hlaModelFromObj: Conversion between the in-memory model and the object that...

In HIBAG: HLA Genotype Imputation with Attribute Bagging

Description

Build a model hlaAttrBagClass from an object of hlaAttrBagObj which is stored in an R object file, or convert hlaAttrBagClass to hlaAttrBagObj.

Usage

hlaModelFromObj(obj)
hlaModelToObj(model)

Arguments

obj	an object of hlaAttrBagObj
model	an object of hlaAttrBagClass

Value

hlaModelFromObj returns hlaAttrBagClass, and hlaModelToObj returns hlaAttrBagObj.

Author(s)

Xiuwen Zheng

See Also

hlaAttrBagging

Examples

# make a "hlaAlleleClass" object
hla.id <- "DQB1"
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 <- 100   # 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))

# train a HIBAG model
set.seed(1000)
model <- hlaAttrBagging(hla, train.geno, nclassifier=2)
print(model)

mobj <- hlaModelToObj(model)

is(model)
is(mobj)


# close the HIBAG model explicitly
hlaClose(model)

Example output

HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.5 (64-bit, AVX2)
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
    # of SNPs randomly sampled as candidates for each selection: 9
    # of SNPs: 77
    # of samples: 60
    # of unique HLA alleles: 12
CPU flags: 64-bit, AVX2
# of threads: 1
[-] 2021-07-02 22:48:36
=== building individual classifier 1, out-of-bag (25/41.7%) ===
[1] 2021-07-02 22:48:36, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
=== building individual classifier 2, out-of-bag (22/36.7%) ===
[2] 2021-07-02 22:48:36, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Accuracy with training data: 95.00%
Out-of-bag accuracy: 94.45%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 2
    total # of SNPs used: 20
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 1.41, min: 13, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 20.50
        (sd: 0.71, min: 20, max: 21, median: 20.50)
    avg. out-of-bag accuracy: 94.45%
        (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Genome assembly: hg19
[1] "hlaAttrBagClass"
[1] "hlaAttrBagObj"

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