## ----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=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)