## ----style, echo = FALSE, results = 'asis'------------------------------------ BiocStyle::markdown() ## ----global_options, include=FALSE-------------------------------------------------------------------------- knitr::opts_chunk$set(fig.width=10, fig.height=7, warning=FALSE, message=FALSE) options(width=110) ## ----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=FALSE) # # # 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=FALSE) # # 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")