vignettes/SubCellBarCode.R

In TanerArslan/SubCellBarCode-R-Package: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome

## ----installPackage, eval=FALSE--------------------------------------------
#  install.packages("devtools")
#  library(devtools)
#  install_github("TanerArslan/SubCellBarCode-R-Package")

## ----Loadpackage-----------------------------------------------------------
library(SubCellBarCode)

## ----exampleData-----------------------------------------------------------
head(hcc827Ctrl)

## ----markerdata------------------------------------------------------------
head(markerProteins)

## ----loadData--------------------------------------------------------------
df <- loadData(protein.data = hcc827Ctrl)

## ----printDimData----------------------------------------------------------
print(dim(df))
head(df)

## ----coverageMarkers-------------------------------------------------------
c.prots <- calculateCoveredProtein(proteinIDs = rownames(df), 
                        markerproteins = markerProteins[,1]) 

## ----markerQC--------------------------------------------------------------
r.markers <- markerQualityControl(coveredProteins = c.prots, protein.data = df)
r.markers[1:5]

## ----finalCoverage---------------------------------------------------------
# uncomment the function when running 
# f.prots <- calculateCoveredProtein(r.markers, markerProteins[,1])

## ----tsneparameter---------------------------------------------------------
#Default parameters
#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.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, 0.2, 0.3, 0.4, 0.5), 
                    perplexity = c(5, 10, 20, 30, 40, 50, 60)) 

## ----tsnedim2--------------------------------------------------------------
set.seed(9)
tsne.map2 <- tsneVisualization(protein.data = df, 
                    markerProteins = r.markers, 
                    dims = 2, 
                    theta = c(0.1, 0.2, 0.3, 0.4, 0.5), 
                    perplexity = c(5, 10, 20, 30, 40, 50, 60))

## ----buildSVM--------------------------------------------------------------
set.seed(2)
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.width = 6, fig.height = 6----------------------------
plotBarcode(sampleClassification = cls.df, protein = "TP53",
            s1PSM = hcc827CtrlPSMCount)

## ----multipleprots, fig.width= 10, fig.height = 8--------------------------
# 26S proteasome complex (26s proteasome regulatory complex)
proteasome26s <- c("PSMA7", "PSMC3", "PSMB1", "PSMA1", "PSMA3",
"PSMA4", "PSMA5", "PSMB4", "PSMB6", "PSMB5", "PSMC2","PSMC4","PSMB3", 
"PSMB2", "PSMD4","PSMA6","PSMC1","PSMC5","PSMC6","PSMB7","PSMD13")

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           

## ----strongCandidates------------------------------------------------------
candidate.df <- candidateRelocatedProteins(sampleCls1 = cls.df, 
                                s1PSM = hcc827CtrlPSMCount, 
                                s1Quant = hcc827Ctrl,
                                sampleCls2 = hcc827GEFClass,
                                s2PSM = hcc827GefPSMCount,
                                s2Quant = hcc827GEF)

## ----printdim--------------------------------------------------------------
print(dim(candidate.df))

## ----printhead-------------------------------------------------------------
head(candidate.df)

## ----strongCandidatesLabel-------------------------------------------------
candidate2.df <- candidateRelocatedProteins(sampleCls1 = cls.df,
                                s1PSM = hcc827CtrlPSMCount, 
                                s1Quant = hcc827Ctrl, 
                                sampleCls2 = hcc827GEFClass, 
                                s2PSM = hcc827GefPSMCount, 
                                s2Quant = hcc827GEF, 
                                annotation = TRUE, 
                                min.psm = 10, 
                                pearson.cor = 0.1) 

## --------------------------------------------------------------------------
sessionInfo()