predict.hlaAttrBagClass {HIBAG} | R Documentation |
To predict HLA type based on a HIBAG model (in parallel).
hlaPredict(object, snp, cl=NULL, type=c("response", "prob", "response+prob"), vote=c("prob", "majority"), allele.check=TRUE, match.type=c("RefSNP+Position", "RefSNP", "Position"), same.strand=FALSE, verbose=TRUE) ## S3 method for class 'hlaAttrBagClass' predict(object, snp, cl, type=c("response", "prob", "response+prob"), vote=c("prob", "majority"), allele.check=TRUE, match.type=c("RefSNP+Position", "RefSNP", "Position"), same.strand=FALSE, verbose=TRUE, ...)
object |
a model of |
snp |
a genotypic object of |
cl |
if a cluster object, created by the package
parallel-package; if |
type |
"response": return the best-guess type plus its posterior probability; "prob": return all posterior probabilities; "response+prob": return the best-guess and all posterior probabilities |
vote |
|
allele.check |
if |
match.type |
|
same.strand |
|
verbose |
if TRUE, show information |
... |
further arguments passed to or from other methods |
If more than 50% of SNP predictors are missing, a warning will be given.
When match.type="RefSNP+Position"
, the matching of SNPs requires
both RefSNP IDs and positions. A lower missing fraction maybe gained by
matching RefSNP IDs or positions only. Call
predict(..., match.type="RefSNP")
or
predict(..., match.type="Position")
for this purpose.
It might be safe to assume that the SNPs with the same positions on the same
genome reference (e.g., hg19) are the same variant albeit the different RefSNP
IDs. Any concern about SNP mismatching should be emailed to the genotyping
platform provider.
Return a hlaAlleleClass
object with posterior probabilities
of predicted HLA types, or a matrix of pairwise possible HLA types with all
posterior probabilities. If type = "response+prob"
, return a
hlaAlleleClass
object with a matrix of postprob
for the
probabilities of all pairs of alleles.
If a probability matrix is returned, colnames
is sample.id
and rownames
is an unordered pair of HLA alleles.
Xiuwen Zheng
hlaAttrBagging
, hlaAllele
,
hlaCompareAllele
, hlaParallelAttrBagging
# make a "hlaAlleleClass" object hla.id <- "A" hla <- hlaAllele(HLA_Type_Table$sample.id, H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")], H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")], locus=hla.id, assembly="hg19") # divide HLA types randomly set.seed(100) hlatab <- hlaSplitAllele(hla, train.prop=0.5) names(hlatab) # "training" "validation" summary(hlatab$training) summary(hlatab$validation) # SNP predictors within the flanking region on each side region <- 500 # kb snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, HapMap_CEU_Geno$snp.position, hla.id, region*1000, assembly="hg19") length(snpid) # 275 # training and validation genotypes train.geno <- hlaGenoSubset(HapMap_CEU_Geno, snp.sel=match(snpid, HapMap_CEU_Geno$snp.id), samp.sel=match(hlatab$training$value$sample.id, HapMap_CEU_Geno$sample.id)) test.geno <- hlaGenoSubset(HapMap_CEU_Geno, samp.sel=match(hlatab$validation$value$sample.id, HapMap_CEU_Geno$sample.id)) # train a HIBAG model set.seed(100) model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=4, verbose.detail=TRUE) summary(model) # validation pred <- predict(model, test.geno) # compare (comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model, call.threshold=0)) (comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model, call.threshold=0.5))