Added (not completely): Bayesian clustering

global.R

 source('modules/auxfunc.R')
 source('modules/downPlot.R')
 source('modules/downCellIDsCls.R')
-source('modules/tabScatter.R')
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+source('modules/tabScatter.R')
+source('modules/tabClBay.R')
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modules/auxfunc.R

@@ -145,14 +145,23 @@ userDataGen <- function() {
   cat(file=stderr(), 'userDataGen: in\n')
   
   locNtp = 40
-  locNtracks = 5
+  locNtracks = 100
   locNsites = 4
-  locNwells = 2
+  locNwells = 1
+  
+  x.rand.1 = c(rnorm(locNtp * locNtracks * locNsites * 0.5, 2, 0.5), rnorm(locNtp * locNtracks * locNsites * 0.5, 2, 0.5))
+  x.rand.2 = c(rnorm(locNtp * locNtracks * locNsites * 0.5, 0, 0.1), rnorm(locNtp * locNtracks * locNsites * 0.5, 0, 0.1))
+  x.rand.3 = rep(rnorm(locNtracks, 2, 0.5), 1, each = locNtp)
+  x.rand.4 = rep(rnorm(locNtracks, 1, 0.1), 1, each = locNtp)
+  
+  x.arg = rep(seq(0, locNtp-1) / locNtp * 4 * pi, locNtracks * locNsites)
   
   dt.nuc = data.table(Metadata_Site = rep(1:locNsites, each = locNtp * locNtracks),
                       Metadata_Well = rep(1:locNwells, each = locNtp * locNsites * locNtracks / locNwells),
-                      Metadata_RealTime = rep(1:locNtp, locNsites* locNtracks),
-                      objCyto_Intensity_MeanIntensity_imErkCor = c(rnorm(locNtp * locNtracks * locNsites * 0.5, .5, 0.1), rnorm(locNtp * locNtracks * locNsites * 0.5, 1, 0.2)),
+                      Metadata_RealTime = x.arg,
+#                      objCyto_Intensity_MeanIntensity_imErkCor = c(rnorm(locNtp * locNtracks * locNsites * 0.5, .5, 0.1), rnorm(locNtp * locNtracks * locNsites * 0.5, 1, 0.2)),
+#                      objNuc_Intensity_MeanIntensity_imErkCor  = c(rnorm(locNtp * locNtracks * locNsites * 0.5, .25, 0.1), rnorm(locNtp * locNtracks * locNsites * 0.5, .5, 0.2)),
+                      objCyto_Intensity_MeanIntensity_imErkCor = x.rand.3 + ifelse(x.arg < 4, 0, 1) / x.rand.3,
                       objNuc_Intensity_MeanIntensity_imErkCor  = c(rnorm(locNtp * locNtracks * locNsites * 0.5, .25, 0.1), rnorm(locNtp * locNtracks * locNsites * 0.5, .5, 0.2)),
                       TrackLabel = rep(1:(locNtracks*locNsites), each = locNtp))
   

modules/tabScatter.R

-# RShiny module for performing hierarchical clustering
+# RShiny module for the scatter plot
 # Use:
 # in ui.R
 # tabPanel(
@@ -28,7 +28,7 @@ tabScatterPlotUI <- function(id, label = "Comparing t-points") {
         4,
         uiOutput(ns('uiSelTptX')),
         uiOutput(ns('uiSelTptY')),
-        checkboxInput(ns('chBfoldChange'), 'Y-axis displays fold change between the two t-points'),
+        checkboxInput(ns('chBfoldChange'), 'Y-axis displays difference between two t-points'),
         numericInput(ns('inNeighTpts'), '#t-pts left & right', value = 0, step = 1, min = 0),
         radioButtons(ns('rBstats'), 'Operation:', list('Mean' = 1, 'Min' = 2, 'Max' = 3))
       ),
@@ -161,7 +161,7 @@ data4scatterPlot <- reactive({
   setnames(loc.dt, c('group.x', 'y.aggr.x', 'y.aggr.y'), c('group', 'x', 'y'))
 
   if (input$chBfoldChange) {
-    loc.dt[ , y := y / x]
+    loc.dt[ , y := y - x]
   }
   return(loc.dt)
   
@@ -233,6 +233,7 @@ output$outPlotScatterInt <- renderPlotly({
   # When running on a server. Based on:
   # https://github.com/ropensci/plotly/issues/494
   
+  locBut = input$butGoScatter
   if (locBut == 0) {
     cat(file=stderr(), 'plotScatterInt Go button not pressed\n')
     return(NULL)

server.R

@@ -1459,4 +1459,7 @@ shinyServer(function(input, output, session) {
       labCol = loc.colnames
     )
   })
+
+  callModule(clustBay, 'TabClustBay', data4clust)
+  
 })

ui.R

@@ -475,7 +475,12 @@ shinyUI(fluidPage(
                    actionButton('butPlotHierSparClDist', 'Plot!'),
                    plotOutput('outPlotHierSparClDist'))
         )
-      )
+      ),
+      
+      tabPanel(
+        'Bayesian Cl.',
+        clustBayUI('TabClustBay'))
+      
     ))
   )
 ))
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