## ----eval=FALSE---------------------------------------------------------- # source("http://bioconductor.org/biocLite.R") # biocLite(c("minfi","ChAMPdata","Illumina450ProbeVariants.db","sva","IlluminaHumanMethylation450kmanifest","limma","RPMM","DNAcopy","preprocessCore","impute","marray","wateRmelon","goseq","plyr","GenomicRanges","RefFreeEWAS","qvalue","isva","doParallel","bumphunter","quadprog","shiny","shinythemes","plotly","RColorBrewer","DMRcate","dendextend","IlluminaHumanMethylationEPICmanifest","FEM","matrixStats","missMethyl","combinat")) ## ----eval=TRUE,message=FALSE, warning=FALSE------------------------------ library("ChAMP") ## ----eval=FALSE---------------------------------------------------------- # testDir=system.file("extdata",package="ChAMPdata") # myLoad <- champ.load(testDir,arraytype="450K") ## ----eval=FALSE---------------------------------------------------------- # data(EPICSimData) ## ---- out.width = 800, fig.retina = NULL,echo=F-------------------------- knitr::include_graphics("Figure/ChAMP_Pipeline.png") ## ----eval=FALSE---------------------------------------------------------- # champ.process(directory = testDir) ## ----eval=FALSE---------------------------------------------------------- # myLoad <- cham.load(testDir) # # Or you may separate about code as champ.import(testDir) + champ.filter() # CpG.GUI() # champ.QC() # Alternatively: QC.GUI() # myNorm <- champ.norm() # champ.SVD() # # If Batch detected, run champ.runCombat() here. # myDMP <- champ.DMP() # DMP.GUI() # myDMR <- champ.DMR() # DMR.GUI() # myBlock <- champ.Block() # Block.GUI() # myGSEA <- champ.GSEA() # myEpiMod <- champ.EpiMod() # myCNA <- champ.CNA() # # # If DataSet is Blood samples, run champ.refbase() here. # myRefbase <- champ.refbase() ## ----eval=FALSE---------------------------------------------------------- # # myLoad <- champ.load(directory = testDir,arraytype="EPIC") # # We simulated EPIC data from beta value instead of .idat file, # # but user may use above code to read .idat files directly. # # Here we we started with myLoad. # # data(EPICSimData) # CpG.GUI(arraytype="EPIC") # champ.QC() # Alternatively QC.GUI(arraytype="EPIC") # myNorm <- champ.norm(arraytype="EPIC") # champ.SVD() # # If Batch detected, run champ.runCombat() here.This data is not suitable. # myDMP <- champ.DMP(arraytype="EPIC") # DMP.GUI() # myDMR <- champ.DMR() # DMR.GUI() # myDMR <- champ.DMR(arraytype="EPIC") # DMR.GUI(arraytype="EPIC") # myBlock <- champ.Block(arraytype="EPIC") # Block.GUI(arraytype="EPIC") # For this simulation data, not Differential Methylation Block is detected. # myGSEA <- champ.GSEA(arraytype="EPIC") # myEpiMod <- champ.EpiMod(arraytype="EPIC") # # # champ.CNA(arraytype="EPIC") # # champ.CNA() function call for intensity data, which is not included in our Simulation data. ## ----eval=FALSE---------------------------------------------------------- # library("doParallel") # detectCores() ## ----eval=FALSE---------------------------------------------------------- # myLoad <- champ.load(testDir) ## ----eval=TRUE----------------------------------------------------------- data(testDataSet) ## ----eval=TRUE----------------------------------------------------------- myLoad$pd ## ----eval=FALSE---------------------------------------------------------- # myImport <- champ.import(testDir) # myLoad <- champ.filter() ## ----eval=FALSE---------------------------------------------------------- # CpG.GUI(CpG=rownames(myLoad$beta),arraytype="450K") ## ---- out.width = 800, fig.retina = NULL,echo=F-------------------------- knitr::include_graphics("Figure/CpGGUI.png") ## ----eval=TRUE,dpi=100,fig.width=7,fig.height=4,message=FALSE------------ champ.QC() ## ----eval=FALSE---------------------------------------------------------- # QC.GUI(beta=myLoad$beta,arraytype="450K") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/QCGUI.jpg") ## ----eval=FALSE---------------------------------------------------------- # myNorm <- champ.norm(beta=myLoad$beta,arraytype="450K",cores=5) ## ----eval=FALSE---------------------------------------------------------- # champ.SVD(beta=myNorm,pd=myLoad$pd) ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/Demo450KSVD.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/HannumSVD.png") ## ----eval=FALSE---------------------------------------------------------- # myCombat <- champ.runCombat(beta=myNorm,pd=myLoad$pd,batchname=c("Slide")) ## ----eval=TRUE,warning=FALSE,message=FALSE------------------------------- myDMP <- champ.DMP(beta = myNorm,pheno=myLoad$pd$Sample_Group) ## ----eval=FALSE---------------------------------------------------------- # head(myDMP[[1]]) ## ----eval=FALSE---------------------------------------------------------- # DMP.GUI(DMP=myDMP[[1]],beta=myNorm,pheno=myLoad$pd$Sample_Group) # # myDMP is a list now, each data frame is stored as myDMP[[1]], myDMP[[2]], myDMP[[3]]... ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/DMP-1.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/DMP-2.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/DMP-3.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/DMP-4.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/HannumDMPGUIplot.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/HannumDMPGUIplot-2.png") ## ----eval=FALSE---------------------------------------------------------- # myDMP <- champ.DMP(beta=myNorm, pheno=myLoad$pd$Sample_Group, compare.group=c("oxBS", "BS")) # # In above code, you can set compare.group() as "oxBS" and "BS" to do DMP detection between hydroxymethylatio and normal methylation. # # hmc <- myDMP[[1]][myDMP[[1]]$deltaBeta>0,] # # Then you can use above code to extract hydroxymethylation CpGs. ## ----eval=FALSE,message=FALSE,warning=TRUE------------------------------- # myDMR <- champ.DMR(beta=myNorm,pheno=myLoad$pd$Sample_Group,method="Bumphunter") ## ----eval=TRUE----------------------------------------------------------- head(myDMR$DMRcateDMR) ## ----eval=FALSE---------------------------------------------------------- # DMR.GUI(DMR=myDMR) # # It might be a little bit slow to open DMR.GUI() because function need to extract annotation for CpGs from DMR. Might take 30 seconds. ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/DMR-1.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/DMR-2.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/DMR-3.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/DMR-4.png") ## ----eval=FALSE---------------------------------------------------------- # myBlock <- champ.Block(beta=myNorm,pheno=myLoad$pd$Sample_Group,arraytype="450K") ## ----eval=TRUE----------------------------------------------------------- head(myBlock$Block) ## ----eval=FALSE---------------------------------------------------------- # Block.GUI(Block=myBlock,beta=myNorm,pheno=myLoad$pd$Sample_Group,runDMP=TRUE,compare.group=NULL,arraytype="450K") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/Block-1.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/Block-2.png") ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/Block-3.png") ## ----eval=FALSE---------------------------------------------------------- # myGSEA <- champ.GSEA(beta=myNorm,DMP=myDMP[[1]], DMR=myDMR, arraytype="450K",adjPval=0.05, method="fisher") # # myDMP and myDMR could (not must) be used directly. ## ----eval=TRUE----------------------------------------------------------- head(myGSEA$DMP) # Above is the GSEA result for differential methylation probes. head(myGSEA$DMR) # Above is the GSEA result for differential methylation regions. # Too many information may be printed, so we are not going to show the result here. ## ----eval=FALSE---------------------------------------------------------- # myEpiMod <- champ.EpiMod(beta=myNorm,pheno=myLoad$pd$Sample_Group) ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/EpiMod.jpg") ## ----eval=FALSE---------------------------------------------------------- # myCNA <- champ.CNA(intensity=myLoad$intensity,pheno=myLoad$pd$Sample_Group) ## ---- out.width = 800, fig.retina = NULL,echo=FALSE---------------------- knitr::include_graphics("Figure/CNAGroupPlot.jpg") ## ----eval=FALSE---------------------------------------------------------- # myRefBase <- champ.refbase(beta=myNorm,arraytype="450K") # # Our test data set is not whole blood. So it should not be run here.