hlaPredict {HIBAG}R Documentation

HIBAG model prediction (in parallel)

Description

To predict HLA type based on a HIBAG model (in parallel).

Usage

hlaPredict(object, snp, cl=FALSE,
    type=c("response", "prob", "response+prob"), vote=c("prob", "majority"),
    allele.check=TRUE, match.type=c("Position", "RefSNP+Position", "RefSNP"),
    same.strand=FALSE, verbose=TRUE)
## S3 method for class 'hlaAttrBagClass'
predict(object, snp, cl=FALSE,
    type=c("response", "prob", "response+prob"), vote=c("prob", "majority"),
    allele.check=TRUE, match.type=c("Position", "RefSNP+Position", "RefSNP"),
    same.strand=FALSE, verbose=TRUE, ...)

Arguments

object

a model of hlaAttrBagClass

snp

a genotypic object of hlaSNPGenoClass

cl

FALSE, TRUE, an integer, or a cluster object created by the parallel-package; if FALSE, use the serial implementation; if TRUE, use the number of threads returned from RcppParallel::defaultNumThreads() (by default using all threads); if an integer, specify the number of threads

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

"prob" (default behavior) – make a prediction based on the averaged posterior probabilities from all individual classifiers; "majority" – majority voting from all individual classifiers, where each classifier votes for an HLA type

allele.check

if TRUE, check and then switch allele pairs if needed

match.type

"Position" – use positions only (by default); "RefSNP+Position" – use both of SNP IDs and positions; "RefSNP" – using SNP IDs only

same.strand

TRUE assuming alleles are on the same strand (e.g., forward strand); otherwise, FALSE not assuming whether on the same strand or not

verbose

if TRUE, show information

...

unused

Details

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 SNP IDs and positions. A lower missing fraction maybe gained by matching SNP IDs or positions only. Call hlaPredict(..., match.type="RefSNP") or hlaPredict(..., match.type="Position") for this purpose. It could 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 SNP IDs. Any concern about SNP mismatching should be emailed to the genotyping platform provider.

Value

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.

Author(s)

Xiuwen Zheng

See Also

hlaAttrBagging, hlaAllele, hlaCompareAllele, hlaParallelAttrBagging, hlaSetKernelTarget

Examples

# 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 <- hlaPredict(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))
[Package HIBAG version 1.26.1 Index]