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app.R
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app.R
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library(shiny)
library(shinyBS)
library(subspace)
library(ggplot2)
library(ggfortify)
library(rCOSA)
library(kohonen)
library(rJava)
library(RANN)
library(rlist)
library(dummies)
library(ggdendro)
source("ReadingData.R")
source("utility.R")
source("smacofFixed.R")
source("doc.R")
# Allow Uploads until 200MB
options(shiny.maxRequestSize=200*1024^2)
options(java.parameters = "-Xmx16000m")
# Define UI for app ----
ui <- fluidPage(
# App title ----
titlePanel("Analysis of Metabolite data"),
tabsetPanel(id = "tabs", type = "tabs",
# Input Panel
tabPanel(title = "Data Input", value = "0",
titlePanel("Please upload your data"),
"Important things about the data:",
"- Rows are individuals, Columns are features",
fileInput(inputId = "inputData",
label = "Data:",
accept = c(".xlsx",".csv",".tsv")),
checkboxInput(inputId = "externalPheno",
label = "Upload phenotypes from another file"),
uiOutput(outputId = "phenoInputField"),
actionButton("toTypes", "Upload")
),
# Input Validation Panel
tabPanel(title = "Specifying Column Types", value = "1",
selectInput(inputId = "idField",
label = "Unique ID:",
choices = list()),
textOutput(outputId = "idError"),
selectInput(inputId = "metabStart",
label = "First Metabolite:",
choices = list()),
selectInput(inputId = "metabEnd",
label = "Last Metabolite:",
choices = list()),
actionButton("toPheno", "Continue")
),
# Phenotype validation Panel
tabPanel(title = "Phenotype factor identification", value = "2",
uiOutput("phenoWhichFactor"),
actionButton("toOut", "Cluster Now")
),
# Output Panel
tabPanel(title = "Output", value = "3",
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Slider for the number of bins ----
selectInput(inputId = "selectedPhenotype",
label = "Phenotype:",
choices = list()),
selectInput(inputId = "clusteringType",
label = "Clustering Algorithm used:",
choices = list("Nothing","Clique","SOM", "COSA","DOC")),
actionButton("resetClustering", "Reset the Clustering"),
checkboxInput("pcaSwitch", "Display the Clusters in the PCA Plot"),
uiOutput(outputId = "min"),
uiOutput(outputId = "max"),
checkboxInput(inputId = "reverse",
label = "Reverse Color Strength"),
checkboxInput(inputId = "removeOutliers",
label = "Remove the highest and lowest 5% (Only of number phenotypes)"),
textOutput(outputId = "phenoInfo"),
plotOutput(outputId = "phenoHist"),
width = 3
),
# Main panel for displaying outputs ----
mainPanel(
bsModal("cosaHist", "Histogram of the rCosa", "go", size = "large",plotOutput("hist", click = "clickHist"),actionButton("autoCluster", "Select clusters automatically")),
uiOutput("clusteringPlot"),
uiOutput("pcaAll"),
uiOutput("downloadButton"),
uiOutput("downloadClusterIds"),
htmlOutput("info"),
width = 9
)
)
)
)
)
# Define server logic required ----
server <- function(input, output, session) {
plotsSize <- 1000
# Data uploaded by the user
data <- reactive({
if (!is.null(input$inputData)){
readFile(input$inputData$datapath)
}
else{
list("Error" = "Please upload a file first!")
}
})
# Transformed/normalized data
finalValues <- reactiveValues(id = 1, metab = list(), pheno = list(), phenoFactors = logical())
# Clusters
clusteringData <- reactiveValues(Clique = list(), CliqueMDS = list(), SOM = list(), COSA = list(), DOC = list(), DocMDS = list(), PhenoPValues = list())
SOMLastPheno <<- -1
# PCAData
pcaValues <- reactiveValues()
#Temp Values to distinguish which click/brush changed
temps <- reactiveValues(clusteringClick = NULL,clusteringBrush = NULL, pcaBrush = NULL, idsFromClusterOld = NULL, index = list(),
oldk = NULL, oldx = NULL, hc = NULL, grps = c(0), id = 1, clickHist = NULL, rects = data.frame(x1=NULL,y1=NULL,x2=NULL,y2=NULL,border=NULL))
output$pca <- renderPlot({
if (length(data())==4&input$clusteringType!="Nothing"){
firstRunForClusteringMethod <- length(clusteringData[[input$clusteringType]])==0
firstPCA <- length(pcaValues$visualisation)==0
colorClusterNotPheno <- FALSE
if (!is.null(input$colorClusterNoPheno)){
colorClusterNotPheno <- input$colorClusterNoPheno[[1]]
}
metabComplete <- data.frame(scale(finalValues$metab))
if (firstRunForClusteringMethod){
switch(input$clusteringType,
SOM={
clusteringData$SOM <- som(data.matrix(metabComplete), grid = somgrid(xdim = 5,ydim = 5, topo = "hexagonal"),rlen = 100, alpha = c(0.08,0.01))
},
COSA={
clusteringData$COSA <- cosa2(metabComplete, lambda = 0.4, niter = 2, noit = 30)
clusteringData$COSA$smacof <- smacof(clusteringData$COSA$D, niter = 30, interc = 1, VERBOSE = FALSE, PLOT = FALSE)
#clusteringData$COSA <- cosa2(metabComplete, niter = 1, noit = 1)
#clusteringData$COSA$smacof <- smacof(clusteringData$COSA$D, niter = 1, interc = 1, VERBOSE = FALSE, PLOT = FALSE)
clusteringData$COSA$idsAndSmacof <- data.frame(cbind(names(finalValues$idFromNum),clusteringData$COSA$smacof$X))
names(clusteringData$COSA$idsAndSmacof) <- c("id","x","y")
toggleModal(session, "cosaHist", toggle = "toggle")
},
DOC={
clusteringData$DOC <- runDoc(metabComplete, 0.1, 0.75, calcW(metabComplete, 1.3))
docMDSValues <- plotFromClusters(getIdsDoc(clusteringData$DOC), returnMDS = TRUE)
clusteringData$DocMDS <- data.frame(cbind(seq_len(nrow(docMDSValues)),docMDSValues))
names(clusteringData$DocMDS) <- c("id","x","y")
},
{
# default is using Clique
inds <- nrow(metabComplete)
xi <- 8
if (inds>800){
xi <- xi + floor((inds-800)/200)
if (xi>14){
xi <- 14
}
}
clusteringData$Clique <- CLIQUE(metabComplete, xi = xi,tau = 0.1)
clusteringData$Clique <- clusteringData$Clique[order(unlist(lapply(lapply(clusteringData$Clique, "[[", "subspace"),"sum")),decreasing = TRUE)]
cliqueMDSValues <- plotFromClusters(lapply(clusteringData$Clique, `[[`, "objects"), returnMDS = TRUE)
clusteringData$CliqueMDS <- data.frame(cbind(seq_len(nrow(cliqueMDSValues)),cliqueMDSValues))
names(clusteringData$CliqueMDS) <- c("id","x","y")
}
)
}
origPheno <- finalValues$pheno[[as.numeric(input$selectedPhenotype)]]
phenotype <- origPheno
phenotypeNoNa <- phenotype[!is.na(phenotype)]
factor <- class(phenotype)=="character"
if (length(finalValues$phenoFactors)>=as.numeric(input$selectedPhenotype)){
factor <- factor || finalValues$phenoFactors[[as.numeric(input$selectedPhenotype)]]
}
if (factor){
origPheno <- factor(origPheno)
phenotype <- as.numeric(factor(phenotype))
phenotypeNoNa <- as.numeric(factor(phenotypeNoNa))
} else {
if (input$removeOutliers[[1]]){
n <- 5
lowestTooHigh <- min(phenotypeNoNa[phenotypeNoNa >= quantile(phenotypeNoNa,prob=1-n/100)])
highestTooLow <- max(phenotypeNoNa[phenotypeNoNa <= quantile(phenotypeNoNa,prob=n/100)])
phenotype[phenotype>lowestTooHigh] <- lowestTooHigh
phenotype[phenotype<highestTooLow] <- highestTooLow
}
}
if (length(unique(finalValues$pheno[[as.numeric(input$selectedPhenotype)]]))==1&&finalValues$pheno[[as.numeric(input$selectedPhenotype)]][[1]]==0){
coloredPoints <- rgb(0, 0, 0, maxColorValue=255, alpha=255)
output$phenoInfo <- NULL
output$phenoHist <- NULL
} else {
colorPalette <- getColors(length(unique(phenotypeNoNa)))
if (input$reverse[[1]]){
output$min <- renderUI(HTML(sprintf(paste("<div style='background-color: ",colorPalette[1],";height: 18px;width: 18px;float: left;margin-right: 3px;'></div> Min: %s",sep = ""), fourDeci(min(phenotypeNoNa)))))
output$max <- renderUI(HTML(sprintf(paste("<div style='background-color: ",colorPalette[length(colorPalette)],";height: 18px;width: 18px;float: left;margin-right: 3px;'></div> Max: %s",sep = ""), fourDeci(max(phenotypeNoNa)))))
coloredPoints <- vecToCol(phenotype,colorPalette)
} else {
colorPalette <- rev(colorPalette)
output$min <- renderUI(HTML(sprintf(paste("<div style='background-color: ",colorPalette[1],";height: 18px;width: 18px;float: left;margin-right: 3px;'></div> Min: %s",sep = ""), fourDeci(min(phenotypeNoNa)))))
output$max <- renderUI(HTML(sprintf(paste("<div style='background-color: ",colorPalette[length(colorPalette)],";height: 18px;width: 18px;float: left;margin-right: 3px;'></div> Max: %s",sep = ""), fourDeci(max(phenotypeNoNa)))))
coloredPoints <- vecToCol(phenotype,colorPalette)
}
output$phenoInfo <- renderText(sprintf("Mean of phenotypes after normalization between 0 and 1: %s", fourDeci(mean(range01(phenotypeNoNa)))))
if (class(origPheno)=="factor"){
#print(colorPalette)
output$phenoHist <- renderPlot({
hist(phenotypeNoNa,xlab="Phenotype as factors",main="Frequency of levels", col=colorPalette,breaks=seq(0,length(levels(origPheno))))
legend("topright",levels(origPheno),col=colorPalette,pch=15,pt.cex = 2)
})
} else {
output$phenoHist <- renderPlot(hist(phenotype))
}
}
clusterNumbers <- rep(16,nrow(metabComplete))
clusterColors <- rep("blue",nrow(metabComplete))
switch(input$clusteringType,
SOM={
if (as.numeric(input$selectedPhenotype)!=SOMLastPheno){
output$clusteringPlot <- renderUI(plotOutput(outputId = "clustering", click = "clustering_click", height = plotsSize/2, width = plotsSize/2))
if (as.numeric(input$selectedPhenotype)==length(finalValues$pheno)){
output$clustering <- renderPlot(plotHeatMap(clusteringData$SOM,finalValues$pheno[-length(finalValues$pheno)],0))
} else {
output$clustering <- renderPlot(plotHeatMap(clusteringData$SOM,finalValues$pheno[-length(finalValues$pheno)],as.numeric(input$selectedPhenotype)))
}
SOMLastPheno <<- as.numeric(input$selectedPhenotype)
}
#clusters <- as.character(sort(unique(clusteringData$SOM$unit.classif)))
#colorClusters <- rainbow(length(clusters))
#names(colorClusters) <- clusters
#clusterColors <- clusteringData$SOM$unit.classif
#for (num in clusters){
# clusterColors <- replace(clusterColors, clusterColors==as.numeric(num), colorClusters[[num]])
#}
if(!is.null(input$clusterId)){
if (input$clusterId!=0){
clusterNumbers[clusteringData$SOM$unit.classif==input$clusterId] <- 17
clusterColors[clusteringData$SOM$unit.classif==input$clusterId] <- "red"
currentCluster <- getCurrentClusters()[[as.numeric(input$clusterId)]]
enrichedPhenos <- getEnrichedString(currentCluster)
numberOfClusters <- length(unique(clusteringData$SOM$unit.classif))
enrichedPhenos <- sprintf("%s<br/>Bonferroni cut-off with %s clusters would be %s<br/>",enrichedPhenos,numberOfClusters,0.05/numberOfClusters)
if (pValueExists(input$clusteringType,input$selectedPhenotype)){
sumNormal <- sum(clusteringData$PhenoPValues[[input$clusteringType]][[as.numeric(input$selectedPhenotype)]]<0.05,na.rm = TRUE)
sumBonferroni <- sum(clusteringData$PhenoPValues[[input$clusteringType]][[as.numeric(input$selectedPhenotype)]]<(0.05/numberOfClusters),na.rm = TRUE)
enrichedPhenos <- sprintf("%s%s Clusters are significant (for this phenotype) with the cutoff <0.05<br/>%s Clusters are significant (for this phenotype) with the Bonferroni cutoff<br/>",enrichedPhenos,sumNormal,sumBonferroni)
}
output$metabUsed <- renderUI(HTML(sprintf("Cluster %s selected<br/>Significant Phenotypes:<br/>%s",input$clusterId,enrichedPhenos)))
finalValues$idsFromCluster <- finalValues$numFromId[clusteringData$SOM$unit.classif==input$clusterId]
} else {
output$metabUsed <- renderUI(HTML("No cluster selected"))
}
}
},
COSA={
output$clusteringPlot <- renderUI(plotOutput(outputId = "clustering", brush = "clustering_brush", height = plotsSize, width = plotsSize))
if(!is.null(input$clusterId)){
if (input$clusterId!=0&length(temps$index)>=as.numeric(input$clusterId)){
currentCluster <- temps$index[[as.numeric(input$clusterId)]]
clusterNumbers[currentCluster] <- 17
clusterColors[currentCluster] <- "red"
enrichedPhenos <- getEnrichedString(currentCluster)
numberOfClusters <- length(temps$index)
enrichedPhenos <- sprintf("%s<br/>Bonferroni cut-off with %s clusters would be %s<br/>",enrichedPhenos,numberOfClusters,0.05/numberOfClusters)
if (pValueExists(input$clusteringType,input$selectedPhenotype)){
sumNormal <- sum(clusteringData$PhenoPValues[[input$clusteringType]][[as.numeric(input$selectedPhenotype)]]<0.05,na.rm = TRUE)
sumBonferroni <- sum(clusteringData$PhenoPValues[[input$clusteringType]][[as.numeric(input$selectedPhenotype)]]<(0.05/numberOfClusters),na.rm = TRUE)
enrichedPhenos <- sprintf("%s%s Clusters are significant (for this phenotype) with the cutoff <0.05<br/>%s Clusters are significant (for this phenotype) with the Bonferroni cutoff<br/>",enrichedPhenos,sumNormal,sumBonferroni)
}
output$metabUsed <- renderUI(HTML(sprintf("Cluster %s selected<br/>Significant Phenotypes:<br/>%s",input$clusterId,enrichedPhenos)))
finalValues$idsFromCluster <- currentCluster
} else {
output$metabUsed <- renderUI(HTML("No cluster selected"))
}
}
if(colorClusterNotPheno){
coloredPoints <- clusterColors
}
output$clustering <- renderPlot(plotSmacof(clusteringData$COSA$smacof[["X"]], cols = coloredPoints, pch = clusterNumbers))
},
DOC={
output$clusteringPlot <- renderUI(plotOutput(outputId = "clustering", brush = "clustering_brush", height = plotsSize, width = plotsSize))
idsInClustersDoc <- getIdsDoc(clusteringData$DOC)
if(!is.null(input$clusterId)){#&input$pcaSwitch[[1]]){
if (input$clusterId!=0){
currentCluster <- idsInClustersDoc[[as.numeric(input$clusterId)]]
enrichedPhenos <- getEnrichedString(currentCluster)
numberOfClusters <- length(idsInClustersDoc)
enrichedPhenos <- sprintf("%s<br/>Bonferroni cut-off with %s clusters would be %s<br/>",enrichedPhenos,numberOfClusters,0.05/numberOfClusters)
if (pValueExists(input$clusteringType,input$selectedPhenotype)){
sumNormal <- sum(clusteringData$PhenoPValues[[input$clusteringType]][[as.numeric(input$selectedPhenotype)]]<0.05,na.rm = TRUE)
sumBonferroni <- sum(clusteringData$PhenoPValues[[input$clusteringType]][[as.numeric(input$selectedPhenotype)]]<(0.05/numberOfClusters),na.rm = TRUE)
enrichedPhenos <- sprintf("%s%s Clusters are significant (for this phenotype) with the cutoff <0.05<br/>%s Clusters are significant (for this phenotype) with the Bonferroni cutoff<br/>",enrichedPhenos,sumNormal,sumBonferroni)
}
usedDimensions <- getDimsDoc(clusteringData$DOC)[[as.numeric(input$clusterId)]]
avgs <- getAvgsDoc(clusteringData$DOC)[as.numeric(input$clusterId),]
metabs <- names(metabComplete)[usedDimensions]
usedMetabs <- sprintf("This cluster was calculed using the following metabolites: <br/>%s<br/><br/>Significant Phenotypes:<br/>%s", paste(metabs, collapse = '<br/>'),enrichedPhenos)
averagesFormatted <- mapply(function(x,y) paste(x, round(as.numeric(y), digits=4), sep=": "), metabs, avgs, SIMPLIFY=FALSE)
metabAvgs <- sprintf("<br/><br/>The metabolites have the following averages: <br/>%s", paste(averagesFormatted, collapse = '<br/>'))
output$metabUsed <- renderUI(HTML(paste(usedMetabs,metabAvgs)))
clusterNumbers[currentCluster] <- 17
clusterColors[currentCluster] <- "red"
finalValues$idsFromCluster <- currentCluster
} else {
output$metabUsed <- renderUI(HTML("No cluster selected"))
}
}
if(colorClusterNotPheno){
coloredPoints <- clusterColors
}
output$clustering <- renderPlot(plotFromClusters(idsInClustersDoc, label = FALSE, colors = coloredPoints, pch = clusterNumbers))
},
{
# default is using Clique
output$clusteringPlot <- renderUI(plotOutput(outputId = "clustering", brush = "clustering_brush", height = plotsSize, width = plotsSize))
if(!is.null(input$clusterId)){
if (input$clusterId!=0&input$clusterId!=""){
currentCluster <- clusteringData$Clique[as.numeric(input$clusterId)][[1]]
enrichedPhenos <- getEnrichedString(currentCluster$objects)
numberOfClusters <- length(clusteringData$Clique)
enrichedPhenos <- sprintf("%s<br/>Bonferroni cut-off with %s clusters would be %s<br/>",enrichedPhenos,numberOfClusters,0.05/numberOfClusters)
if (pValueExists(input$clusteringType,input$selectedPhenotype)){
sumNormal <- sum(clusteringData$PhenoPValues[[input$clusteringType]][[as.numeric(input$selectedPhenotype)]]<0.05,na.rm = TRUE)
sumBonferroni <- sum(clusteringData$PhenoPValues[[input$clusteringType]][[as.numeric(input$selectedPhenotype)]]<(0.05/numberOfClusters),na.rm = TRUE)
enrichedPhenos <- sprintf("%s%s Clusters are significant (for this phenotype) with the cutoff <0.05<br/>%s Clusters are significant (for this phenotype) with the Bonferroni cutoff<br/>",enrichedPhenos,sumNormal,sumBonferroni)
}
metabs <- names(metabComplete)[currentCluster$subspace]
usedMetabs <- sprintf("This cluster was calculed using the following metabolites: <br/>%s<br/><br/>Significant Phenotypes:<br/>%s", paste(metabs, collapse = '<br/>'),enrichedPhenos)
output$metabUsed <- renderUI(HTML(usedMetabs))
clusterNumbers[currentCluster$objects] <- 17
clusterColors[currentCluster$objects] <- "red"
finalValues$idsFromCluster <- currentCluster$objects
} else {
output$metabUsed <- renderUI(HTML("No cluster selected"))
}
}
if(colorClusterNotPheno){
coloredPoints <- clusterColors
}
output$clustering <- renderPlot(plotFromClusters(lapply(clusteringData$Clique, `[[`, "objects"), label = FALSE, colors = coloredPoints, pch = clusterNumbers))
}
)
if (firstPCA){
pca_data <- prcomp(metabComplete, scale. = TRUE)
## Let us calculat the variances covered by components.
pca_data_perc=round(100*pca_data$sdev^2/sum(pca_data$sdev^2),1)
pcaValues$percentage <- pca_data_perc
## create a data frame with principal component 1 (PC1), PC2, Conditions and sample names
df_pca_data = data.frame(PC1 = pca_data$x[,1], PC2 = pca_data$x[,2])
pcaValues$visualisation <- df_pca_data
pcaValues$visWithId <- data.frame(cbind(names(finalValues$idFromNum),pcaValues$visualisation))
names(pcaValues$visWithId) <- c("id","x","y")
}
coloredPointsPCA <- coloredPoints
if(colorClusterNotPheno){
coloredPointsPCA <- clusterColors
}
plotNoLims(pcaValues$visualisation, "PCA - PC1 vs PC2", sprintf("PC1 (%s%%)",pcaValues$percentage[[1]]),sprintf("PC2 (%s%%)",pcaValues$percentage[[2]]), cols = coloredPointsPCA, pch = clusterNumbers)
}
})
#height = function() {session$clientData$output_pca_width}
getEnrichedString <- function(cluster){
enrichedPhenos <- ""
for (i in seq_len(length(finalValues$pheno)-1)){
currentPheno <- finalValues$pheno[[i]]
#print(names(finalValues$pheno)[[i]])
pVal <- calcPValueForCluster(cluster,currentPheno,i)
if (pVal<0.05){
if (finalValues$phenoFactors[[i]]){
phenoAsFactor <- factor(currentPheno)
countsAll <- table(factor(currentPheno,levels(phenoAsFactor)))
countsIn <- table(factor(currentPheno[cluster],levels(phenoAsFactor)))
countsOut <- table(factor(currentPheno[-cluster],levels(phenoAsFactor)))
relCountsAll <- (countsAll/sum(countsAll))*100
relCountsIn <- (countsIn/sum(countsIn))*100
relCountsOut <- (countsOut/sum(countsOut))*100
details <- ""
for (j in seq_len(length(relCountsAll))){
details <- sprintf("%s%s (mean overall: %s%%): %s%% vs. %s%%<br/>",details,names(relCountsAll)[[j]],twoDeci(relCountsAll[[j]]),twoDeci(relCountsIn[[j]]),twoDeci(relCountsOut[[j]]))
}
enrichedPhenos <- sprintf("%s<br/>%s (Percentage mean overall): in cluster vs. outside cluster<br/>%s-> pValue of %s<br/>",enrichedPhenos,names(finalValues$pheno)[[i]],details,pVal)
} else {
meanAll <- mean(currentPheno,na.rm = TRUE)
meanInside <- mean(currentPheno[cluster],na.rm = TRUE)
meanOutside <- mean(currentPheno[-cluster],na.rm = TRUE)
enrichedPhenos <- sprintf("%s<br/>%s (mean overall: %s): %s in cluster, %s outside of cluster<br/>-> pValue of %s<br/>",enrichedPhenos,names(finalValues$pheno)[[i]],fourDeci(meanAll),fourDeci(meanInside),fourDeci(meanOutside),pVal)
}
}
}
return(enrichedPhenos)
}
# Goto Input Validation Panel
observeEvent(input$toTypes, {
if (length(data())==4){
columnNames <- colnames(data()$values)
columns <- seq_len(length(columnNames))
names(columns)<-columnNames
updateSelectInput(session, "idField", choices = columns, selected = columns[data()$id])
updateSelectInput(session, "metabStart", choices = columns, selected = columns[data()$metab_start])
updateSelectInput(session, "metabEnd", choices = columns, selected = columns[data()$metab_end])
}
updateTabsetPanel(session, "tabs",
selected = "1")
output$pcaAll <- renderUI({
plotOutput(outputId = "pca",
brush = "pca_brush", height = plotsSize, width = plotsSize)
})
})
# Check if ID Field is unique
observeEvent(input$idField, {
if (length(data())==4){
dataNoNa <- data()$values[complete.cases(data()$values), ]
tempId <- as.numeric(input$idField)
if (length(dataNoNa[[tempId]])!=length(unique(dataNoNa[[tempId]]))){
output$idError <- renderText("Identifier is not unique")
} else {
output$idError <- NULL
}
}
})
# Reset the saved Clusterings
observeEvent(input$resetClustering, {
metabComplete <- data.frame(scale(finalValues$metab))
clusteringData$PhenoPValues[[input$clusteringType]] <- list()
switch(input$clusteringType,
SOM={
clusteringData$SOM <- som(data.matrix(metabComplete), grid = somgrid(xdim = 5,ydim = 5, topo = "hexagonal"),rlen = 100, alpha = c(0.08,0.01))
},
COSA={
temps$index = list()
temps$oldk = NULL
temps$oldx = NULL
temps$hc = NULL
temps$grps = c(0)
temps$clickHist = NULL
temps$rects = data.frame(x1=NULL,y1=NULL,x2=NULL,y2=NULL,border=NULL)
clusteringData$COSA <- cosa2(metabComplete, lambda = 0.4, niter = 2, noit = 30)
clusteringData$COSA$smacof <- smacof(clusteringData$COSA$D, niter = 30, interc = 1, VERBOSE = FALSE, PLOT = FALSE)
clusteringData$COSA$idsAndSmacof <- data.frame(cbind(names(finalValues$idFromNum),clusteringData$COSA$smacof$X))
names(clusteringData$COSA$idsAndSmacof) <- c("id","x","y")
toggleModal(session, "cosaHist", toggle = "toggle")
},
DOC={
clusteringData$DOC <- runDoc(metabComplete, 0.1, 0.75, calcW(metabComplete, 1.3))
docMDSValues <- plotFromClusters(getIdsDoc(clusteringData$DOC), returnMDS = TRUE)
clusteringData$DocMDS <- data.frame(cbind(seq_len(nrow(docMDSValues)),docMDSValues))
names(clusteringData$DocMDS) <- c("id","x","y")
},
Clique={
inds <- nrow(metabComplete)
xi <- 8
if (inds>800){
xi <- xi + floor((inds-800)/200)
if (xi>14){
xi <- 14
}
}
clusteringData$Clique <- CLIQUE(metabComplete, xi = xi,tau = 0.1)
cliqueMDSValues <- plotFromClusters(lapply(clusteringData$Clique, `[[`, "objects"), returnMDS = TRUE)
clusteringData$CliqueMDS <- data.frame(cbind(seq_len(nrow(cliqueMDSValues)),cliqueMDSValues))
names(clusteringData$CliqueMDS) <- c("id","x","y")
}
)
})
# Goto Output Panel
observeEvent(input$toPheno, {
if (length(data())==4){
clusteringData$Clique = list()
clusteringData$SOM = list()
clusteringData$COSA = list()
clusteringData$DOC = list()
tempId <- as.numeric(input$idField)
tempStart <- as.numeric(input$metabStart)
tempEnd <- as.numeric(input$metabEnd)
inputData <- data()$values[complete.cases(data()$values[c(tempStart:tempEnd)]), ]
if (length(inputData[[tempId]])!=length(unique(inputData[[tempId]]))){
output$idError <- renderText("Identifier is not unique")
return()
}
if (input$externalPheno[[1]]&&!is.null(input$inputPheno)){
dataTemp <- merge(inputData, readPhenoFile(input$inputPheno$datapath), by = names(inputData)[[tempId]], all.x = TRUE)
if (nrow(dataTemp)==0){
showNotification("Could not match any metab ids with phenotype ids. Please check you have the correct
ID column, they are named the same and the values are written in the same format.",type = "warning")
showNotification("Continuing with the phenotypes from the metabolite data sheet.",type = "message")
} else {
inputData <- dataTemp
}
}
finalValues$tempInfo <- "Nothing clicked/brushed yet"
finalValues$completeData <- inputData
finalValues$id <- inputData[[tempId]]
finalValues$numFromId <- seq_len(length(inputData[[tempId]]))
names(finalValues$numFromId) <- inputData[[tempId]]
finalValues$idFromNum <- inputData[[tempId]]
names(finalValues$idFromNum) <- seq_len(length(inputData[[tempId]]))
finalValues$metab <- inputData[c(tempStart:tempEnd)]
finalValues$pheno <- inputData[-c(tempId,c(tempStart:tempEnd))]
finalValues$phenoFactors <- logical(length(finalValues$pheno))
finalValues$pheno$None <- numeric(length(finalValues$id[[1]]))
pheno <- finalValues$pheno[-which(names(finalValues$pheno)=="None")]
numberLevels <- sapply(pheno, function(x) length(unique(x)))
probablyFactors <- seq_len(length(pheno))[numberLevels<10]
output$phenoWhichFactor <- renderUI({
checkboxGroupInput(
"phenosWhichAreFactors",
"",
selected = probablyFactors,
choiceNames = names(pheno),
choiceValues = seq_len(length(pheno))
)
})
columnNames <- names(finalValues$pheno)
#clusteringData$PhenoPValues <- as.list(rep(NA,length(columnNames)))
columns <- seq_len(length(columnNames))
names(columns)<-columnNames
if (length(columns)>0){
updateSelectInput(session, "selectedPhenotype", choices = columns, selected = columns[["None"]])
}
}
updateTabsetPanel(session, "tabs",
selected = "2")
})
# Goto Input Validation Panel
observeEvent(input$toOut, {
if (!is.null(input$phenosWhichAreFactors)){
finalValues$phenoFactors[as.numeric(input$phenosWhichAreFactors)] <- TRUE
}
updateTabsetPanel(session, "tabs",
selected = "3")
})
#Take phenotypes from different file
output$phenoInputField <- renderUI({
if (input$externalPheno[[1]]){
return(fileInput(inputId = "inputPheno",
label = "Phenotypes (they need to have the same unique id as the metab data):",
accept = c(".xlsx",".csv",".tsv")))
} else {
return(NULL)
}
})
output$info <- renderUI({
clusteringClick <- input$clustering_click
clusteringBrush <- input$clustering_brush
pcaBrush <- input$pca_brush
idsFromCluster <- finalValues$idsFromCluster
ids <- NULL
currentClusterId <- as.numeric(input$clusterId)
if (input$clusteringType=="Nothing"){
return(HTML(finalValues$tempInfo))
}
if (!is.null(clusteringBrush)&!equalsBrush(temps$clusteringBrush,clusteringBrush)){
switch(input$clusteringType,
SOM={
},
COSA={
ids <- brushedPoints(clusteringData$COSA$idsAndSmacof, clusteringBrush, xvar = "x", yvar = "y")$id
},
DOC={
ids <- brushedPoints(clusteringData$DocMDS, clusteringBrush, xvar = "x", yvar = "y")$id
},
{
# default is using Clique
ids <- brushedPoints(clusteringData$CliqueMDS, clusteringBrush, xvar = "x", yvar = "y")$id
}
)
} else if (!is.null(pcaBrush)&!equalsBrush(temps$pcaBrush,pcaBrush)){
ids <- brushedPoints(pcaValues$visWithId, pcaBrush, xvar = "x", yvar = "y")$id
} else if (!is.null(clusteringClick)&!equalsClick(temps$clusteringClick,clusteringClick)){
switch(input$clusteringType,
SOM={
if (length(clusteringData$SOM)==0)
return(" ")
xValues <- clusteringData$SOM$grid$pts[,1]
distX <- abs(xValues-clusteringClick$x)
yValues <- clusteringData$SOM$grid$pts[,2]
distY <- abs(yValues-clusteringClick$y)
allBubbleIds <- 1:nrow(clusteringData$SOM$grid$pts)
currentBubbleId <- allBubbleIds[distX==min(distX)&distY==min(distY)]
if (length(currentBubbleId)==0)
return(" ")
ids <- seq_len(nrow(clusteringData$SOM$data[[1]]))[clusteringData$SOM$unit.classif==currentBubbleId]
currentClusterId <- as.numeric(currentBubbleId)
#
#finalValues$currentIds <- finalValues$idFromNum[as.character(ids)]
#phenotypes <- finalValues$pheno[[as.numeric(input$selectedPhenotype)]]
#phenoInPoints <- phenotypes[ids]
#phenotypeAverage <- mean(phenoInPoints[!is.na(phenoInPoints)])
#naPercentage <- length(phenoInPoints[is.na(phenoInPoints)])/length(phenoInPoints)*100
#
#averageInBubble <- clusteringData$SOM$codes[[1]][currentBubbleId,]
#averagesFormatted <- mapply(function(x,y) paste(x, round(as.numeric(y), digits=4), sep=": "), names(finalValues$metab), averageInBubble, SIMPLIFY=FALSE)
#average <- sprintf("The code vector for this node is: <br/>%s", paste(averagesFormatted, collapse = '<br/>'))
#
#idString <- sprintf("The area you selected (phenotype average of %s, %s%% NA values), contains the following ids: <br/>%s", round(phenotypeAverage, digits = 2),round(naPercentage, digits = 2), paste(finalValues$currentIds, collapse = ', '))
#finalValues$tempInfo <- paste(idString, average, sep = '<br/>')
},
COSA={
},
DOC={
},
{
# default is using Clique
}
)
} else if (!is.null(idsFromCluster)&!identical(idsFromCluster,temps$idsFromClusterOld)){
ids <- idsFromCluster
}
if (!is.null(ids)){
finalValues$currentIds <- finalValues$idFromNum[as.character(ids)]
phenotypes <- finalValues$pheno[[as.numeric(input$selectedPhenotype)]]
phenoInPoints <- phenotypes[ids]
phenotypeAverage <- mean(phenoInPoints[!is.na(phenoInPoints)])
naPercentage <- length(phenoInPoints[is.na(phenoInPoints)])/length(phenoInPoints)*100
if (input$clusteringType=="SOM"){
averageInBubble <- clusteringData$SOM$codes[[1]][currentClusterId,]
averagesFormatted <- mapply(function(x,y) paste(x, round(as.numeric(y), digits=4), sep=": "), names(finalValues$metab), averageInBubble, SIMPLIFY=FALSE)
average <- sprintf("The code vector for this node is: <br/>%s", paste(averagesFormatted, collapse = '<br/>'))
} else {
averageSelected <- colMeans(finalValues$metab[ids,])
averagesFormatted <- mapply(function(x,y) paste(x, round(as.numeric(y), digits=4), sep=": "), names(finalValues$metab), averageSelected, SIMPLIFY=FALSE)
average <- sprintf("The average values in this area are: <br/>%s", paste(averagesFormatted, collapse = '<br/>'))
}
idString <- sprintf("The area you selected (phenotype average of %s, %s%% NA values), contains the following ids: <br/>%s", round(phenotypeAverage, digits = 2),round(naPercentage, digits = 2), paste(finalValues$currentIds, collapse = ', '))
finalValues$tempInfo <- paste(idString, average, sep = '<br/>')
}
temps$clusteringClick <- clusteringClick
temps$clusteringBrush <- clusteringBrush
temps$pcaBrush <- pcaBrush
temps$idsFromClusterOld <- idsFromCluster
return(HTML(finalValues$tempInfo))
})
output$downloadClusterIds <- renderUI({
currentClusters <- getCurrentClusters()
if (length(currentClusters)==0){
return(NULL)
} else {
downloadButton("downloadClusterValues", "Download ids of current clusters")
}
})
# Downloads the ids of the current clusters
output$downloadClusterValues <- downloadHandler(
filename = function() {
paste("ids-", input$clusteringType, Sys.Date(), ".tsv", sep="")
},
content = function(file) {
selectedPheno <- c(7,9,21,22,24)
currentClusters <- getCurrentClusters()
pVals <- paste(names(finalValues$pheno)[selectedPheno],"pVal",sep="-")
means <- paste(names(finalValues$pheno)[selectedPheno],"mean",sep="-")
header <- sprintf("ClusterId\tIdsInCluster\t%s", paste(pVals, means, sep="\t", collapse = "\t"))
dataToDownload <- unlist(sapply(seq_along(currentClusters), function(x){
cluster <- finalValues$idFromNum[as.character(currentClusters[[x]])]
ids <- paste(cluster, collapse=",")
result <- paste(x,ids,sep = "\t")
for (i in selectedPheno){
currentPheno <- finalValues$pheno[[i]]
meanInside <- mean(currentPheno[cluster],na.rm = TRUE)
meanOutside <- mean(currentPheno[-cluster],na.rm = TRUE)
meanAll <- mean(currentPheno,na.rm = TRUE)
if (!pValueExists(input$clusteringType,i)){
clusteringData$PhenoPValues[[input$clusteringType]][[i]] <- unlist(sapply(currentClusters, function(y) calcPValueForCluster(y,finalValues$pheno[[i]],i)))
}
result <- sprintf("%s\t%s\t%s_%s_%s", result,clusteringData$PhenoPValues[[input$clusteringType]][[i]][[x]],meanInside,meanOutside,meanAll)
}
return(result)
})
)
write(c(header,dataToDownload), file, ncolumns = 1)
},
contentType = "text/tab-separated-values"
)
output$downloadButton <- renderUI({
if (length(finalValues$currentIds)==0){
return(NULL)
} else {
downloadButton("downloadValues", "Download selected metabolites")
}
})
# Downloads the current subset displayed in info
output$downloadValues <- downloadHandler(
filename = function() {
paste("metabAndPheno-", Sys.Date(), ".tsv", sep="")
},
content = function(file) {
dataToDownload <- finalValues$completeData[finalValues$numFromId[as.character(finalValues$currentIds)],]
write.table(dataToDownload, file, sep = "\t", dec = ",", row.names=FALSE)
},
contentType = "text/tab-separated-values"
)
PCASwitchObserver <- observe({
if (input$clusteringType=="Nothing"){
return()
}
if(input$pcaSwitch[[1]]){
output$pcaAll <- renderUI({
return({
fluidRow(
column(9,
plotOutput(outputId = "pca",
brush = "pca_brush", height = plotsSize, width = plotsSize)
),
column(3,
selectInput(inputId = "clusterId",
label = "Select the cluster:",
choices = list()),
checkboxInput(inputId = "colorClusterNoPheno",
label = "Color cluster in red instead of phenotype"),
htmlOutput("metabUsed")
)
)
})
})
current <- getCurrentClusters()
len <- length(current)
columns <- seq(0,len)
if (len>0&length(finalValues$pheno)!=as.numeric(input$selectedPhenotype)){
if (!pValueExists(input$clusteringType,input$selectedPhenotype)|input$clusteringType=="COSA"){
clusteringData$PhenoPValues[[input$clusteringType]][[as.numeric(input$selectedPhenotype)]] <- unlist(sapply(current, function(x) calcPValueForCluster(x,finalValues$pheno[[as.numeric(input$selectedPhenotype)]],as.numeric(input$selectedPhenotype))))
}
pValues <- clusteringData$PhenoPValues[[input$clusteringType]][[as.numeric(input$selectedPhenotype)]]
pValues <- c(max(pValues)+1,pValues)
if (length(columns)!=length(pValues)){
print("Something is wrong")
print(paste("Columns:",length(columns),"pValues:",length(pValues)))
}
columns <- columns[order(pValues)]
}
updateSelectInput(session, "clusterId", choices = columns)
} else {
output$pcaAll <- renderUI({
plotOutput(outputId = "pca",
brush = "pca_brush", height = plotsSize, width = plotsSize)
})
}
})
getCurrentClusters <- function(){
if (input$clusteringType=="Nothing"){
return()
}
if (input$clusteringType=="Clique"){
return(lapply(clusteringData$Clique, "[[", "objects"))
} else if (input$clusteringType=="DOC"){
return(getIdsDoc(clusteringData$DOC))
} else if (input$clusteringType=="SOM"){
ids <- 1:nrow(finalValues$metab)
clusters <- list()
for (i in seq_len(25)){
clusters[[i]] <- ids[clusteringData$SOM$unit.classif==as.numeric(i)]
}
return(clusters)
}
return(temps$index)
}
output$hist <- renderPlot({
if (length(clusteringData$COSA)>0){
if (is.null(clusteringData$COSA$hist)){
clusteringData$COSA$hist <- hierclust(clusteringData$COSA$D, denplot = FALSE)
}
if (!is.null(input$clickHist)){
if (is.null(clusteringData$COSA$hist)|equalsClick(input$clickHist,temps$clickHist)){
return(clusteringData$COSA$hist)
}
rec.col = "blue"
old.col = "blue"
retval <- temps$index #list()
oldk <- temps$oldk #NULL
oldx <- temps$oldx #NULL
id <- temps$id #1
cat("Showing dynamic visualisation. Press Escape/Ctrl + C to stop.")
x <- input$clickHist
if (is.null(x)) {
break
}
k <- min(which(rev(clusteringData$COSA$hist$height) < x$y))
k <- max(k, 2)
if (!is.null(oldx)) {
modifiedRect(clusteringData$COSA$hist, k = oldk, x = oldx, border = old.col)
}
retval[[temps$id]] <- unlist(modifiedRect(clusteringData$COSA$hist, k = k, x = x$x,
border = rec.col))
temps$oldx <- x$x
temps$oldk <- k
grps <- temps$grps
grps[retval[[temps$id]]] <- temps$id
names(retval) <- paste("grp", 1:temps$id, sep = "")
temps$id <- temps$id+1
temps$clickHist <- x
temps$grps <- grps
temps$index <- unique(retval)
temps$index <- temps$index[!unlist(sapply(temps$index, is.null))]
#invisible(list(grps = grps, index = retval))
}
outPlot <- ggdendrogram(clusteringData$COSA$hist$dendro)
if (nrow(temps$rects)>0){
outPlot <- outPlot + geom_rect(data = temps$rects, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2), color="blue")
}
outPlot
}
})
modifiedRect <- function (tree, k = NULL, which = NULL, x = NULL, h = NULL,
border = 2, cluster = NULL) {
if (length(h) > 1L | length(k) > 1L)
stop("'k' and 'h' must be a scalar")
if (!is.null(h)) {
if (!is.null(k))
stop("specify exactly one of 'k' and 'h'")
k <- min(which(rev(tree$height) < h))
k <- max(k, 2)
}
else if (is.null(k))
stop("specify exactly one of 'k' and 'h'")
if (k < 2 | k > length(tree$height))
stop(gettextf("k must be between 2 and %d", length(tree$height)),
domain = NA)
if (is.null(cluster))
cluster <- cutree(tree, k = k)
clustab <- table(cluster)[unique(cluster[tree$order])]
m <- c(0, cumsum(clustab))
if (!is.null(x)) {
if (!is.null(which))
stop("specify exactly one of 'which' and 'x'")
which <- x
for (n in seq_along(x)) which[n] <- max(which(m < x[n]))
}
else if (is.null(which))
which <- 1L:k
if (any(which > k))
stop(gettextf("all elements of 'which' must be between 1 and %d",
k), domain = NA)
border <- rep_len(border, length(which))
retval <- list()
for (n in seq_along(which)) {
newRow <- data.frame(x1=m[which[n]] + 0.66, y1=par("usr")[3L], x2=m[which[n] + 1] + 0.33, y2=mean(rev(tree$height)[(k - 1):k]), border=border[n])
temps$rects <- rbind(temps$rects, newRow)
retval[[n]] <- which(cluster == as.integer(names(clustab)[which[n]]))
}
temps$rects <- unique.data.frame(temps$rects)
invisible(retval)
}
pValueExists <- function(clustType,phenotype){
one <- !is.null(clusteringData$PhenoPValues[[clustType]])
two <- length(clusteringData$PhenoPValues[[clustType]])>=as.numeric(phenotype)
result <- one & two
if (result){
result <- !is.null(clusteringData$PhenoPValues[[clustType]][[as.numeric(phenotype)]])
}
return(result)
}
#Scales vector to the range 0 to 1
range01 <- function(x){(x-min(x[!is.na(x)]))/(max(x[!is.na(x)])-min(x[!is.na(x)]))}
# round to k decimals
specify_decimal <- function(k) { function(x){trimws(format(round(x, k), nsmall=k))} }
# round to 4 decimals
fourDeci <- specify_decimal(4)
# round to 2 decimals
twoDeci <- specify_decimal(2)