## ----install,eval = FALSE-------------------------------------------------- # if (!requireNamespace("BiocManager")) # install.packages("BiocManager") # BiocManager::install("SubCellBarCode") ## ----Loadpackage----------------------------------------------------------- library(SubCellBarCode) ## ----exampleData----------------------------------------------------------- head(hcc827Ctrl) ## ----markerdata------------------------------------------------------------ head(markerProteins) ## ----loadData-------------------------------------------------------------- df <- loadData(protein.data = hcc827Ctrl) ## ----printDimData---------------------------------------------------------- print(dim(df)) head(df) ## ----subset data----------------------------------------------------------- set.seed(2) df <- df[sample(nrow(df), 6000),] ## ----coverageMarkers, fig = TRUE------------------------------------------- c.prots <- calculateCoveredProtein(proteinIDs = rownames(df), markerproteins = markerProteins[,1]) ## ----markerQC-------------------------------------------------------------- r.markers <- markerQualityControl(coveredProteins = c.prots, protein.data = df) r.markers[1:5] ## ----finalCoverage, fig = TRUE--------------------------------------------- ## uncomment the function when running # f.prots <- calculateCoveredProtein(r.markers, markerProteins[,1]) ## ----tsneparameter--------------------------------------------------------- #Default parameters #Run the broad-range parameters to optimize well !!! #Output dimensionality #dims = 3 #Speed/accuracy trade-off (increase for less accuracy) #theta = c(0.1, 0.2, 0.3, 0.4, 0.5) #Perplexity parameter #perplexity = c(5, 10, 20, 30, 40, 50, 60) ## ----tsnedim3, fig = TRUE, fig.width = 6.5, fig.height = 6.5--------------- set.seed(6) tsne.map <- tsneVisualization(protein.data = df, markerProteins = r.markers, dims = 3, theta = c(0.1), perplexity = c(60)) ## ----tsnedim2, fig = TRUE-------------------------------------------------- set.seed(9) tsne.map2 <- tsneVisualization(protein.data = df, markerProteins = r.markers, dims = 2, theta = c(0.5), perplexity = c(60)) ## ----buildSVM-------------------------------------------------------------- set.seed(4) cls <- svmClassification(markerProteins = r.markers, protein.data = df, markerprot.df = markerProteins) ## ----testdata-------------------------------------------------------------- # testing data predictions for replicate A and B test.A <- cls[[1]]$svm.test.prob.out test.B <- cls[[2]]$svm.test.prob.out head(test.A) ## ----allPred--------------------------------------------------------------- # all predictions for replicate A and B all.A <- cls[[1]]$all.prot.pred all.B <- cls[[2]]$all.prot.pred ## ----compartmentThreshold-------------------------------------------------- t.c.df <- computeThresholdCompartment(test.repA = test.A, test.repB = test.B) ## ----headcompartmentThreshold---------------------------------------------- head(t.c.df) ## ----applycompartmentThreshold--------------------------------------------- c.cls.df <- applyThresholdCompartment(all.repA = all.A, all.repB = all.B, threshold.df = t.c.df) ## ----headcompartmentCls---------------------------------------------------- head(c.cls.df) ## ----neighborhoodThreshold------------------------------------------------- t.n.df <- computeThresholdNeighborhood(test.repA = test.A, test.repB = test.B) ## ----headneighborhoodThreshold--------------------------------------------- head(t.n.df) ## ----applyNeighborhoodThreshold-------------------------------------------- n.cls.df <- applyThresholdNeighborhood(all.repA = all.A, all.repB = all.B, threshold.df = t.n.df) ## ----headNeighborhoodCls--------------------------------------------------- head(n.cls.df) ## ----mergecls-------------------------------------------------------------- cls.df <- mergeCls(compartmentCls = c.cls.df, neighborhoodCls = n.cls.df) ## ----headmerge------------------------------------------------------------- head(cls.df) ## ----hcc827psmcount-------------------------------------------------------- head(hcc827CtrlPSMCount) ## ----plotbarcode, fig = TRUE, fig.width = 6, fig.height = 6---------------- plotBarcode(sampleClassification = cls.df, protein = "NLRP4", s1PSM = hcc827CtrlPSMCount) ## ----multipleprots, fig = TRUE, fig.width= 10, fig.height = 8-------------- # 26S proteasome complex (26s proteasome regulatory complex) proteasome26s <- c("PSMA7", "PSMC3","PSMA4", "PSMB4", "PSMB6", "PSMB5", "PSMC2","PSMC4", "PSMB3", "PSMA6","PSMC5","PSMC6") plotMultipleProtein(sampleClassification = cls.df, proteinList = proteasome26s) ## ----headHCC827GEFCls------------------------------------------------------ head(hcc827GEFClass) ## ----sankey, fig.width = 6, fig.height = 3--------------------------------- sankeyPlot(sampleCls1 = cls.df, sampleCls2 = hcc827GEFClass) ## ----headPSMCount---------------------------------------------------------- head(hcc827CtrlPSMCount) ## ----relocation parameters------------------------------------------------- ##parameters #sampleCls1 = sample 1 classification output #s1PSM = sample 2 PSM count #s1Quant = Sample 1 Quantification data #sampleCls2 = sample 2 classification output #s2PSM = sample 2 classification output #sample2Quant = Sample 2 Quantification data #min.psm = minumum psm count #pearson.cor = perason correlation coefficient ## ----strongCandidates, fig = TRUE------------------------------------------ candidate.df <- candidateRelocatedProteins(sampleCls1 = cls.df, s1PSM = hcc827CtrlPSMCount, s1Quant = hcc827Ctrl, sampleCls2 = hcc827GEFClass, s2PSM = hcc827GefPSMCount, s2Quant = hcc827GEF, min.psm = 2, pearson.cor = 0.8) ## ----printdim-------------------------------------------------------------- print(dim(candidate.df)) ## ----printhead------------------------------------------------------------- head(candidate.df) ## ----strongCandidatesLabel, fig = TRUE------------------------------------- candidate2.df <- candidateRelocatedProteins(sampleCls1 = cls.df, s1PSM = hcc827CtrlPSMCount, s1Quant = hcc827Ctrl, sampleCls2 = hcc827GEFClass, s2PSM = hcc827GefPSMCount, s2Quant = hcc827GEF, annotation = TRUE, min.psm = 9, pearson.cor = 0.05) ## -------------------------------------------------------------------------- sessionInfo()