hlaPublish: Finalize a HIBAG model

In HIBAG: HLA Genotype Imputation with Attribute Bagging

Description

Finalize a HIBAG model by removing unused SNP predictors and adding appendix information (platform, training set, authors, warning, etc)

Usage

hlaPublish(mobj, platform=NULL, information=NULL, warning=NULL,
    rm.unused.snp=TRUE, anonymize=TRUE, verbose=TRUE)

Arguments

mobj	an object of hlaAttrBagObj or hlaAttrBagClass
platform	the text of platform information
information	the other information, like authors
warning	any warning message
rm.unused.snp	if TRUE, remove unused SNPs from the model
anonymize	if TRUE, remove sample IDs
verbose	if TRUE, show information

Value

Returns a new object of hlaAttrBagObj.

Author(s)

Xiuwen Zheng

See Also

hlaModelFromObj, hlaModelToObj

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")

# training genotypes
region <- 250   # kb
snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, HapMap_CEU_Geno$snp.position,
    hla.id, region*1000, assembly="hg19")
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
    snp.sel = match(snpid, HapMap_CEU_Geno$snp.id),
    samp.sel = match(hla$value$sample.id, HapMap_CEU_Geno$sample.id))


#
# train a HIBAG model
#
set.seed(1000)

# please use "nclassifier=100" when you use HIBAG for real data
model <- hlaAttrBagging(hla, train.geno, nclassifier=2, verbose.detail=TRUE)
summary(model)
length(model$snp.id)

mobj <- hlaPublish(model,
    platform = "Illumina 1M Duo",
    information = "Training set -- HapMap Phase II")
model2 <- hlaModelFromObj(mobj)
length(mobj$snp.id)
mobj$appendix
summary(mobj)

p1 <- hlaPredict(model, train.geno)
p2 <- hlaPredict(model2, train.geno)

# check
cbind(p1$value, p2$value)

HIBAG documentation built on March 24, 2021, 6 p.m.