added alignment via decipher

Dashboard.Rmd

@@ -1,89 +1,91 @@
----
-title: "SARS-CoV-2 Non-human Shifts "
-output: flexdashboard::flex_dashboard
-runtime: shiny
----
-
-```{r setup, include=FALSE}
-library(shiny)
-library(tidyverse)
-library(flexdashboard)
-library(ape)
-library(phangorn)
-library(phylocanvas)
-```
-
-```{r global, include=FALSE}
-KnownSeqs <- read.dna("all_aligned.fasta", format = "fasta")
-KnownSeqsMeta <- read.table("metadata.csv", header = T, sep = ',')
-```
-
-Column {.sidebar}
------------------------------------------------------------------------
-
-Upload your data here.
-
-```{r}
-fileInput("sample", label = "Sequence data:", buttonLabel = "Browse...", placeholder = "No file selected")
-
-selectInput("species", label = "Select organism:",
-            choices = c("All", "canis_lupus", "felis_catus", "human", "mink",
-                        "panthera_leo", "panthera_tigris_jacksoni"), selected = "All")
-
-selectInput("location", label = "Select location:",
-            choices = c("All", "Netherlands", "Poland", "sars_cov2_wuhan_hu1_root", "Wisconson-USA", "Unknown"),
-            selected = "All")
-
-```
-
-Column
------------------------------------------------------------------------
-
-### SARS-CoV-2 Tree
-
-```{r}
-# Defining dataset to use
-renderTable({
-#renderPlot({
-  if(input$species == "All" & input$location == "All"){
-    DesiredSeqs <- KnownSeqs
-    DesiredIds <- names(KnownSeqs)
-  }else if(input$species == "All" & input$location != "All"){
-    DesiredIds <- KnownSeqsMeta %>% filter(location == input$location) %>% select(seqid)
-    DesiredIndex <- which(names(KnownSeqs) %in% DesiredIds)
-    DesiredSeqs <- KnownSeqs[DesiredIndex]
-  }else if(input$species != "All" & input$location == "All") {
-    DesiredIds <- KnownSeqsMeta %>% filter(species == input$species) %>% select(seqid)
-    DesiredIndex <- which(names(KnownSeqs) %in% DesiredIds)
-    DesiredSeqs <- KnownSeqs[DesiredIndex]
-  }else{
-    DesiredIds <- KnownSeqsMeta %>% filter(species == input$species, location == input$species) %>% select(seqid)
-    DesiredIndex <- which(names(KnownSeqs) %in% DesiredIds)
-    DesiredSeqs <- KnownSeqs[DesiredIndex]
-  }
-  #DesiredIds
-
-# Adding sample data
-  file <- input$sample
-  SampleSeq <- read.dna(file$datapath, format = "fasta")
-  AllSeqs <- cbind(as.matrix(DesiredSeqs), as.matrix(SampleSeq), fill.with.gaps = T)
-  names(AllSeqs)
-#})
-#tree construction, uncomment when ready
-#also, may be slow? not sure at 30 seqs X 30kb
-#from the phangorn tutorial
-#https://cran.r-project.org/web/packages/phangorn/vignettes/Trees.pdf
-#initial tree
-  #msa <- phyDat(KnownSeqs, type = "DNA")
-  #dm  <- dist.ml(msa)
-  #treeNJ <- NJ(dm)
-#ML tuning
-#fit <- pml(treeNJ, data=aln)
-#can also try ratchet/stochastic for rearrangement, but slower (better topology)
-#fitGTR <- optim.plm(fit, model="GTR",optInv=TRUE,optGamma=TRUE,
-#                    rearrangement="NNI",control=pml.control(trace=0))
-#todo, figure out how to extract the tree
-
-  #phylocanvas(treeNJ, treetype = "rectangular", alignlabels = T)
-})
-```
+---
+title: "SARS-CoV-2 Non-human Shifts "
+output: flexdashboard::flex_dashboard
+runtime: shiny
+---
+
+```{r setup, include=FALSE}
+library(shiny)
+library(tidyverse)
+library(flexdashboard)
+library(ape)
+library(phangorn)
+library(phylocanvas)
+library(DECIPHER)
+library(tictoc)
+library(dplyr)
+```
+
+```{r global, include=FALSE}
+KnownSeqs <- read.dna("all_aligned.fasta", format = "fasta")
+KnownSeqsMeta <- read.table("metadata.csv", header = T, sep = ',')
+```
+
+Column {.sidebar}
+-----------------------------------------------------------------------
+
+Upload your data here.
+
+```{r}
+fileInput("sample", label = "Sequence data:", buttonLabel = "Browse...", placeholder = "No file selected")
+selectInput("species", label = "Select organism:",
+            choices = c("All", "canis_lupus", "felis_catus", "human", "mink",
+                        "panthera_leo", "panthera_tigris_jacksoni"), selected = "All")
+selectInput("location", label = "Select location:",
+            choices = c("All", "Netherlands", "Poland", "sars_cov2_wuhan_hu1_root", "Wisconson-USA", "Unknown"),
+            selected = "All")
+```
+
+Column
+-----------------------------------------------------------------------
+
+### SARS-CoV-2 Tree
+
+```{r}
+# Defining dataset to use
+renderTable({
+#renderPlot({
+  if(input$species == "All" & input$location == "All"){
+    DesiredSeqs <- KnownSeqs
+    DesiredIds <- names(KnownSeqs)
+  }else if(input$species == "All" & input$location != "All"){
+    DesiredIds <- KnownSeqsMeta %>% filter(location == input$location) %>% select(seqid)
+    DesiredIndex <- which(names(KnownSeqs) %in% DesiredIds)
+    DesiredSeqs <- KnownSeqs[DesiredIndex]
+  }else if(input$species != "All" & input$location == "All") {
+    DesiredIds <- KnownSeqsMeta %>% filter(species == input$species) %>% select(seqid)
+    DesiredIndex <- which(names(KnownSeqs) %in% DesiredIds)
+    DesiredSeqs <- KnownSeqs[DesiredIndex]
+  }else{
+    DesiredIds <- KnownSeqsMeta %>% filter(species == input$species, location == input$species) %>% select(seqid)
+    DesiredIndex <- which(names(KnownSeqs) %in% DesiredIds)
+    DesiredSeqs <- KnownSeqs[DesiredIndex]
+  }
+  #DesiredIds
+# Adding sample data
+  file <- input$sample
+  SampleSeq <- readDNAStringSet(file$datapath,format='fasta')
+  DesiredSeqsConv=DesiredSeqs %>% as.list %>% as.character %>% lapply(.,paste0,collapse="") %>% unlist %>% DNAStringSet
+  #AllSeqs <- cbind(as.matrix(DesiredSeqs), as.matrix(SampleSeq), fill.with.gaps = T)
+  aligned=AlignProfiles(DesiredSeqsConv,SampleSeq)
+  #names(AllSeqs)
+#})
+#tree construction, uncomment when ready
+#also, may be slow? not sure at 30 seqs X 30kb
+#from the phangorn tutorial
+#https://cran.r-project.org/web/packages/phangorn/vignettes/Trees.pdf
+#initial tree
+  tic()
+  msa <- phyDat(KnownSeqs, type = "DNA")
+  dm  <- dist.ml(msa)
+  treeNJ <- NJ(dm)
+  ttime=toc()
+#ML tuning
+#fit <- pml(treeNJ, data=aln)
+#can also try ratchet/stochastic for rearrangement, but slower (better topology)
+#fitGTR <- optim.plm(fit, model="GTR",optInv=TRUE,optGamma=TRUE,
+#                    rearrangement="NNI",control=pml.control(trace=0))
+#todo, figure out how to extract the tree
+  #phylocanvas(treeNJ, treetype = "rectangular", alignlabels = T)
+})
+```