## ---- eval=FALSE---------------------------------------------------------
## # 'MSstatsInput.csv' is the MSstats report from Skyline.
## input <- read.csv(file="MSstatsInput.csv")
## 
## raw <- SkylinetoMSstatsFormat(input)

## ---- eval=FALSE---------------------------------------------------------
## # Read in MaxQuant files
## proteinGroups <- read.table("proteinGroups.txt", sep="\t", header=TRUE)
## 
## infile <- read.table("evidence.txt", sep="\t", header=TRUE)
## 
## # Read in annotation including condition and biological replicates per run.
## # Users should make this annotation file. It is not the output from MaxQuant.
## annot <- read.csv("annotation.csv", header=TRUE)
## 
## raw <- MaxQtoMSstatsFormat(evidence=infile,
##                            annotation=annot,
##                            proteinGroups=proteinGroups)

## ---- eval=FALSE---------------------------------------------------------
## input <- read.csv("output_progenesis.csv", stringsAsFactors=F)
## 
## # Read in annotation including condition and biological replicates per run.
## # Users should make this annotation file. It is not the output from Progenesis.
## annot <- read.csv('annotation.csv')
## 
## raw <- ProgenesistoMSstatsFormat(input, annotation=annot)

## ---- eval=FALSE---------------------------------------------------------
## input <- read.csv("output_spectronaut.csv", stringsAsFactors=F)
## 
## quant <- SpectronauttoMSstatsFormat(input)

## ---- eval=FALSE---------------------------------------------------------
## QuantData <- dataProcess(SRMRawData)

## ---- eval=FALSE---------------------------------------------------------
## QuantData <- dataProcess(SRMRawData)
## 
## # Profile plot
## dataProcessPlots(data=QuantData, type="ProfilePlot")
## 
## # Quality control plot
## dataProcessPlots(data=QuantData, type="QCPlot")	
## 
## # Quantification plot for conditions
## dataProcessPlots(data=QuantData, type="ConditionPlot")

## ---- eval=FALSE---------------------------------------------------------
## QuantData <- dataProcess(SRMRawData)
## 
## levels(QuantData$ProcessedData$GROUP_ORIGINAL)
## comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0), nrow=1)
## row.names(comparison) <- "T7-T1"
## 
## # Tests for differentially abundant proteins with models:
## testResultOneComparison <- groupComparison(contrast.matrix=comparison, data=QuantData)

## ---- eval=FALSE---------------------------------------------------------
## QuantData <- dataProcess(SRMRawData)
## 
## # based on multiple comparisons  (T1 vs T3; T1 vs T7; T1 vs T9)
## comparison1<-matrix(c(-1,0,1,0,0,0,0,0,0,0),nrow=1)
## comparison2<-matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
## comparison3<-matrix(c(-1,0,0,0,0,0,0,0,1,0),nrow=1)
## comparison<-rbind(comparison1,comparison2, comparison3)
## row.names(comparison)<-c("T3-T1","T7-T1","T9-T1")
## 
## testResultMultiComparisons <- groupComparison(contrast.matrix=comparison, data=QuantData)
## 
## # Volcano plot
## groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="VolcanoPlot")
## 
## # Heatmap
## groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="Heatmap")
## 
## # Comparison Plot
## groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="ComparisonPlot")

## ---- eval=FALSE---------------------------------------------------------
## testResultOneComparison <- groupComparison(contrast.matrix=comparison, data=QuantData)
## 
## # normal quantile-quantile plots
## modelBasedQCPlots(data=testResultOneComparison, type="QQPlots")
## 
## # residual plots
## modelBasedQCPlots(data=testResultOneComparison, type="ResidualPlots")

## ---- eval=FALSE---------------------------------------------------------
## QuantData <- dataProcess(SRMRawData)
## head(QuantData$ProcessedData)
## 
## ## based on multiple comparisons  (T1 vs T3; T1 vs T7; T1 vs T9)
## comparison1 <- matrix(c(-1,0,1,0,0,0,0,0,0,0),nrow=1)
## comparison2 <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
## comparison3 <- matrix(c(-1,0,0,0,0,0,0,0,1,0),nrow=1)
## comparison <- rbind(comparison1,comparison2, comparison3)
## row.names(comparison) <- c("T3-T1","T7-T1","T9-T1")
## 
## testResultMultiComparisons <- groupComparison(contrast.matrix=comparison,data=QuantData)
## 
## #(1) Minimal number of biological replicates per condition
## designSampleSize(data=testResultMultiComparisons$fittedmodel, numSample=TRUE,
##   desiredFC=c(1.25,1.75), FDR=0.05, power=0.8)
## 
## #(2) Power calculation
## designSampleSize(data=testResultMultiComparisons$fittedmodel, numSample=2,
##   desiredFC=c(1.25,1.75), FDR=0.05, power=TRUE)

## ---- eval=FALSE---------------------------------------------------------
## # (1) Minimal number of biological replicates per condition
## result.sample <- designSampleSize(data=testResultMultiComparisons$fittedmodel, numSample=TRUE,
##                                 desiredFC=c(1.25,1.75), FDR=0.05, power=0.8)
## designSampleSizePlots(data=result.sample)
## 
## # (2) Power
## result.power <- designSampleSize(data=testResultMultiComparisons$fittedmodel, numSample=2,
##                                desiredFC=c(1.25,1.75), FDR=0.05, power=TRUE)
## designSampleSizePlots(data=result.power)

## ---- eval=FALSE---------------------------------------------------------
## QuantData <- dataProcess(SRMRawData)
## 
## # Sample quantification
## sampleQuant <- quantification(QuantData)
## 
## # Group quantification
## groupQuant <- quantification(QuantData, type="Group")

## ---- eval=FALSE---------------------------------------------------------
## # Consider data from a spiked-in contained in an example dataset
## head(SpikeInDataNonLinear)
## 
## nonlinear_quantlim_out <- nonlinear_quantlim(SpikeInDataNonLinear)
## 
## # Get values of LOB/LOD
## nonlinear_quantlim_out$LOB[1]
## nonlinear_quantlim_out$LOD[1]

## ---- eval=FALSE---------------------------------------------------------
## # Consider data from a spiked-in contained in an example dataset
## head(SpikeInDataLinear)
## 
## linear_quantlim_out <- linear_quantlim(SpikeInDataLinear)
## 
## # Get values of LOB/LOD
## linear_quantlim_out$LOB[1]
## linear_quantlim_out$LOD[1]

## ---- eval=FALSE---------------------------------------------------------
## # Consider data from a spiked-in contained in an example dataset
## head(SpikeInDataNonLinear)
## 
## nonlinear_quantlim_out <- nonlinear_quantlim(SpikeInDataNonLinear, alpha = 0.05)
## 
## #Get values of LOB/LOD
## nonlinear_quantlim_out$LOB[1]
## nonlinear_quantlim_out$LOD[1]
## 
## plot_quantlim(spikeindata = SpikeInDataLinear, quantlim_out  = nonlinear_quantlim_out,
## dir_output =  getwd(), alpha = 0.05)