hlaPredict: HIBAG model prediction (in parallel)

In zhengxwen/HIBAG: HLA Genotype Imputation with Attribute Bagging

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+dosage", "response", "prob", "response+prob"),
    vote=c("prob", "majority"), allele.check=TRUE,
    match.type=c("Position", "Pos+Allele", "RefSNP+Position", "RefSNP"),
    same.strand=FALSE, verbose=TRUE, verbose.match=TRUE)
## S3 method for class 'hlaAttrBagClass'
predict(object, snp, cl=FALSE,
    type=c("response+dosage", "response", "prob", "response+prob"),
    vote=c("prob", "majority"), allele.check=TRUE,
    match.type=c("Position", "Pos+Allele", "RefSNP+Position", "RefSNP"),
    same.strand=FALSE, verbose=TRUE, verbose.match=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+dosage": return the best-guess types and dosages for each allele (by default); "response": return the best-guess types with its posterior probability; "prob": return a matrix for all posterior probabilities; "response+prob": return the best-guess, dosages 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
verbose.match	if TRUE, show missing SNP proportions for different match.type
...	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, hlaAlleleToVCF

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, type="response+dosage")
pred

head(pred$value)
pred$dosage[, 1:4]  # a dosage matrix

# compare
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
    call.threshold=0))
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
    call.threshold=0.5))

zhengxwen/HIBAG documentation built on Nov. 19, 2024, 1:01 p.m.