predict.hlaAttrBagClass {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=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, ...)

### Arguments

`object` | a model of `hlaAttrBagClass`
`snp` | a genotypic object of `hlaSNPGenoClass`
`cl` | if a cluster object, created by the package parallel-package; if `NULL`, serial implementation will be performed; if numeric, specify the number of cores
`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` | `"RefSNP+Position"` (by default) – using both of RefSNP IDs and positions; `"RefSNP"` – using RefSNP IDs only; `"Position"` – using positions 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
`...` | further arguments passed to or from other methods

### 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 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.

### 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`

### 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 <- 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))

[Package HIBAG version 1.16.0 Index]