Nothing
R/champ.load.R
In ChAMP: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC
Defines functions
champ.load
Documented in
champ.load
if(getRversion() >= "3.1.0") utils::globalVariables(c("sampleNames<-","EPIC.manifest.hg19","EPIC.manifest.pop.hg19","hm450.manifest.hg19","hm450.manifest.pop.hg19","multi.hit","probe.features","probe.features.epic"))
champ.load <- function(directory = getwd(),
method = "ChAMP",
methValue="B",
autoimpute=TRUE,
filterDetP=TRUE,
ProbeCutoff=0,
SampleCutoff=0.1,
detPcut=0.01,
filterBeads=TRUE,
beadCutoff=0.05,
filterNoCG=TRUE,
filterSNPs=TRUE,
population=NULL,
filterMultiHit=TRUE,
filterXY=TRUE,
force=FALSE,
arraytype="450K")
{
message("[===========================]")
message("[<<<< ChAMP.LOAD START >>>>>]")
message("-----------------------------")
mybeadcount <- function(x)
{
#select out bead count dataframe
getNBeads(x) -> nb
#match rownames of beadcount dataframe to addresses
getProbeInfo(x,type="I")->typeIadd
match(typeIadd$AddressA,rownames(nb))->typeImatchA
match(typeIadd$AddressB,rownames(nb))->typeImatchB
#match rownames of beadcount dataframe to addresses
getProbeInfo(x,type="II")->typeIIadd
match(typeIIadd$Address,rownames(nb))->typeIImatch
nb->nbcg
locusNames <- getManifestInfo(x, "locusNames")
bc_temp <- matrix(NA_real_, ncol = ncol(x), nrow = length(locusNames),
dimnames = list(locusNames, sampleNames(x)))
TypeII.Name <- getProbeInfo(x, type = "II")$Name
bc_temp[TypeII.Name, ] <- nbcg[getProbeInfo(x, type = "II")$AddressA,]
TypeI <- getProbeInfo(x, type = "I")
bc_temp->bcB
bc_temp->bcA
bcB[TypeI$Name, ] <- nbcg[TypeI$AddressB,]
bcA[TypeI$Name, ] <- nbcg[TypeI$AddressA,]
which(bcB<3) -> bcB3
which(bcA<3) -> bcA3
bcA->bcA2
bcB->bcB2
bcA2[bcA3]<-NA
bcA2[bcB3]<-NA
data.frame(bcA2)->bc
bc
}
if(method=="minfi")
{
message("\n[ Loading Data with Minfi Method ]")
message("----------------------------------")
message("Loading data from ", directory)
myDir <- directory
suppressWarnings(targets <- read.metharray.sheet(myDir))
rgSet <- read.metharray.exp(targets = targets,extended=TRUE,force=force)
if(arraytype=="EPIC") rgSet@annotation <- c(array="IlluminaHumanMethylationEPIC",annotation="ilm10b4.hg19")
sampleNames(rgSet)=rgSet[[1]]
pd <- pData(rgSet)
mset <- preprocessRaw(rgSet)
detP <- detectionP(rgSet)
message("<< Read DataSet Success. >>\n")
if(methValue=="B") tmp = getBeta(mset, "Illumina") else tmp = getM(mset)
tmp[detP >= detPcut] <- NA
message("The fraction of failed positions per sample\n
(You may need to delete samples with high proportion of failed probes\n): ")
numfail <- matrix(colMeans(is.na(tmp)))
rownames(numfail) <- colnames(detP)
colnames(numfail) <- "Failed CpG Fraction."
print(numfail)
RemainSample <- which(numfail < SampleCutoff)
if(any(numfail >= SampleCutoff))
message("The detSamplecut parameter is : ",SampleCutoff, "\nSamples : ",
paste(rownames(numfail)[which(numfail >= SampleCutoff)],collapse=",")," will be deleted.\n",
"There are ",length(RemainSample)," samples left for analysis.\n")
rgSet <- rgSet[,RemainSample]
detP <- detP[,RemainSample]
mset <- mset[,RemainSample]
pd <- pd[RemainSample,]
tmp <- tmp[,RemainSample]
if(filterDetP)
{
mset.f = mset[rowSums(is.na(tmp)) <= ProbeCutoff*ncol(detP),]
if(ProbeCutoff==0)
{
message("Filtering probes with a detection p-value above ",detPcut," in one or more samples has removed ",dim(mset)[1]-dim(mset.f)[1]," probes from the analysis. If a large number of probes have been removed, ChAMP suggests you to identify potentially bad samples.")
}else{
message("Filtering probes with a detection p-value above ",detPcut," in at least ",ProbeCutoff*100,"% of samples has removed ",dim(mset)[1]-dim(mset.f)[1]," probes from the analysis. If a large number of probes have been removed, ChAMP suggests you look at the failedSample file to identify potentially bad samples.")
}
mset=mset.f
tmp <- tmp[rowSums(is.na(tmp)) <= ProbeCutoff*ncol(detP),]
message("<< Filter DetP Done. >>\n")
}
if(sum(is.na(tmp))==0){
message("\nThere is no NA values in your matrix, there is no need to imputation.\n")
}else
{
message("\nThere are ",sum(is.na(tmp))," NA remain in filtered Data Set. Impute can be done for remain NAs, but not suitable for small number samples. For small Data Set (like only 20 samples), we suggest you set parameter ProbeCutoff as 0 in champ.load() here, which would remove all NA involved probe no matter how many samples of those probes are NA.\n")
}
if(autoimpute & sum(is.na(tmp)) > 0){
message("Impute will be conducted here for remain ",sum(is.na(tmp))," NAs. Note that if you don't do this, NA values would be kept in your data set. You may use champ.impute() function to do more complex imputation as well.")
# Open a file to send messages to save lot's of information from impute.knn
message("\nImpute function is working now, it may need couple minutes...")
zz <- file("ImputeMessage.Rout", open="wt")
sink(zz)
sink(zz, type="message")
tmp <- impute.knn(tmp,k=5)$data
sink(type="message")
sink()
message("<< Imputation Done. >>\n")
}
if(filterBeads)
{
bc=mybeadcount(rgSet)
bc2=bc[rowSums(is.na(bc)) < beadCutoff*(ncol(bc)),]
mset.f2 = mset[featureNames(mset) %in% row.names(bc2),]
tmp <- tmp[rownames(tmp) %in% row.names(bc2),]
message("Filtering probes with a beadcount <3 in at least ",beadCutoff*100,"% of samples, has removed ",dim(mset)[1]-dim(mset.f2)[1]," from the analysis.")
mset=mset.f2
message("<< Filter Beads Done. >>\n")
}
if(filterNoCG)
{
mset.f2=dropMethylationLoci(mset,dropCH=T)
tmp <- tmp[rownames(tmp) %in% featureNames(mset.f2),]
message("Filtering non-cg probes, has removed ",dim(mset)[1]-dim(mset.f2)[1]," from the analysis.")
mset <- mset.f2
message("<< Filter NoCG Done. >>\n")
}
if(filterSNPs)
{
if(arraytype=="450K")
{
if(is.null(population))
{
message("Using general 450K SNP list for filtering.")
data(hm450.manifest.hg19)
maskname <- rownames(hm450.manifest.hg19)[which(hm450.manifest.hg19$MASK_general==TRUE)]
}else if(!population %in% c("AFR","EAS","EUR","SAS","AMR","GWD","YRI","TSI",
"IBS","CHS","PUR","JPT","GIH","CHB","STU","ITU",
"LWK","KHV","FIN","ESN","CEU","PJL","ACB","CLM",
"CDX","GBR","BEB","PEL","MSL","MXL","ASW"))
{
message("Using general 450K SNP list for filtering.")
data(hm450.manifest.hg19)
maskname <- rownames(hm450.manifest.hg19)[which(hm450.manifest.hg19$MASK_general==TRUE)]
}else
{
message("Using ",population," specific 450K SNP list for filtering.")
data(hm450.manifest.pop.hg19)
maskname <- rownames(hm450.manifest.pop.hg19)[which(hm450.manifest.pop.hg19[,paste("MASK_general_",population,sep="")]==TRUE)]
}
}else
{
if(is.null(population))
{
message("Using general EPIC SNP list for filtering.")
data(EPIC.manifest.hg19)
maskname <- rownames(EPIC.manifest.hg19)[which(EPIC.manifest.hg19$MASK_general==TRUE)]
}else if(!population %in% c("AFR","EAS","EUR","SAS","AMR","GWD","YRI","TSI",
"IBS","CHS","PUR","JPT","GIH","CHB","STU","ITU",
"LWK","KHV","FIN","ESN","CEU","PJL","ACB","CLM",
"CDX","GBR","BEB","PEL","MSL","MXL","ASW"))
{
message("Using general EPIC SNP list for filtering.")
data(EPIC.manifest.hg19)
maskname <- rownames(EPIC.manifest.hg19)[which(EPIC.manifest.hg19$MASK_general==TRUE)]
}else
{
message("Using ",population," specific EPIC SNP list for filtering.")
data(EPIC.manifest.pop.hg19)
maskname <- rownames(EPIC.manifest.pop.hg19)[which(EPIC.manifest.pop.hg19[,paste("MASK_general_",population,sep="")]==TRUE)]
}
}
mset.f2=mset[!featureNames(mset) %in% maskname,]
tmp <- tmp[!rownames(tmp) %in% maskname,]
message("Filtering probes with SNPs as identified in Zhou's Nucleic Acids Research Paper, 2016, has removed ",dim(mset)[1]-dim(mset.f2)[1]," from the analysis.")
mset=mset.f2
message("<< Filter SNP Done. >>\n")
}
if(filterMultiHit)
{
data(multi.hit)
mset.f2=mset[!featureNames(mset) %in% multi.hit$TargetID,]
tmp <- tmp[!rownames(tmp) %in% multi.hit$TargetID,]
message("Filtering probes that align to multiple locations as identified in Nordlund et al, has removed ",dim(mset)[1]-dim(mset.f2)[1]," from the analysis.")
mset=mset.f2
message("<< Filter MultiHit Done. >>\n")
}
if(filterXY)
{
if(arraytype=="EPIC") data(probe.features.epic) else data(probe.features)
autosomes=probe.features[!probe.features$CHR %in% c("X","Y"), ]
mset.f2=mset[featureNames(mset) %in% row.names(autosomes),]
tmp <- tmp[rownames(tmp) %in% row.names(autosomes),]
message("Filtering probes on the X or Y chromosome has removed ",dim(mset)[1]-dim(mset.f2)[1]," from the analysis.")
mset=mset.f2
message("<< Filter XY chromosome Done. >>\n")
}
message(paste(if(methValue=="B") "[Beta" else "[M","value is selected as output.]\n"))
beta.raw <- tmp
intensity <- minfi::getMeth(mset) + minfi::getUnmeth(mset)
detP <- detP[which(row.names(detP) %in% row.names(beta.raw)),]
if(min(beta.raw, na.rm=TRUE)<=0) beta.raw[beta.raw<=0] <- min(beta.raw[beta.raw > 0])
message("Zeros in your dataset have been replaced with smallest positive value.\n")
if(max(beta.raw, na.rm=TRUE)>=0) beta.raw[beta.raw>=1] <- max(beta.raw[beta.raw < 1])
message("One in your dataset have been replaced with largest value below 1.\n")
message("The analysis will be proceed with ", dim(beta.raw)[1], " probes and ",dim(beta.raw)[2], " samples.\n")
message("Current Data Set contains ",sum(is.na(beta.raw))," NA in ", if(methValue=="B") "[Beta]" else "[M]"," Matrix.\n")
message("[<<<<< ChAMP.LOAD END >>>>>>]")
message("[===========================]")
message("[You may want to process champ.QC() next.]\n")
return(list(mset=mset,rgSet=rgSet,pd=pd,intensity=intensity,beta=beta.raw,detP=detP))
} else {
message("\n[ Loading Data with ChAMP Method ]")
message("----------------------------------")
message("Note that ChAMP method will NOT return rgSet or mset, they object defined by minfi. Which means, if you use ChAMP method to load data, you can not use SWAN or FunctionNormliazation method in champ.norm() (you can use BMIQ or PBC still). But All other function should not be influenced.\n")
myImport <- champ.import(directory,arraytype=arraytype)
if(methValue=="B")
myLoad <- champ.filter(beta=myImport$beta,
M=NULL,
pd=myImport$pd,
intensity=myImport$intensity,
Meth=NULL,
UnMeth=NULL,
detP=myImport$detP,
beadcount=myImport$beadcount,
autoimpute=autoimpute,
filterDetP=filterDetP,
ProbeCutoff=ProbeCutoff,
SampleCutoff=SampleCutoff,
detPcut=detPcut,
filterBeads=filterBeads,
beadCutoff=beadCutoff,
filterNoCG=filterNoCG,
filterSNPs=filterSNPs,
population=population,
filterMultiHit=filterMultiHit,
filterXY=filterXY,
arraytype=arraytype)
else
myLoad <- champ.filter(beta=NULL,
M=myImport$M,
pd=myImport$pd,
intensity=myImport$intensity,
Meth=NULL,
UnMeth=NULL,
detP=myImport$detP,
beadcount=myImport$beadcount,
autoimpute=autoimpute,
filterDetP=filterDetP,
ProbeCutoff=ProbeCutoff,
SampleCutoff=SampleCutoff,
detPcut=detPcut,
filterBeads=filterBeads,
beadCutoff=beadCutoff,
filterNoCG=filterNoCG,
filterSNPs=filterSNPs,
population=population,
filterMultiHit=filterMultiHit,
filterXY=filterXY,
arraytype=arraytype)
message("[<<<<< ChAMP.LOAD END >>>>>>]")
message("[===========================]")
message("[You may want to process champ.QC() next.]\n")
return(myLoad)
}
}
Try the ChAMP package in your browser
Any scripts or data that you put into this service are public.
ChAMP documentation built on Nov. 8, 2020, 5:58 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions champ.load
Documented in champ.load
if(getRversion() >= "3.1.0") utils::globalVariables(c("sampleNames<-","EPIC.manifest.hg19","EPIC.manifest.pop.hg19","hm450.manifest.hg19","hm450.manifest.pop.hg19","multi.hit","probe.features","probe.features.epic"))
champ.load <- function(directory = getwd(),
method = "ChAMP",
methValue="B",
autoimpute=TRUE,
filterDetP=TRUE,
ProbeCutoff=0,
SampleCutoff=0.1,
detPcut=0.01,
filterBeads=TRUE,
beadCutoff=0.05,
filterNoCG=TRUE,
filterSNPs=TRUE,
population=NULL,
filterMultiHit=TRUE,
filterXY=TRUE,
force=FALSE,
arraytype="450K")
{
message("[===========================]")
message("[<<<< ChAMP.LOAD START >>>>>]")
message("-----------------------------")
mybeadcount <- function(x)
{
#select out bead count dataframe
getNBeads(x) -> nb
#match rownames of beadcount dataframe to addresses
getProbeInfo(x,type="I")->typeIadd
match(typeIadd$AddressA,rownames(nb))->typeImatchA
match(typeIadd$AddressB,rownames(nb))->typeImatchB
#match rownames of beadcount dataframe to addresses
getProbeInfo(x,type="II")->typeIIadd
match(typeIIadd$Address,rownames(nb))->typeIImatch
nb->nbcg
locusNames <- getManifestInfo(x, "locusNames")
bc_temp <- matrix(NA_real_, ncol = ncol(x), nrow = length(locusNames),
dimnames = list(locusNames, sampleNames(x)))
TypeII.Name <- getProbeInfo(x, type = "II")$Name
bc_temp[TypeII.Name, ] <- nbcg[getProbeInfo(x, type = "II")$AddressA,]
TypeI <- getProbeInfo(x, type = "I")
bc_temp->bcB
bc_temp->bcA
bcB[TypeI$Name, ] <- nbcg[TypeI$AddressB,]
bcA[TypeI$Name, ] <- nbcg[TypeI$AddressA,]
which(bcB<3) -> bcB3
which(bcA<3) -> bcA3
bcA->bcA2
bcB->bcB2
bcA2[bcA3]<-NA
bcA2[bcB3]<-NA
data.frame(bcA2)->bc
bc
}
if(method=="minfi")
{
message("\n[ Loading Data with Minfi Method ]")
message("----------------------------------")
message("Loading data from ", directory)
myDir <- directory
suppressWarnings(targets <- read.metharray.sheet(myDir))
rgSet <- read.metharray.exp(targets = targets,extended=TRUE,force=force)
if(arraytype=="EPIC") rgSet@annotation <- c(array="IlluminaHumanMethylationEPIC",annotation="ilm10b4.hg19")
sampleNames(rgSet)=rgSet[[1]]
pd <- pData(rgSet)
mset <- preprocessRaw(rgSet)
detP <- detectionP(rgSet)
message("<< Read DataSet Success. >>\n")
if(methValue=="B") tmp = getBeta(mset, "Illumina") else tmp = getM(mset)
tmp[detP >= detPcut] <- NA
message("The fraction of failed positions per sample\n
(You may need to delete samples with high proportion of failed probes\n): ")
numfail <- matrix(colMeans(is.na(tmp)))
rownames(numfail) <- colnames(detP)
colnames(numfail) <- "Failed CpG Fraction."
print(numfail)
RemainSample <- which(numfail < SampleCutoff)
if(any(numfail >= SampleCutoff))
message("The detSamplecut parameter is : ",SampleCutoff, "\nSamples : ",
paste(rownames(numfail)[which(numfail >= SampleCutoff)],collapse=",")," will be deleted.\n",
"There are ",length(RemainSample)," samples left for analysis.\n")
rgSet <- rgSet[,RemainSample]
detP <- detP[,RemainSample]
mset <- mset[,RemainSample]
pd <- pd[RemainSample,]
tmp <- tmp[,RemainSample]
if(filterDetP)
{
mset.f = mset[rowSums(is.na(tmp)) <= ProbeCutoff*ncol(detP),]
if(ProbeCutoff==0)
{
message("Filtering probes with a detection p-value above ",detPcut," in one or more samples has removed ",dim(mset)[1]-dim(mset.f)[1]," probes from the analysis. If a large number of probes have been removed, ChAMP suggests you to identify potentially bad samples.")
}else{
message("Filtering probes with a detection p-value above ",detPcut," in at least ",ProbeCutoff*100,"% of samples has removed ",dim(mset)[1]-dim(mset.f)[1]," probes from the analysis. If a large number of probes have been removed, ChAMP suggests you look at the failedSample file to identify potentially bad samples.")
}
mset=mset.f
tmp <- tmp[rowSums(is.na(tmp)) <= ProbeCutoff*ncol(detP),]
message("<< Filter DetP Done. >>\n")
}
if(sum(is.na(tmp))==0){
message("\nThere is no NA values in your matrix, there is no need to imputation.\n")
}else
{
message("\nThere are ",sum(is.na(tmp))," NA remain in filtered Data Set. Impute can be done for remain NAs, but not suitable for small number samples. For small Data Set (like only 20 samples), we suggest you set parameter ProbeCutoff as 0 in champ.load() here, which would remove all NA involved probe no matter how many samples of those probes are NA.\n")
}
if(autoimpute & sum(is.na(tmp)) > 0){
message("Impute will be conducted here for remain ",sum(is.na(tmp))," NAs. Note that if you don't do this, NA values would be kept in your data set. You may use champ.impute() function to do more complex imputation as well.")
# Open a file to send messages to save lot's of information from impute.knn
message("\nImpute function is working now, it may need couple minutes...")
zz <- file("ImputeMessage.Rout", open="wt")
sink(zz)
sink(zz, type="message")
tmp <- impute.knn(tmp,k=5)$data
sink(type="message")
sink()
message("<< Imputation Done. >>\n")
}
if(filterBeads)
{
bc=mybeadcount(rgSet)
bc2=bc[rowSums(is.na(bc)) < beadCutoff*(ncol(bc)),]
mset.f2 = mset[featureNames(mset) %in% row.names(bc2),]
tmp <- tmp[rownames(tmp) %in% row.names(bc2),]
message("Filtering probes with a beadcount <3 in at least ",beadCutoff*100,"% of samples, has removed ",dim(mset)[1]-dim(mset.f2)[1]," from the analysis.")
mset=mset.f2
message("<< Filter Beads Done. >>\n")
}
if(filterNoCG)
{
mset.f2=dropMethylationLoci(mset,dropCH=T)
tmp <- tmp[rownames(tmp) %in% featureNames(mset.f2),]
message("Filtering non-cg probes, has removed ",dim(mset)[1]-dim(mset.f2)[1]," from the analysis.")
mset <- mset.f2
message("<< Filter NoCG Done. >>\n")
}
if(filterSNPs)
{
if(arraytype=="450K")
{
if(is.null(population))
{
message("Using general 450K SNP list for filtering.")
data(hm450.manifest.hg19)
maskname <- rownames(hm450.manifest.hg19)[which(hm450.manifest.hg19$MASK_general==TRUE)]
}else if(!population %in% c("AFR","EAS","EUR","SAS","AMR","GWD","YRI","TSI",
"IBS","CHS","PUR","JPT","GIH","CHB","STU","ITU",
"LWK","KHV","FIN","ESN","CEU","PJL","ACB","CLM",
"CDX","GBR","BEB","PEL","MSL","MXL","ASW"))
{
message("Using general 450K SNP list for filtering.")
data(hm450.manifest.hg19)
maskname <- rownames(hm450.manifest.hg19)[which(hm450.manifest.hg19$MASK_general==TRUE)]
}else
{
message("Using ",population," specific 450K SNP list for filtering.")
data(hm450.manifest.pop.hg19)
maskname <- rownames(hm450.manifest.pop.hg19)[which(hm450.manifest.pop.hg19[,paste("MASK_general_",population,sep="")]==TRUE)]
}
}else
{
if(is.null(population))
{
message("Using general EPIC SNP list for filtering.")
data(EPIC.manifest.hg19)
maskname <- rownames(EPIC.manifest.hg19)[which(EPIC.manifest.hg19$MASK_general==TRUE)]
}else if(!population %in% c("AFR","EAS","EUR","SAS","AMR","GWD","YRI","TSI",
"IBS","CHS","PUR","JPT","GIH","CHB","STU","ITU",
"LWK","KHV","FIN","ESN","CEU","PJL","ACB","CLM",
"CDX","GBR","BEB","PEL","MSL","MXL","ASW"))
{
message("Using general EPIC SNP list for filtering.")
data(EPIC.manifest.hg19)
maskname <- rownames(EPIC.manifest.hg19)[which(EPIC.manifest.hg19$MASK_general==TRUE)]
}else
{
message("Using ",population," specific EPIC SNP list for filtering.")
data(EPIC.manifest.pop.hg19)
maskname <- rownames(EPIC.manifest.pop.hg19)[which(EPIC.manifest.pop.hg19[,paste("MASK_general_",population,sep="")]==TRUE)]
}
}
mset.f2=mset[!featureNames(mset) %in% maskname,]
tmp <- tmp[!rownames(tmp) %in% maskname,]
message("Filtering probes with SNPs as identified in Zhou's Nucleic Acids Research Paper, 2016, has removed ",dim(mset)[1]-dim(mset.f2)[1]," from the analysis.")
mset=mset.f2
message("<< Filter SNP Done. >>\n")
}
if(filterMultiHit)
{
data(multi.hit)
mset.f2=mset[!featureNames(mset) %in% multi.hit$TargetID,]
tmp <- tmp[!rownames(tmp) %in% multi.hit$TargetID,]
message("Filtering probes that align to multiple locations as identified in Nordlund et al, has removed ",dim(mset)[1]-dim(mset.f2)[1]," from the analysis.")
mset=mset.f2
message("<< Filter MultiHit Done. >>\n")
}
if(filterXY)
{
if(arraytype=="EPIC") data(probe.features.epic) else data(probe.features)
autosomes=probe.features[!probe.features$CHR %in% c("X","Y"), ]
mset.f2=mset[featureNames(mset) %in% row.names(autosomes),]
tmp <- tmp[rownames(tmp) %in% row.names(autosomes),]
message("Filtering probes on the X or Y chromosome has removed ",dim(mset)[1]-dim(mset.f2)[1]," from the analysis.")
mset=mset.f2
message("<< Filter XY chromosome Done. >>\n")
}
message(paste(if(methValue=="B") "[Beta" else "[M","value is selected as output.]\n"))
beta.raw <- tmp
intensity <- minfi::getMeth(mset) + minfi::getUnmeth(mset)
detP <- detP[which(row.names(detP) %in% row.names(beta.raw)),]
if(min(beta.raw, na.rm=TRUE)<=0) beta.raw[beta.raw<=0] <- min(beta.raw[beta.raw > 0])
message("Zeros in your dataset have been replaced with smallest positive value.\n")
if(max(beta.raw, na.rm=TRUE)>=0) beta.raw[beta.raw>=1] <- max(beta.raw[beta.raw < 1])
message("One in your dataset have been replaced with largest value below 1.\n")
message("The analysis will be proceed with ", dim(beta.raw)[1], " probes and ",dim(beta.raw)[2], " samples.\n")
message("Current Data Set contains ",sum(is.na(beta.raw))," NA in ", if(methValue=="B") "[Beta]" else "[M]"," Matrix.\n")
message("[<<<<< ChAMP.LOAD END >>>>>>]")
message("[===========================]")
message("[You may want to process champ.QC() next.]\n")
return(list(mset=mset,rgSet=rgSet,pd=pd,intensity=intensity,beta=beta.raw,detP=detP))
} else {
message("\n[ Loading Data with ChAMP Method ]")
message("----------------------------------")
message("Note that ChAMP method will NOT return rgSet or mset, they object defined by minfi. Which means, if you use ChAMP method to load data, you can not use SWAN or FunctionNormliazation method in champ.norm() (you can use BMIQ or PBC still). But All other function should not be influenced.\n")
myImport <- champ.import(directory,arraytype=arraytype)
if(methValue=="B")
myLoad <- champ.filter(beta=myImport$beta,
M=NULL,
pd=myImport$pd,
intensity=myImport$intensity,
Meth=NULL,
UnMeth=NULL,
detP=myImport$detP,
beadcount=myImport$beadcount,
autoimpute=autoimpute,
filterDetP=filterDetP,
ProbeCutoff=ProbeCutoff,
SampleCutoff=SampleCutoff,
detPcut=detPcut,
filterBeads=filterBeads,
beadCutoff=beadCutoff,
filterNoCG=filterNoCG,
filterSNPs=filterSNPs,
population=population,
filterMultiHit=filterMultiHit,
filterXY=filterXY,
arraytype=arraytype)
else
myLoad <- champ.filter(beta=NULL,
M=myImport$M,
pd=myImport$pd,
intensity=myImport$intensity,
Meth=NULL,
UnMeth=NULL,
detP=myImport$detP,
beadcount=myImport$beadcount,
autoimpute=autoimpute,
filterDetP=filterDetP,
ProbeCutoff=ProbeCutoff,
SampleCutoff=SampleCutoff,
detPcut=detPcut,
filterBeads=filterBeads,
beadCutoff=beadCutoff,
filterNoCG=filterNoCG,
filterSNPs=filterSNPs,
population=population,
filterMultiHit=filterMultiHit,
filterXY=filterXY,
arraytype=arraytype)
message("[<<<<< ChAMP.LOAD END >>>>>>]")
message("[===========================]")
message("[You may want to process champ.QC() next.]\n")
return(myLoad)
}
}
Try the ChAMP package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.