HIBAG (version 1.8.3)

hlaParallelAttrBagging: Build a HIBAG model via parallel computation

Description

To build a HIBAG model for predicting HLA types via parallel computation.

Usage

hlaParallelAttrBagging(cl, hla, snp, auto.save="", nclassifier=100, mtry=c("sqrt", "all", "one"), prune=TRUE, rm.na=TRUE, stop.cluster=FALSE, verbose=TRUE)

Arguments

cl

a cluster object, created by the package parallel or snow; if NULL is given, a uniprocessor implementation will be performed

hla

training HLA types, an object of hlaAlleleClass

snp

training SNP genotypes, an object of hlaSNPGenoClass

auto.save

specify a autosaved file, see details

nclassifier

the total number of individual classifiers

mtry

a character or a numeric value, the number of variables randomly sampled as candidates for each selection. See details

prune

if TRUE, to perform a parsimonious forward variable selection, otherwise, exhaustive forward variable selection. See details

rm.na

if TRUE, remove the samples with missing HLA types

stop.cluster

TRUE: stop cluster nodes after computing

verbose

if TRUE, show information

Value

Return an object of hlaAttrBagClass if auto.save is specified.

Details

mtry (the number of variables randomly sampled as candidates for each selection): "sqrt", using the square root of the total number of candidate SNPs; "all", using all candidate SNPs; "one", using one SNP; an integer, specifying the number of candidate SNPs; 0 < r < 1, the number of candidate SNPs is "r * the total number of SNPs".

prune: there is no significant difference on accuracy between parsimonious and exhaustive forward variable selections. If prune = TRUE, the searching algorithm performs a parsimonious forward variable selection: if a new SNP predictor reduces the current out-of-bag accuracy, then it is removed from the candidate SNP set for future searching. Parsimonious selection helps to improve the computational efficiency by reducing the searching times of non-informative SNP markers.

If auto.save="", the function returns a HIBAG model (an object of hlaAttrBagClass); otherwise, there is no return.

References

Zheng X, Shen J, Cox C, Wakefield J, Ehm M, Nelson M, Weir BS; HIBAG -- HLA Genotype Imputation with Attribute Bagging. Pharmacogenomics Journal. doi: 10.1038/tpj.2013.18. http://www.nature.com/tpj/journal/v14/n2/full/tpj201318a.html

See Also

hlaAttrBagging, hlaClose

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))


#############################################################################

library(parallel)

# use option cl.core to choose an appropriate cluster size.
cl <- makeCluster(getOption("cl.cores", 2))
set.seed(100)

# train a HIBAG model in parallel
# please use "nclassifier=100" when you use HIBAG for real data
hlaParallelAttrBagging(cl, hlatab$training, train.geno, nclassifier=4,
    auto.save="tmp_model.RData", stop.cluster=TRUE)

mobj <- get(load("tmp_model.RData"))
summary(mobj)
model <- hlaModelFromObj(mobj)

# validation
pred <- predict(model, test.geno)
summary(pred)

# compare
hlaCompareAllele(hlatab$validation, pred, allele.limit=model)$overall


# since 'stop.cluster=TRUE' used in 'hlaParallelAttrBagging'
# need a new cluster
cl <- makeCluster(getOption("cl.cores", 2))

pred <- predict(model, test.geno, cl=cl)
summary(pred)

# stop parallel nodes
stopCluster(cl)


# delete the temporary file
unlink(c("tmp_model.RData"), force=TRUE)