## ----options,echo=FALSE----------------------------------------------- options(width=72) ## ----lbsload,message=FALSE,cache=TRUE--------------------------------- library(dplyr) suppressWarnings(library(qPLEXanalyzer)) suppressWarnings(library(qPLEXdata)) data(human_anno) ## ----exp1_specificity,message=FALSE,cache=TRUE------------------------ ## load data data(exp1_specificity) ## create MSnSet object MSnset_data <- convertToMSnset(exp1_specificity$intensities, metadata=exp1_specificity$metadata, indExpData=c(6:15),Sequences=1,Accessions=5) ## Normalization MSnset_norm <- groupScaling(MSnset_data, median) ## Summation of peptide to protein intensity MSnset_Pnorm <- summarizeIntensities(MSnset_norm, sum, human_anno) ## Differential analysis contrasts <- c(ER_vs_IgG = "ER - IgG") diffstats <- computeDiffStats(MSnSetObj=MSnset_Pnorm, contrasts=contrasts) diffexp <- getContrastResults(diffstats=diffstats, contrast=contrasts) diffexp <- diffexp[which(diffexp$adj.P.Val < 0.01 & diffexp$log2FC >1),] ## ----exp2_Xlink,message=FALSE,cache=TRUE------------------------------ ## load data data(exp2_Xlink) ## create MSnSet object MSnset_data <- convertToMSnset(exp2_Xlink$intensities, metadata=exp2_Xlink$metadata, indExpData=c(7:16),Sequences=2,Accessions=6) exprs(MSnset_data) <- exprs(MSnset_data)+0.01 MSnset_data <- MSnset_data[,-5] ## Normalization MSnset_norm <- groupScaling(MSnset_data, median) ## Summation of peptide to protein intensity MSnset_Pnorm <- summarizeIntensities(MSnset_norm, sum, human_anno) ## Differential analysis contrasts <- c(DSG.FA_vs_FA = "DSG.FA - FA") diffstats <- computeDiffStats(MSnSetObj=MSnset_Pnorm, contrasts=contrasts) diffexp <- getContrastResults(diffstats=diffstats, contrast=contrasts, controlGroup = "IgG") diffexp <- diffexp[which(diffexp$adj.P.Val < 0.05 & abs(diffexp$log2FC) > 0.5),] ## ----exp3_OHT_ESR1,message=FALSE,cache=TRUE,fig.asp=0.7--------------- ## load data data(exp3_OHT_ESR1) ## create MSnSet object MSnset_data1 <- convertToMSnset(exp3_OHT_ESR1$intensities_qPLEX1, metadata=exp3_OHT_ESR1$metadata_qPLEX1, indExpData=c(7:16),Sequences=2,Accessions=6) pData(MSnset_data1)$Run <- 1 MSnset_data2 <- convertToMSnset(exp3_OHT_ESR1$intensities_qPLEX2, metadata=exp3_OHT_ESR1$metadata_qPLEX2, indExpData=c(7:16),Sequences=2,Accessions=6) pData(MSnset_data2)$Run <- 2 MSnset_data3 <- convertToMSnset(exp3_OHT_ESR1$intensities_qPLEX3, metadata=exp3_OHT_ESR1$metadata_qPLEX3, indExpData=c(7:16),Sequences=2,Accessions=6) pData(MSnset_data3)$Run <- 3 ## Summation of peptide to protein intensity MSnset_P1 <- summarizeIntensities(MSnset_data1, sum, human_anno) MSnset_P2 <- summarizeIntensities(MSnset_data2, sum, human_anno) MSnset_P3 <- summarizeIntensities(MSnset_data3, sum, human_anno) ## Normalization MSnset_P1 <- rowScaling(MSnset_P1,mean) MSnset_P2 <- rowScaling(MSnset_P2,mean) MSnset_P3 <- rowScaling(MSnset_P3,mean) ###### Compute common unique peptides features1 <- fData(MSnset_data1) features1 <- as.data.frame(features1[, c("Sequences", "Accessions")], stringsAsFactors = FALSE) features2 <- fData(MSnset_data2) features2 <- as.data.frame(features2[, c("Sequences", "Accessions")], stringsAsFactors = FALSE) features3 <- fData(MSnset_data3) features3 <- as.data.frame(features3[, c("Sequences", "Accessions")], stringsAsFactors = FALSE) features <- rbind(features1,features2,features3) features <- unique(features) features$Sequences <- as.character(features$Sequences) features$Accessions <- as.character(features$Accessions) counts <- features %>% count(Accessions) colnames(counts)[2] <- "Count" ##### create combine MSnSet object MSnset_P1 <- updateFvarLabels(MSnset_P1) MSnset_P2 <- updateFvarLabels(MSnset_P2) MSnset_P3 <- updateFvarLabels(MSnset_P3) MSnset_P1 <- updateSampleNames(MSnset_P1) MSnset_P2 <- updateSampleNames(MSnset_P2) MSnset_P3 <- updateSampleNames(MSnset_P3) suppressWarnings(MSnset_comb <- combine(MSnset_P1, MSnset_P2, MSnset_P3)) tokeep <- which(complete.cases(fData(MSnset_comb))==TRUE) MSnset_comb <- MSnset_comb[tokeep,] sampleNames(MSnset_comb) <- pData(MSnset_comb)$SampleName pData(MSnset_comb)$BioRep <- c(rep(1,4),rep(2,4),c(1,2),rep(3,4),rep(4,4),c(3,4), rep(5,4),rep(6,4),c(5,6)) fData(MSnset_comb) <- fData(MSnset_comb)[,c(1:4)] colnames(fData(MSnset_comb)) <- c("Accessions","Gene","Description", "GeneSymbol") ind <- match(fData(MSnset_comb)$Accessions, counts$Accessions) fData(MSnset_comb)$Count <- counts$Count[ind] ### create separate MSnSet for IgG comparision pheno <- pData(MSnset_comb) pheno$SampleGroup <- c(rep(c(rep("Exp",8),rep("IgG",2)),3)) pheno$SampleGroup <- factor(pheno$SampleGroup) MSnset_IgG <- MSnset_comb pData(MSnset_IgG) <- pheno ### Differential analysis to find ER specific interactors contrasts <- c( Exp_vs_IgG = "Exp - IgG" ) diffstats <- computeDiffStats(MSnSetObj=MSnset_IgG, contrasts=contrasts, transform = FALSE) results <- getContrastResults(diffstats=diffstats, contrast=contrasts, transform = FALSE) ### create subset of protein filtering non-specific IgG ind <- which(results$adj.P.Val < 0.01 & results$log2FC > 1) diff_IgG <- results[ind,] ind <- match(diff_IgG$Accessions, fData(MSnset_comb)$Accessions) MSnset_subset <- MSnset_comb[ind] IgG_ind <- which(pData(MSnset_subset)$SampleGroup == "IgG") ### perform regression analysis on dataset MSnset_reg <- regressIntensity(MSnset_subset, controlInd=IgG_ind, ProteinId="P03372") ### Differential analysis contrasts <- c( tam.2h_vs_vehicle = "tam.2h - vehicle", tam.6h_vs_vehicle = "tam.6h - vehicle", tam.24h_vs_vehicle = "tam.24h - vehicle" ) suppressWarnings(diffstats <- computeDiffStats(MSnSetObj=MSnset_reg, contrasts=contrasts, transform = FALSE)) diffexp1 <- getContrastResults(diffstats=diffstats, contrast=contrasts[1], transform = FALSE) diffexp1 <- diffexp1[which(diffexp1$adj.P.Val < 0.05 & abs(diffexp1$log2FC) > 0.5),] diffexp2 <- getContrastResults(diffstats=diffstats, contrast=contrasts[2], transform = FALSE) diffexp2 <- diffexp2[which(diffexp2$adj.P.Val < 0.05 & abs(diffexp2$log2FC) > 0.5),] diffexp3 <- getContrastResults(diffstats=diffstats, contrast=contrasts[3], transform = FALSE) diffexp3 <- diffexp3[which(diffexp3$adj.P.Val < 0.05 & abs(diffexp3$log2FC) > 0.5),] ## ----exp4_OHT_FP,message=FALSE,cache=TRUE----------------------------- ## load data data(exp4_OHT_FP) ## create MSnSet object MSnset_data1 <- convertToMSnset(exp4_OHT_FP$FP_1, metadata=exp4_OHT_FP$metadata_FP1, indExpData=c(7:14),Sequences=2,Accessions=6) pData(MSnset_data1)$Run <- 1 MSnset_data2 <- convertToMSnset(exp4_OHT_FP$FP_2, metadata=exp4_OHT_FP$metadata_FP2, indExpData=c(7:14),Sequences=2,Accessions=6) pData(MSnset_data2)$Run <- 2 ## Summation of peptide to protein intensity MSnset_P1 <- summarizeIntensities(MSnset_data1, sum, human_anno) MSnset_P2 <- summarizeIntensities(MSnset_data2, sum, human_anno) ### Computing common unique peptides features1 <- fData(MSnset_data1) features1 <- as.data.frame(features1[, c("Sequences","Accessions")], stringsAsFactors = FALSE) features2 <- fData(MSnset_data2) features2 <- as.data.frame(features2[, c("Sequences","Accessions")], stringsAsFactors = FALSE) features <- rbind(features1,features2) features <- unique(features) features$Sequences <- as.character(features$Sequences) features$Accessions <- as.character(features$Accessions) counts <- features %>% count(Accessions) colnames(counts)[2] <- "Count" ##### create combine MSnSet object MSnset_P1 <- updateFvarLabels(MSnset_P1) MSnset_P2 <- updateFvarLabels(MSnset_P2) MSnset_P1 <- updateSampleNames(MSnset_P1) MSnset_P2 <- updateSampleNames(MSnset_P2) suppressWarnings(MSnset_comb <- combine(MSnset_P1, MSnset_P2)) tokeep <- which(complete.cases(fData(MSnset_comb))==TRUE) MSnset_comb <- MSnset_comb[tokeep,] sampleNames(MSnset_comb) <- pData(MSnset_comb)$SampleName fData(MSnset_comb) <- fData(MSnset_comb)[,c(1:4)] colnames(fData(MSnset_comb)) <- c("Accessions","Gene","Description", "GeneSymbol") ind <- match(fData(MSnset_comb)$Accessions,counts$Accessions) fData(MSnset_comb)$Count <- counts$Count[ind] ## Normalization MSnset_Pnorm <- normalizeScaling(MSnset_comb, median) ## Differential analysis contrasts <- c( tam.2h_vs_vehicle = "tam.2h - vehicle", tam.6h_vs_vehicle = "tam.6h - vehicle", tam.24h_vs_vehicle = "tam.24h - vehicle" ) batchEffect <- c("Run", "BioRep") diffstats <- computeDiffStats(MSnset_Pnorm, contrasts=contrasts, batchEffect=batchEffect) diffexp1 <- getContrastResults(diffstats=diffstats, contrast=contrasts[1]) diffexp1 <- diffexp1[which(diffexp1$adj.P.Val < 0.05 & abs(diffexp1$log2FC) > 0.5),] diffexp2 <- getContrastResults(diffstats=diffstats, contrast=contrasts[2]) diffexp2 <- diffexp2[which(diffexp2$adj.P.Val < 0.05 & abs(diffexp2$log2FC) > 0.5),] diffexp3 <- getContrastResults(diffstats=diffstats, contrast=contrasts[3]) diffexp3 <- diffexp3[which(diffexp3$adj.P.Val < 0.05 & abs(diffexp3$log2FC) > 0.5),] ## ----exp5_PDX,message=FALSE,cache=TRUE-------------------------------- ## load data data(exp5_PDX) ## create MSnSet object MSnset_data <- convertToMSnset(exp5_PDX$intensities, metadata=exp5_PDX$metadata, indExpData=c(7:16), Sequences=2,Accessions=6) ## Exclude outlier and techical replicate samples MSnset_data <- MSnset_data[,-c(7:10)] ## Normalization MSnset_norm <- groupScaling(MSnset_data, median) ## Summation of peptide to protein intensity MSnset_Pnorm <- summarizeIntensities(MSnset_norm, sum, human_anno) ## Differential analysis contrasts <- c(PDX_vs_IgG = "PDX - IgG") diffstats <- computeDiffStats(MSnset_Pnorm, contrasts=contrasts) diffexp <- getContrastResults(diffstats=diffstats, contrast=contrasts) diffexp <- diffexp[which(diffexp$adj.P.Val < 0.05 & diffexp$log2FC > 1),] ## ----exp6_ER,message=FALSE,cache=TRUE--------------------------------- ## load data data(exp6_ER) ## create MSnSet object MSnset_data <- convertToMSnset(exp6_ER$intensities, metadata=exp6_ER$metadata, indExpData=c(6:15), Sequences=2, Accessions=5, rmMissing=FALSE) exprs(MSnset_data)[is.na(exprs(MSnset_data))] <- 0 exprs(MSnset_data) <- exprs(MSnset_data)+0.01 ## Normalization MSnset_norm <- groupScaling(MSnset_data, median, groupingColumn="SampleGroup") ## Summation of peptide to protein intensity MSnset_Pnorm <- summarizeIntensities(MSnset_norm, sum, human_anno) ## Differential analysis contrasts <- c(ER_vs_IgG = "ER - IgG") diffstats <- computeDiffStats(MSnset_Pnorm, contrasts=contrasts) diffexp <- getContrastResults(diffstats=diffstats, contrast=contrasts) diffexp <- diffexp[which(diffexp$adj.P.Val < 0.01 & diffexp$log2FC >1),] ## ----exp7_NCOA3,message=FALSE,cache=TRUE------------------------------ ## load data data(exp7_NCOA3) ## create MSnSet object MSnset_data <- convertToMSnset(exp7_NCOA3$intensities, metadata=exp7_NCOA3$metadata, indExpData=c(7:16), Sequences=2, Accessions=6, rmMissing=FALSE) exprs(MSnset_data)[is.na(exprs(MSnset_data))] <- 0 exprs(MSnset_data) <- exprs(MSnset_data)+0.01 ## Normalization MSnset_norm <- groupScaling(MSnset_data, median, groupingColumn="SampleGroup") ## Summation of peptide to protein intensity MSnset_Pnorm <- summarizeIntensities(MSnset_norm, sum, human_anno) ## Differential analysis contrasts <- c(NCOA3_vs_IgG = "NCOA3 - IgG") diffstats <- computeDiffStats(MSnset_Pnorm, contrasts=contrasts) diffexp <- getContrastResults(diffstats=diffstats, contrast=contrasts) diffexp <- diffexp[which(diffexp$adj.P.Val < 0.01 & diffexp$log2FC >1),] ## ----exp8_CBP,message=FALSE,cache=TRUE-------------------------------- ## load data data(exp8_CBP) ## create MSnSet object MSnset_data <- convertToMSnset(exp8_CBP$intensities, metadata=exp8_CBP$metadata, indExpData=c(7:16), Sequences=2, Accessions=6, rmMissing=FALSE) exprs(MSnset_data)[is.na(exprs(MSnset_data))] <- 0 exprs(MSnset_data) <- exprs(MSnset_data)+0.01 ## Normalization MSnset_norm <- groupScaling(MSnset_data, median, groupingColumn="SampleGroup") ## Summation of peptide to protein intensity MSnset_Pnorm <- summarizeIntensities(MSnset_norm, sum, human_anno) ## Differential analysis contrasts <- c(CREBBP_vs_IgG = "CREBBP - IgG") diffstats <- computeDiffStats(MSnset_Pnorm, contrasts=contrasts) diffexp <- getContrastResults(diffstats=diffstats, contrast=contrasts) diffexp <- diffexp[which(diffexp$adj.P.Val < 0.01 & diffexp$log2FC >1),] ## ----exp9_PolII,message=FALSE,cache=TRUE------------------------------ ## load data data(exp9_PolII) ## create MSnSet object MSnset_data <- convertToMSnset(exp9_PolII$intensities, metadata=exp9_PolII$metadata, indExpData=c(7:16), Sequences=2, Accessions=6, rmMissing=FALSE) exprs(MSnset_data)[is.na(exprs(MSnset_data))] <- 0 exprs(MSnset_data) <- exprs(MSnset_data)+0.01 ## Normalization MSnset_norm <- groupScaling(MSnset_data, median, groupingColumn="SampleGroup") ## Summation of peptide to protein intensity MSnset_Pnorm <- summarizeIntensities(MSnset_norm, sum, human_anno) ## Differential analysis contrasts <- c(POLR2A_vs_IgG = "POLR2A - IgG") diffstats <- computeDiffStats(MSnset_Pnorm, contrasts=contrasts) diffexp <- getContrastResults(diffstats=diffstats, contrast=contrasts) diffexp <- diffexp[which(diffexp$adj.P.Val < 0.01 & diffexp$log2FC >1),] ## ----info,echo=TRUE--------------------------------------------------- sessionInfo()