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server.R
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server.R
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options(shiny.maxRequestSize=10000*1024^2)
library(openxlsx)
library(samr)
library(GSA)
library(shiny)
source("GSA.listsets.revised.R")
source("GSA.correlate.revised.R")
source("GSA.plot.revised.R")
shinyServer(function(input, output, session) {
##########Read uploaded data!
getData = reactive({
objFile = input$file$datapath
if(!is.na(objFile)){
dat = read.xlsx(as.character(objFile), 1, colNames = FALSE)
geneid = dat[-1,1]
genenames = dat[-1,2]
imputedX = NULL
x = as.matrix(dat[-1, c(-1,-2)])
class(x) = "numeric"
originalX = NULL
originalX = cbind(geneid, genenames, x)
colnamesOriginalX = as.vector(dat[1,])
colnamesOriginalX[1] = " "
colnamesOriginalX[2] = " "
colnames(originalX) = colnamesOriginalX
if(sum(is.na(x)) > 0){
imputedummy = impute.knn(x, k = input$numberOfNeighbors)
x = imputedummy$data
imputedX = cbind(geneid, genenames, x)
colnamesImputedX = as.vector(dat[1,])
colnamesImputedX[1] = " "
colnamesImputedX[2] = " "
colnames(imputedX) = colnamesImputedX
}
firstrow = as.vector(dat[1,c(-1,-2)])
censoring.status = NULL
eigengene.number = NULL
if( (input$responseType_seq == "Survival" && input$assayType == "seq") || (input$responseType_array == "Survival" && input$assayType == "array")){
survival = survival(firstrow)
y = survival$y
censoring.status = survival$censoring.status
}
else if( (input$responseType_array == "Pattern discovery" && input$assayType == "array") ){
y = NULL
eigengene.number = firstrow[1]
last = nchar(eigengene.number)
eigengene.number = as.numeric(substr(eigengene.number, last, last))
}
else{
y = firstrow
}
data =list(x=x,y=y, genenames=genenames, geneid=geneid, logged2= as.logical(input$dataLogged), censoring.status = censoring.status, eigengene.number = eigengene.number, imputedX = imputedX, originalX = originalX)
data
}
})
#########Modify changes for survival datasets
survival = function(firstrow){
y = vector(length = length(firstrow), mode ="integer")
censoring.status = vector(length = length(firstrow), mode ="integer")
for(i in seq.int(length(y))) {
split = strsplit(as.character(firstrow[,i]), ",")
y[i] = as.character(data.frame(split)[1,])
y[i] = gsub(" ","", y[i])
y[i] = substring(y[i], 2)
censoring.status[i] = as.character(data.frame(split)[2,])
censoring.status[i] = gsub(" ","", censoring.status[i])
censoring.status[i] = substring(censoring.status[i], 1, 1)
}
y = as.numeric(y)
censoring.status = as.numeric(censoring.status)
list(y= y, censoring.status = censoring.status)
}
##########GSA ##############################
######get results from GSA
getGSA = reactive({
if(input$goButton2 != 0){
data = getData()
gmtFile = input$gmtFile
if(!is.null(data) && !is.null(gmtFile)){
x = data$x
y = data$y
class(y) = "numeric"
genenames = data$genenames
censoring.status = data$censoring.status
geneset.obj = GSA.read.gmt(gmtFile$datapath)
genesets = geneset.obj$genesets
geneset.names = geneset.obj$geneset.names
s0.perc = if(is.na(input$s0.perc) || input$s0 == "Automatic"){NULL}else{input$s0.perc}
GSA.obj = GSA(x, y, genenames=genenames, genesets=genesets, method = "maxmean", resp.type= input$responseType_array, minsize = input$minGeneSet, maxsize = input$maxGeneSet, random.seed = input$random.seed, knn.neighbors = input$numberOfNeighbors, s0.perc = s0.perc, nperms = input$nperms, censoring.status = censoring.status)
list(GSA.obj = GSA.obj, geneset.names = geneset.names, genenames = genenames, genesets = genesets, geneset.obj = geneset.obj)
}
}
})
getGSACorrelateList = reactive({
GSA = getGSA()
if(!is.null(GSA)){
geneset.obj = GSA$geneset.obj
genenames = GSA$genenames
GSA.correlate.list = GSA.correlate.revised(geneset.obj, genenames)
GSA.correlate.list
}
})
getGSAList = reactive({
GSA = getGSA()
if(!is.null(GSA)){
GSA.obj = GSA$GSA.obj
geneset.names = GSA$geneset.names
GSA.list = GSA.listsets.revised(GSA.obj, geneset.names = geneset.names, FDRcut = findFDR())
GSA.list
}
})
getGSAFullList = reactive({
GSA = getGSA()
if(!is.null(GSA)){
GSA.obj = GSA$GSA.obj
geneset.names = GSA$geneset.names
GSA.list = GSA.listsets.revised(GSA.obj, geneset.names = geneset.names, FDRcut = 1)
GSA.list
}
})
getComputedValues2 = reactive({
GSA = getGSA()
if(!is.null(GSA)){
GSA.obj = GSA$GSA.obj
computedValues = matrix(NA, nrow = 2, ncol = 1)
colnames(computedValues) = "Values"
rownames(computedValues) = c("Exchangibility factor s0", "s0 percentile")
computedValues[1,1] = GSA.obj$s0
computedValues[2,1] = GSA.obj$s0.perc
cbind(rownames(computedValues), computedValues)
}
})
getInputParameters2 = reactive({
input$goButton2
findFDR()
isolate({
if(input$goButton2!= 0){
current = matrix(NA, nrow = 10, ncol = 1)
objFile = input$file$name
gmtFile = input$gmtFile
s0.perc = if(is.na(input$s0.perc) || input$s0 == "Automatic"){"Automatic"}else{paste(input$s0.perc, " percentile")}
current[1,1] = as.character(objFile)
current[2,1] = gmtFile$name
current[3,1] = input$responseType_array
current[4,1] = findFDR()
current[5,1] = input$nperms
current[6,1] = s0.perc
current[7,1] = input$numberOfNeighbors
current[8,1] = input$minGeneSet
current[9,1] = input$maxGeneSet
current[10,1] = input$random.seed
rownames_current = c("File Name", "Gene set (gmt) file", "Data Type", "False discovery rate (in each tail)", "Number of permutations", "Input percentile for exchangeability factor s0", "Number of neighbors for KNN", "Minimum gene set size", "Maximum gene set size", "Seed for Random number generator")
rownames(current) = rownames_current
colnames(current) = "value"
cbind(rownames_current, current)
}
})
})
findFDR = reactive({
if(input$fdrChoice == "FDR Slider")
input$fdrSlider
else if (input$fdrChoice == "Manually Enter FDR")
input$fdrInput
})
#########Output GSA tables/plots
output$geneSetTable = renderTable({
GSA.correlate.list = getGSACorrelateList()
if(!is.null(GSA.correlate.list)){
GSA.correlate.list$result
}
})
output$geneSetQuantile = renderTable({
GSA.correlate.list = getGSACorrelateList()
if(!is.null(GSA.correlate.list)){
GSA.correlate.list$QuantileCoverage
}
})
output$geneSetTableGenes = renderTable({
GSA.correlate.list = getGSACorrelateList()
if(!is.null(GSA.correlate.list)){
GSA.correlate.list$tableGenes
}
})
output$geneSetPlot = renderPlot({
GSA = getGSA()
if(!is.null(GSA)){
GSA.obj = GSA$GSA.obj
GSA.plot.revised(GSA.obj, FDRcut = 1, fac = 0)
}
})
output$positiveGeneSet = renderTable({
GSA.list = getGSAList()
if(!is.null(GSA.list$positive)){
if(!is.na(GSA.list$positive[1])){
GSA.list$positive
}
}
})
output$negativeGeneSet = renderTable({
GSA.list = getGSAList()
if(!is.null(GSA.list$negative)){
if(!is.na(GSA.list$negative[1])){
GSA.list$negative
}
}
})
output$positiveFullGeneSet = renderTable({
GSAFullList = getGSAFullList()
if(!is.null(GSAFullList)){
GSAFullList$positive
}
})
output$negativeFullGeneSet = renderTable({
GSAFullList = getGSAFullList()
if(!is.null(GSAFullList)){
GSAFullList$negative
}
})
output$testGeneSet = renderTable({
GSA = getGSA()
if(!is.null(GSA)){
genesets = GSA$genesets
GSA.obj = GSA$GSA.obj
genenames = GSA$genenames
GSA.genescores(input$geneset.number, genesets, GSA.obj, genenames)
}
})
output$computedValues2 = renderTable({
if(!is.null(getComputedValues2())){
getComputedValues2()
}
})
output$inputParameters2 = renderTable({
inputParameters2 = getInputParameters2()
if(!is.null(inputParameters2)){
inputParameters2
}
})
######output texts in the GSA interface
output$geneSetInfoText <- renderText({
if(!is.null(getGSAList())){
"Information for gene set collection"
}
})
output$geneSetInfoText2 <- renderText({
if(!is.null(getGSAList())){
"Quantiles of fraction coverage of gene-sets"
}
})
output$geneSetInfoText3 <- renderText({
if(!is.null(getGSAList())){
"Table of number of genes in genesets"
}
})
output$GSAPlotText <- renderText({
if(!is.null(getGSAList())){
"GSA Plot"
}
})
output$GeneScoreText <- renderText({
if(!is.null(getGSAList())){
"Gene Score"
}
})
output$geneSetPositive = renderText({
GSA.list = getGSAList()
if(!is.null(GSA.list$nsets.pos)){
paste("Positive Gene Sets (", GSA.list$nsets.pos, ")", sep = "")
}
})
output$geneSetNegative = renderText({
GSA.list = getGSAList()
if(!is.null(GSA.list$nsets.neg)){
paste("Negative Gene Sets (", GSA.list$nsets.neg, ")", sep = "")
}
})
output$geneSetFullPositive = renderText({
GSAFullList = getGSAFullList()
if(!is.null(GSAFullList)){
paste("Positive Gene Sets (", GSAFullList$nsets.pos, ")", sep = "")
}
})
output$geneSetFullNegative = renderText({
GSAFullList = getGSAFullList()
if(!is.null(GSAFullList)){
paste("Negative Gene Sets (", GSAFullList$nsets.neg, ")", sep = "")
}
})
output$inputParametersText2 <- renderText({
if(!is.null(getGSA())){
"Input Parameters"
}
})
output$computedValuesText2 <- renderText({
if(!is.null(getGSA())){
"Computed Values"
}
})
#################################SAM
############get results from SAM
getSamrObj = reactive({
# input$goButton
if(input$goButton != 0){
data = getData()
if(!is.null(data)){
resp.type = if(input$assayType == "seq"){input$responseType_seq}else{input$responseType_array}
s0.perc = if(is.na(input$s0.perc) || input$s0 == "Automatic"){NULL}else{input$s0.perc}
center.arrays = as.logical(input$centerArrays)
samr.obj = samr(data, resp.type = resp.type, assay.type = input$assayType, s0.perc = s0.perc, nperms = input$nperms, center.arrays = center.arrays, testStatistic = input$testStatistic, time.summary.type = input$timeSummaryType, regression.method = input$regressionMethod, random.seed = input$random.seed, knn.neighbors = input$numberOfNeighbors)
samr.obj
}
}
})
getDeltaTable = reactive({
samr.obj = getSamrObj()
if(!is.null(samr.obj)){
delta.table = samr.compute.delta.table(samr.obj, min.foldchange = input$min.foldchange)
delta.table
}
})
getEigengene = reactive({
samr.obj = getSamrObj()
if(!is.null(samr.obj) && input$responseType_array == "Pattern discovery"){
eigengene = samr.obj$eigengene
eigengene
}
})
getSampleSize = reactive({
ar = isolate(input$responseType_array)
seq = isolate(input$responseType_seq)
samr.obj = getSamrObj()
if(!is.null(samr.obj) && ((input$assayType == "array" && (ar == "Two class unpaired" || ar == "Two class paired" || ar == "One class" || ar == "Survival")) || (input$assayType == "seq" && (seq == "Two class unpaired" || seq == "Two class paired" || seq == "Survival")))){
data = getData()
y = data$y
dif = input$dif
#quick fix not allowing blocks for sample size
if(!substring(y[1],2,6) %in% c("Block", "block")){
samplesize.factors = input$sampleSizeFactors
samplesize.factors = as.numeric(unlist(strsplit(samplesize.factors, ",")))
samr.assess.samplesize.obj = samr.assess.samplesize(samr.obj, data, dif, samplesize.factors)
samr.assess.samplesize.obj
}
}
})
getMissRateTable = reactive({
samr.obj = getSamrObj()
if(!is.null(samr.obj)){
delta = findDelta()
delta.table = getDeltaTable()
missrate.table = samr.missrate(samr.obj, delta, delta.table)
missrate.table
}
})
getSiggenesTable = reactive({
samr.obj = getSamrObj()
if(!is.null(samr.obj)){
delta = findDelta()
data = getData()
delta.table = getDeltaTable()
min.foldchange = input$min.foldchange
siggenes.table = samr.compute.siggenes.table(samr.obj,delta, data, delta.table, min.foldchange = min.foldchange, compute.localfdr= input$localFDR)
siggenes.table
}
})
getAllgenesTable = reactive({
samr.obj = getSamrObj()
if(!is.null(samr.obj)){
delta = findDelta()
data = getData()
delta.table = getDeltaTable()
min.foldchange = input$min.foldchange
Allgenes = samr.compute.siggenes.table(samr.obj,delta, data, delta.table, min.foldchange = min.foldchange, compute.localfdr= input$localFDR, all.genes= TRUE)
Allgenes
}
})
getImputedX = reactive({
data = getData()
imputedX = data$imputedX
if(!is.null(imputedX)){
imputedX
}
})
getOriginalX = reactive({
data = getData()
originalX = data$originalX
if(!is.null(originalX)){
originalX
}
})
getComputedValues= reactive({
samr.obj = getSamrObj()
if(!is.null(samr.obj)){
computedValues = matrix(NA, nrow = 5, ncol = 1)
colnames(computedValues) = "Values"
rownames(computedValues) = c("Estimated proportion of non-null features (genes)", "s0 percentile", "False Discovery Rate", "Estimated tail strength", "Estimated standard error of tail strength")
tail.strength = getTailStrength()
computedValues[1,1] = samr.obj$pi0
computedValues[2,1] = samr.obj$s0.perc
delta.table = samr.compute.delta.table(samr.obj, min.foldchange= input$min.foldchange, dels= findDelta())[5]
computedValues[3,1] = if(is.na(delta.table)){0.00}else{delta.table}
computedValues[4,1] = tail.strength$ts
computedValues[5,1] = tail.strength$se.ts
if(input$assayType == "array" && input$analysisType == "Standard" && input$responseType_array == "Multiclass"){
multiclassAdd = matrix(NA, nrow = 2, ncol = 1)
rownames(multiclassAdd) = c("2.5% null value for multiclass contrasts", "97.5% null value for multiclass contrasts")
multiclassAdd[1,1] = samr.obj$stand.contrasts.95[1]
multiclassAdd[2,1] = samr.obj$stand.contrasts.95[2]
computedValues = rbind(computedValues, multiclassAdd)
}
if(input$assayType == "seq"){
depth = samr.obj$depth
seqAdd = matrix(NA, nrow = 1, ncol = 1)
rownames(seqAdd) = "Quantiles of estimated sequencing depths (0%, 25%, 50%, 75%, 100%)"
seqAdd[1,1] = paste(quantile(depth, 0)[[1]], quantile(depth, 0.25)[[1]], quantile(depth, 0.5)[[1]], quantile(depth, 0.75)[[1]], quantile(depth, 1)[[1]], sep = ", ")
computedValues = rbind(computedValues, seqAdd)
}
cbind(name = rownames(computedValues), computedValues)
}
})
getTailStrength = reactive({
samr.obj = getSamrObj()
if(!is.null(samr.obj)){
samr.tail.strength(samr.obj)
}
})
getInputParameters = reactive({
input$goButton
findDelta()
input$min.foldchange
isolate({
if(input$goButton!= 0){
if(input$assayType == "array"){
current = matrix(NA, nrow = 14, ncol = 1)
objFile = input$file$name
s0.perc = if(is.na(input$s0.perc) || input$s0 == "Automatic"){"Automatic"}else{paste(input$s0.perc, " percentile")}
current[1,1] = as.character(objFile)
current[2,1] = input$responseType_array
current[3,1] = capitalize(input$assayType)
current[4,1] = input$centerArrays
current[5,1] = findDelta()
current[6,1] = input$min.foldchange
current[7,1] = if(input$testStatistic == "standard"){"T-statistic"} else{"Wilcoxon"}
current[8,1] = capitalize(input$regressionMethod)
current[9,1] = input$dataLogged
current[10,1] = input$nperms
current[11,1] = s0.perc
current[12,1] = input$numberOfNeighbors
current[13,1] = input$random.seed
current[14,1] = capitalize(input$timeSummaryType)
rownames_current = c("File Name", "Data Type", "Array or Seq data?", "Arrays centered?", "Delta", "Minimum fold change", "Test statistic", "Regression method", "Are data are log scale?", "Number of permutations", "Input percentile for exchangeability factor s0", "Number of neighbors for KNN", "Seed for Random number generator", "Time summary type")
if(input$responseType_array == "Quantitative"){
current = matrix(current[c(-6, -7, -9, -14),], ncol = 1)
rownames_current = rownames_current[c(-6,-7, -9, -14)]
}
else if(input$responseType_array == "Two class unpaired"){
current = matrix(current[c(-8, -14),], ncol = 1)
rownames_current = rownames_current[c(-8, -14)]
}
else if(input$responseType_array == "Survival"){
current = matrix(current[c(-6, -7,-8,-9,-14),], ncol = 1)
rownames_current = rownames_current[c(-6, -7,-8,-9, -14)]
}
else if(input$responseType_array == "Multiclass" || input$responseType_array == "One class" || input$responseType_array == "Pattern discovery"){
current = matrix(current[c(-6,-7,-8,-9,-14),], ncol = 1)
rownames_current = rownames_current[c(-6,-7,-8,-9, -14)]
}
else if(input$responseType_array == "Two class paired"){
current = matrix(current[c(-7,-8,-14),], ncol = 1)
rownames_current = rownames_current[c(-7,-8,-14)]
}
else if(input$responseType_array == "Two class unpaired timecourse"){
current = matrix(current[c(-6, -8, -9),], ncol = 1)
rownames_current = rownames_current[c(-6, -8, -9)]
}
else if(input$responseType_array == "Two class paired timecourse"){
current = matrix(current[c(-6, -7, -8, -9),], ncol = 1)
rownames_current = rownames_current[c(-6, -7, -8, -9)]
}
else if(input$responseType_array == "One class timecourse"){
current = matrix(current[c(-6, -7, -8, -9),], ncol = 1)
rownames_current = rownames_current[c(-6, -7, -8, -9)]
}
rownames(current) = rownames_current
colnames(current) = "value"
current = cbind(name = rownames_current, current)
}
if(input$assayType == "seq"){
current = matrix(NA, nrow = 9, ncol = 1)
objFile = input$file$name
current[1,1] = as.character(objFile)
current[2,1] = input$responseType_seq
current[3,1] = capitalize(input$assayType)
current[4,1] = input$centerArrays
current[5,1] = findDelta()
current[6,1] = input$min.foldchange
current[7,1] = input$nperms
current[8,1] = input$numberOfNeighbors
current[9,1] = input$random.seed
rownames_current = c("File Name", "Data Type", "Array or Seq data?", "Arrays centered?", "Delta", "Minimum fold change", "Number of permutations", "Number of neighbors for KNN", "Seed for Random number generator")
if(input$responseType_seq == "Quantitative" || input$responseType_seq == "Survival" || input$responseType_seq == "Multiclass"){
current = matrix(current[c(-6),], ncol = 1)
rownames_current = rownames_current[c(-6)]
}
rownames(current) = rownames_current
colnames(current) = "value"
current = cbind(name = rownames_current, current)
}
current
}
})
})
findDelta = reactive({
if(input$deltaChoice == "Delta Slider")
input$deltaSlider
else if (input$deltaChoice == "Manually Enter Delta")
input$deltaInput
})
output$rseed <- renderUI({
if(input$randomButton == 0){
value = 1234567
}else{
value = round(runif(1, 0, 10^6))
}
numericInput("random.seed", "Random Seed", value= value)
})
output$slider <- renderUI({
delta.table = getDeltaTable()
if(!is.null(delta.table)){
sliderInput("deltaSlider", label = "Delta value", min = 0, max = round(as.numeric(delta.table[dim(delta.table)[1],1]), 2), value = 0, step = 0.01)
}
})
capitalize = function(word){
letters = strsplit(word,'')
theletters = letters[[1]]
theletters[1] = toupper(theletters[1])
paste(theletters,collapse='')
}
############Write texts for SAM interface
output$missRateText = renderText({
if(!is.null(getSamrObj())){
paste("Estimated Miss rates for Delta = ", findDelta())
}
})
output$eigengeneText = renderText({
if(!is.null(getSamrObj()) && input$responseType_array == "Pattern discovery"){
"Eigengene"
}
})
output$eigengene = renderTable({
eigengene = getEigengene()
if(!is.null(eigengene)){
eigengene
}
})
output$negativeGenesText <- renderText({
siggenes.table = getSiggenesTable()
if(!is.null(siggenes.table)){
paste("Negative Genes (", siggenes.table$ngenes.lo, ")", sep ="")
}
})
output$positiveGenesText <- renderText({
siggenes.table = getSiggenesTable()
if(!is.null(siggenes.table)){
paste("Positive Genes (", siggenes.table$ngenes.up, ")", sep = "")
}
})
output$allPositiveGenesText <- renderText({
allgenes = getAllgenesTable()
if(!is.null(allgenes)){
paste("Positive Genes (", allgenes$ngenes.up, ")", sep = "")
}
})
output$allNegativeGenesText = renderText({
allgenes = getAllgenesTable()
if(!is.null(allgenes)){
paste("Negative Genes (", allgenes$ngenes.lo, ")", sep = "")
}
})
output$deltaTableText <- renderText({
if(!is.null(getSamrObj())){
"Delta Table"
}
})
output$samPlotText <- renderText({
if(!is.null(getSamrObj())){
"SAM Plot"
}
})
output$inputParametersText <- renderText({
if(!is.null(getSamrObj())){
"Input Parameters"
}
})
output$computedValuesText <- renderText({
if(!is.null(getSamrObj())){
"Computed Values"
}
})
output$sampleSizePlotText2 <- renderText({
if(!is.null(getSampleSize())){
"Sample Size Plot"
}
})
output$sampleTableText1 = renderText({
samr.assess.samplesize.obj = getSampleSize()
if(!is.null(getSampleSize())){
paste("Sample Size = ", samr.assess.samplesize.obj$samplesize.factor[1] * samr.assess.samplesize.obj$n, sep = "")
}
})
output$sampleTableText2 = renderText({
samr.assess.samplesize.obj = getSampleSize()
if(!is.null(getSampleSize())){
paste("Sample Size = ", samr.assess.samplesize.obj$samplesize.factor[2] * samr.assess.samplesize.obj$n, sep = "")
}
})
output$sampleTableText3 = renderText({
samr.assess.samplesize.obj = getSampleSize()
if(!is.null(getSampleSize())){
paste("Sample Size = ", samr.assess.samplesize.obj$samplesize.factor[3] * samr.assess.samplesize.obj$n, sep = "")
}
})
output$sampleTableText4 = renderText({
samr.assess.samplesize.obj = getSampleSize()
if(!is.null(getSampleSize())){
paste("Sample Size = ", samr.assess.samplesize.obj$samplesize.factor[4] * samr.assess.samplesize.obj$n, sep = "")
}
})
########Output tables and plots for SAM
output$missRate = renderTable({
if(!is.null(getMissRateTable())){
getMissRateTable()
}
})
output$sampleTable1 = renderTable({
samr.assess.samplesize.obj = getSampleSize()
if(!is.null(samr.assess.samplesize.obj)){
samr.assess.samplesize.obj$results[,,1]
}
})
output$sampleTable2 = renderTable({
samr.assess.samplesize.obj = getSampleSize()
if(!is.null(samr.assess.samplesize.obj)){
samr.assess.samplesize.obj$results[,,2]
}
})
output$sampleTable3 = renderTable({
samr.assess.samplesize.obj = getSampleSize()
if(!is.null(samr.assess.samplesize.obj)){
samr.assess.samplesize.obj$results[,,3]
}
})
output$sampleTable4 = renderTable({
samr.assess.samplesize.obj = getSampleSize()
if(!is.null(samr.assess.samplesize.obj)){
samr.assess.samplesize.obj$results[,,4]
}
})
output$samplePlot = renderPlot({
samr.assess.samplesize.obj = getSampleSize()
if(!is.null(samr.assess.samplesize.obj)){
samr.assess.samplesize.plot(samr.assess.samplesize.obj)
}
})
output$samrPlot = renderPlot({
samr.obj = getSamrObj()
delta = findDelta()
min.foldchange = input$min.foldchange
if(!is.null(samr.obj)){
samr.plot(samr.obj, delta, min.foldchange = min.foldchange)
}
})
output$deltaTable = renderTable({
delta.table = getDeltaTable()
if(!is.null(delta.table)){
delta.table
}
})
output$Allgenes.table.up = renderTable({
Allgenes = getAllgenesTable()
if(!is.null(Allgenes$genes.up)){
genes.up = Allgenes$genes.up
genes.up[,4:7] = round(as.numeric(as.character(genes.up[,4:7])),4)
genes.up
}
})
output$Allgenes.table.lo = renderTable({
Allgenes = getAllgenesTable()
if(!is.null(Allgenes$genes.lo)){
genes.lo = Allgenes$genes.lo
genes.lo[,4:7] = round(as.numeric(as.character(genes.lo[,4:7])),4)
genes.lo
}
})
output$siggenes.table.up <- renderTable({
siggenes.table = getSiggenesTable()
if(!is.null(siggenes.table$genes.up)){
genes.up = siggenes.table$genes.up
genes.up[,4:7] = round(as.numeric(as.character(genes.up[,4:7])),4)
genes.up
}
})
output$siggenes.table.lo <- renderTable({
siggenes.table = getSiggenesTable()
if(!is.null(siggenes.table$genes.lo)){
genes.lo = siggenes.table$genes.lo
genes.lo[,4:7] = round(as.numeric(as.character(genes.lo[,4:7])),4)
genes.lo
}
})
output$computedValues = renderTable({
if(!is.null(getComputedValues())){
getComputedValues()
}
})
output$inputParameters = renderTable({
inputParameters = getInputParameters()
if(!is.null(inputParameters)){
inputParameters
}
})
##########Saving results into xlsx format
#######SAM results save
savethis = observe({
if(input$saveButton != 0){
isolate({
dir = input$dir
file = input$fname
siggenes.table = getSiggenesTable()
missrate.table = getMissRateTable()
delta.table = getDeltaTable()
samr.assess.samplesize.obj = getSampleSize()
Allgenes = getAllgenesTable()
samr.obj = getSamrObj()
delta = findDelta()
min.foldchange = input$min.foldchange
inputParameters = getInputParameters()
inputParameters = inputParameters[,-1, drop = FALSE]
computedValues = getComputedValues()
computedValues = computedValues[,-1, drop = FALSE]
eigengene = getEigengene()
imputedX = getImputedX()
originalX = getOriginalX()
titleStyle = createStyle(fontSize = 14, fontColour = "#FFFFFF", halign = "center", fgFill = "#4F81BD")
wb = createWorkbook()
if(!is.null(samr.obj)){
png(file = "SAMPlot.png")
samr.plot(samr.obj, delta, min.foldchange = min.foldchange)
dev.off()
}
if(!is.null(samr.assess.samplesize.obj)){
png(file = "samplesizePlot.png")
samr.assess.samplesize.plot(samr.assess.samplesize.obj)
dev.off()
}
if(!is.null(originalX)){
addWorksheet(wb, sheetName = "Original Data")
writeData(wb, originalX, sheet = "Original Data")
}
if(!is.null(imputedX)){
addWorksheet(wb, sheetName = "Imputed Data")
writeData(wb, imputedX, sheet = "Imputed Data")
}
if(!is.null(samr.obj)){
addWorksheet(wb, sheetName = "SAM Plot")
insertImage(wb, sheet = "SAM Plot", file = "SAMPlot.png", width = 5, height = 5)
}
if(!is.null(delta.table)){
addWorksheet(wb, sheetName = "Delta Table")
writeData(wb, sheet = "Delta Table", x = "Delta Table")
writeData(wb, sheet = "Delta Table", x = delta.table, startRow = 2)
addStyle(wb, sheet = "Delta Table", titleStyle, rows = 1, cols = 1)
setColWidths(wb, "Delta Table", cols= 1:8, widths = 18)
}
if(!is.null(missrate.table)){
missraterow = nrow(delta.table) + 4
writeData(wb, sheet = "Delta Table", x = "Miss Rates", startRow = missraterow)
writeData(wb, sheet = "Delta Table", x = missrate.table, startRow = missraterow + 1)
addStyle(wb, sheet = "Delta Table", titleStyle, rows = missraterow, cols = 1)
}
if((siggenes.table$ngenes.up != 0) || (siggenes.table$ngenes.lo != 0)){
addWorksheet(wb, sheetName = "Significant Genes")
setColWidths(wb, "Significant Genes", cols= 1:11, widths = 18)
}
significantgenerow = 1
if(!is.null(siggenes.table$genes.up)){
writeData(wb, sheet = "Significant Genes", x = "Significant Pos", startRow = significantgenerow)
addStyle(wb, sheet = "Significant Genes", titleStyle, rows = significantgenerow, cols = 1)
writeData(wb, sheet = "Significant Genes", x = siggenes.table$genes.up, startRow = significantgenerow + 1)
significantgenerow = siggenes.table$ngenes.up + 4