidean","gower", "horn",
-                                                 "jaccard","jsd","kl", "kulczynski",  "mahalanobis", "morisita", "mountford","raup",
-                                                 "SERE"="sere","Unifrac"),selected="bray")
+      res = selectInput("DistClust","Distance",unique(sort(c("altGower", "binomial", "bray", "canberra", "cao", "chao", "euclidean","gower", "horn",
+                                                 "jaccard","kulczynski",  "mahalanobis", "morisita", "mountford", "raup",
+                                                 "SERE"="sere","Unifrac", dist_phyl))),selected="bray")
     }
     
     return(res)