inst/doc/ChAMP.R

In ChAMP: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC

## ----eval=FALSE---------------------------------------------------------------
#  if (!requireNamespace("BiocManager", quietly=TRUE))
#      install.packages("BiocManager")
#  BiocManager::install(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()
#  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")
#  
#  # 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/Demo450KSVD2.jpg")

## ---- 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---------------------------------------------------------------
#  myebayGSEA <- champ.ebGSEA(beta=myNorm,pheno=myLoad$pd$Sample_Group,arraytype="450K")

## ----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.