HIBAG-package: HLA Genotype Imputation with Attribute Bagging
In HIBAG: HLA Genotype Imputation with Attribute Bagging
Description
Details
Author(s)
References
Examples
Description
To impute HLA types from unphased SNP data using an attribute bagging
method.
Details
Package: HIBAG
Type: R/Bioconductor Package
License: GPL version 3
Kernel Version: v1.4
HIBAG is a state of the art software package for imputing HLA types using
SNP data, and it uses the R statistical programming language. HIBAG is highly
accurate, computationally tractable, and can be used by researchers with
published parameter estimates instead of requiring access to large training
sample datasets. It combines the concepts of attribute bagging, an ensemble
classifier method, with haplotype inference for SNPs and HLA types. Attribute
bagging is a technique which improves the accuracy and stability of classifier
ensembles using bootstrap aggregating and random variable selection.
Features:
1) HIBAG can be used by researchers with published parameter estimates
(http://www.biostat.washington.edu/~bsweir/HIBAG/) instead of
requiring access to large training sample datasets.
2) A typical HIBAG parameter file contains only haplotype frequencies at
different SNP subsets rather than individual training genotypes.
3) SNPs within the xMHC region (chromosome 6) are used for imputation.
4) HIBAG employs unphased genotypes of unrelated individuals as a training
set.
5) HIBAG supports parallel computing with R.
Author(s)
Xiuwen Zheng [aut, cre, cph] zhengx@u.washington.edu,
Bruce S. Weir [ctb, ths] bsweir@u.washington.edu
References
Zheng X, Shen J, Cox C, Wakefield J, Ehm M, Nelson M, Weir BS;
HIBAG – HLA Genotype Imputation with Attribute Bagging.
The Pharmacogenomics Journal. doi: 10.1038/tpj.2013.18.
https://www.nature.com/articles/tpj201318
Examples
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# HLA_Type_Table data
head(HLA_Type_Table)
dim(HLA_Type_Table) # 60 13
# HapMap_CEU_Geno data
summary(HapMap_CEU_Geno)
######################################################################
# 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)
# please use "nclassifier=100" when you use HIBAG for real data
model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=4,
verbose.detail=TRUE)
summary(model)
# validation
pred <- hlaPredict(model, test.geno)
summary(pred)
# compare
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
call.threshold=0))
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
call.threshold=0.5))
# save the parameter file
mobj <- hlaModelToObj(model)
save(mobj, file="HIBAG_model.RData")
save(test.geno, file="testgeno.RData")
save(hlatab, file="HLASplit.RData")
# Clear Workspace
hlaClose(model) # release all resources of model
rm(list = ls())
######################################################################
# NOW, load a HIBAG model from the parameter file
mobj <- get(load("HIBAG_model.RData"))
model <- hlaModelFromObj(mobj)
# validation
test.geno <- get(load("testgeno.RData"))
hlatab <- get(load("HLASplit.RData"))
pred <- hlaPredict(model, test.geno)
# compare
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
call.threshold=0.5))
#########################################################################
# import a PLINK BED file
#
bed.fn <- system.file("extdata", "HapMap_CEU.bed", package="HIBAG")
fam.fn <- system.file("extdata", "HapMap_CEU.fam", package="HIBAG")
bim.fn <- system.file("extdata", "HapMap_CEU.bim", package="HIBAG")
hapmap.ceu <- hlaBED2Geno(bed.fn, fam.fn, bim.fn, assembly="hg19")
#########################################################################
# predict
#
pred <- hlaPredict(model, hapmap.ceu, type="response")
head(pred$value)
# sample.id allele1 allele2 prob
# 1 NA10859 01:01 03:01 0.9999992
# 2 NA11882 01:01 29:02 1.0000000
# ...
# delete the temporary files
unlink(c("HIBAG_model.RData", "testgeno.RData", "HLASplit.RData"), force=TRUE)
Example output
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.5 (64-bit, AVX2)
sample.id A.1 A.2 B.1 B.2 C.1 C.2 DQA1.1 DQA1.2 DQB1.1 DQB1.2
1 NA11882 01:01 29:02 15:01 44:03 06:02 16:01 01:02 03:01 03:02 06:02
2 NA11881 03:01 26:01 07:02 07:02 07:02 07:02 01:02 01:02 06:02 06:02
3 NA11993 26:01 29:02 44:03 <NA> 16:01 16:01 01:01 01:02 05:01 06:02
4 NA11992 01:01 02:01 08:01 35:01 04:01 07:01 01:01 05:01 02:01 05:01
5 NA11995 01:01 01:01 08:01 57:01 06:02 07:01 01:02 01:03 06:02 06:03
6 NA11994 01:01 11:01 07:02 51:01 07:02 15:02 03:01 03:01 03:02 03:02
DRB1.1 DRB1.2
1 04:01 15:01
2 15:01 15:01
3 01:01 15:01
4 01:01 03:01
5 13:01 15:01
6 04:02 04:04
[1] 60 13
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
[1] "training" "validation"
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
[1] 275
Exclude 11 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 64-bit, AVX2
# of threads: 1
[-] 2021-01-12 21:26:47
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13
2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13
3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13
4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13
5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13
6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13
7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16
8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18
9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19
10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21
11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23
12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23
13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23
[1] 2021-01-12 21:26:47, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17
2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23
3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31
4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31
5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32
6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34
7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38
8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36
9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42
10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42
11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44
12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48
[2] 2021-01-12 21:26:47, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11
2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12
3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12
4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12
5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12
6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12
7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12
8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14
9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14
10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14
11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14
[3] 2021-01-12 21:26:47, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12
2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13
3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16
4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18
5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18
6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20
7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20
8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21
9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21
10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21
11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22
12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23
13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23
[4] 2021-01-12 21:26:47, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470
Max. Mean SD
0.5261716555 0.0476729895 0.1180875414
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 38
avg. # of SNPs in an individual classifier: 12.25
(sd: 0.96, min: 11, max: 13, median: 12.50)
avg. # of haplotypes in an individual classifier: 27.00
(sd: 14.63, min: 14, max: 48, median: 23.00)
avg. out-of-bag accuracy: 81.61%
(sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470
Max. Mean SD
0.5261716555 0.0476729895 0.1180875414
Genome assembly: hg19
HIBAG model:
4 individual classifiers
264 SNPs
14 unique HLA alleles
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
# of samples: 26
CPU flags: 64-bit, AVX2
# of threads: 1
Predicting (2021-01-12 21:26:48) 0%
Predicting (2021-01-12 21:26:48) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002097 0.007711 0.034403 0.028032 0.526172
$overall
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 23 49 0.8846154 0.9423077 0
n.call call.rate
1 26 1
$confusion
True
Predict 01:01 02:01 02:06 03:01 11:01 23:01 24:02 24:03 25:01 26:01 29:02 31:01
01:01 12 1 0 0 0 0 0 0 0 0 0 0
02:01 0 21 0 0 0 0 0 0 0 0 0 0
02:06 0 0 0 0 0 0 0 0 0 0 0 0
03:01 0 0 0 5 0 0 0 0 0 0 0 0
11:01 0 0 0 0 2 0 0 0 0 0 0 0
23:01 0 0 0 0 0 1 0 0 0 0 0 0
24:02 0 0 0 0 0 1 3 0 0 0 0 0
24:03 0 0 0 0 0 0 0 0 0 0 0 0
25:01 0 0 0 0 0 0 0 0 1 0 0 0
26:01 0 0 0 0 0 0 0 0 0 0 0 0
29:02 0 0 0 0 0 0 0 0 0 0 1 0
31:01 0 0 0 0 0 0 0 0 0 1 0 1
32:01 0 0 0 0 0 0 0 0 0 0 0 0
68:01 0 0 0 0 0 0 0 0 0 0 0 0
... 0 0 0 0 0 0 0 0 0 0 0 0
True
Predict 32:01 68:01
01:01 0 0
02:01 0 0
02:06 0 0
03:01 0 0
11:01 0 0
23:01 0 0
24:02 0 0
24:03 0 0
25:01 0 0
26:01 0 0
29:02 0 0
31:01 0 0
32:01 1 0
68:01 0 1
... 0 0
$detail
allele train.num train.freq valid.num valid.freq call.rate accuracy
1 01:01 13 0.19117647 12 0.23076923 1 0.9807692
2 02:01 21 0.30882353 22 0.42307692 1 0.9807692
3 02:06 1 0.01470588 0 0.00000000 0 NaN
4 03:01 4 0.05882353 5 0.09615385 1 1.0000000
5 11:01 3 0.04411765 2 0.03846154 1 1.0000000
6 23:01 1 0.01470588 2 0.03846154 1 0.9807692
7 24:02 8 0.11764706 3 0.05769231 1 0.9807692
8 24:03 1 0.01470588 0 0.00000000 0 NaN
9 25:01 4 0.05882353 1 0.01923077 1 1.0000000
10 26:01 2 0.02941176 1 0.01923077 1 0.9807692
11 29:02 3 0.04411765 1 0.01923077 1 1.0000000
12 31:01 2 0.02941176 1 0.01923077 1 0.9807692
13 32:01 3 0.04411765 1 0.01923077 1 1.0000000
14 68:01 2 0.02941176 1 0.01923077 1 1.0000000
sensitivity specificity ppv npv miscall miscall.prop
1 1.0000000 0.9750000 0.9230769 1.0000000 <NA> NaN
2 0.9545455 1.0000000 1.0000000 0.9677419 01:01 1
3 NaN NaN NaN NaN <NA> NaN
4 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
5 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
6 0.5000000 1.0000000 1.0000000 0.9803922 24:02 1
7 1.0000000 0.9795918 0.7500000 1.0000000 <NA> NaN
8 NaN NaN NaN NaN <NA> NaN
9 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
10 0.0000000 1.0000000 NaN 0.9807692 31:01 1
11 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
12 1.0000000 0.9803922 0.5000000 1.0000000 <NA> NaN
13 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
14 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
$overall
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 21 42 1 1 0.5
n.call call.rate
1 21 0.8076923
$confusion
True
Predict 01:01 02:01 02:06 03:01 11:01 23:01 24:02 24:03 25:01 26:01 29:02 31:01
01:01 12 0 0 0 0 0 0 0 0 0 0 0
02:01 0 18 0 0 0 0 0 0 0 0 0 0
02:06 0 0 0 0 0 0 0 0 0 0 0 0
03:01 0 0 0 4 0 0 0 0 0 0 0 0
11:01 0 0 0 0 2 0 0 0 0 0 0 0
23:01 0 0 0 0 0 0 0 0 0 0 0 0
24:02 0 0 0 0 0 0 2 0 0 0 0 0
24:03 0 0 0 0 0 0 0 0 0 0 0 0
25:01 0 0 0 0 0 0 0 0 0 0 0 0
26:01 0 0 0 0 0 0 0 0 0 0 0 0
29:02 0 0 0 0 0 0 0 0 0 0 1 0
31:01 0 0 0 0 0 0 0 0 0 0 0 1
32:01 0 0 0 0 0 0 0 0 0 0 0 0
68:01 0 0 0 0 0 0 0 0 0 0 0 0
... 0 0 0 0 0 0 0 0 0 0 0 0
True
Predict 32:01 68:01
01:01 0 0
02:01 0 0
02:06 0 0
03:01 0 0
11:01 0 0
23:01 0 0
24:02 0 0
24:03 0 0
25:01 0 0
26:01 0 0
29:02 0 0
31:01 0 0
32:01 1 0
68:01 0 1
... 0 0
$detail
allele train.num train.freq valid.num valid.freq call.rate accuracy
1 01:01 13 0.19117647 12 0.23076923 1.0000000 1
2 02:01 21 0.30882353 22 0.42307692 0.8181818 1
3 02:06 1 0.01470588 0 0.00000000 0.0000000 NaN
4 03:01 4 0.05882353 5 0.09615385 0.8000000 1
5 11:01 3 0.04411765 2 0.03846154 1.0000000 1
6 23:01 1 0.01470588 2 0.03846154 0.0000000 NaN
7 24:02 8 0.11764706 3 0.05769231 0.6666667 1
8 24:03 1 0.01470588 0 0.00000000 0.0000000 NaN
9 25:01 4 0.05882353 1 0.01923077 0.0000000 NaN
10 26:01 2 0.02941176 1 0.01923077 0.0000000 NaN
11 29:02 3 0.04411765 1 0.01923077 1.0000000 1
12 31:01 2 0.02941176 1 0.01923077 1.0000000 1
13 32:01 3 0.04411765 1 0.01923077 1.0000000 1
14 68:01 2 0.02941176 1 0.01923077 1.0000000 1
sensitivity specificity ppv npv miscall miscall.prop
1 1 1 1 1 <NA> NaN
2 1 1 1 1 <NA> NaN
3 NaN NaN NaN NaN <NA> NaN
4 1 1 1 1 <NA> NaN
5 1 1 1 1 <NA> NaN
6 NaN NaN NaN NaN <NA> NaN
7 1 1 1 1 <NA> NaN
8 NaN NaN NaN NaN <NA> NaN
9 NaN NaN NaN NaN <NA> NaN
10 NaN NaN NaN NaN <NA> NaN
11 1 1 1 1 <NA> NaN
12 1 1 1 1 <NA> NaN
13 1 1 1 1 <NA> NaN
14 1 1 1 1 <NA> NaN
HIBAG model:
4 individual classifiers
264 SNPs
14 unique HLA alleles
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
# of samples: 26
CPU flags: 64-bit, AVX2
# of threads: 1
Predicting (2021-01-12 21:26:48) 0%
Predicting (2021-01-12 21:26:48) 100%
$overall
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 21 42 1 1 0.5
n.call call.rate
1 21 0.8076923
$confusion
True
Predict 01:01 02:01 02:06 03:01 11:01 23:01 24:02 24:03 25:01 26:01 29:02 31:01
01:01 12 0 0 0 0 0 0 0 0 0 0 0
02:01 0 18 0 0 0 0 0 0 0 0 0 0
02:06 0 0 0 0 0 0 0 0 0 0 0 0
03:01 0 0 0 4 0 0 0 0 0 0 0 0
11:01 0 0 0 0 2 0 0 0 0 0 0 0
23:01 0 0 0 0 0 0 0 0 0 0 0 0
24:02 0 0 0 0 0 0 2 0 0 0 0 0
24:03 0 0 0 0 0 0 0 0 0 0 0 0
25:01 0 0 0 0 0 0 0 0 0 0 0 0
26:01 0 0 0 0 0 0 0 0 0 0 0 0
29:02 0 0 0 0 0 0 0 0 0 0 1 0
31:01 0 0 0 0 0 0 0 0 0 0 0 1
32:01 0 0 0 0 0 0 0 0 0 0 0 0
68:01 0 0 0 0 0 0 0 0 0 0 0 0
... 0 0 0 0 0 0 0 0 0 0 0 0
True
Predict 32:01 68:01
01:01 0 0
02:01 0 0
02:06 0 0
03:01 0 0
11:01 0 0
23:01 0 0
24:02 0 0
24:03 0 0
25:01 0 0
26:01 0 0
29:02 0 0
31:01 0 0
32:01 1 0
68:01 0 1
... 0 0
$detail
allele train.num train.freq valid.num valid.freq call.rate accuracy
1 01:01 13 0.19117647 12 0.23076923 1.0000000 1
2 02:01 21 0.30882353 22 0.42307692 0.8181818 1
3 02:06 1 0.01470588 0 0.00000000 0.0000000 NaN
4 03:01 4 0.05882353 5 0.09615385 0.8000000 1
5 11:01 3 0.04411765 2 0.03846154 1.0000000 1
6 23:01 1 0.01470588 2 0.03846154 0.0000000 NaN
7 24:02 8 0.11764706 3 0.05769231 0.6666667 1
8 24:03 1 0.01470588 0 0.00000000 0.0000000 NaN
9 25:01 4 0.05882353 1 0.01923077 0.0000000 NaN
10 26:01 2 0.02941176 1 0.01923077 0.0000000 NaN
11 29:02 3 0.04411765 1 0.01923077 1.0000000 1
12 31:01 2 0.02941176 1 0.01923077 1.0000000 1
13 32:01 3 0.04411765 1 0.01923077 1.0000000 1
14 68:01 2 0.02941176 1 0.01923077 1.0000000 1
sensitivity specificity ppv npv miscall miscall.prop
1 1 1 1 1 <NA> NaN
2 1 1 1 1 <NA> NaN
3 NaN NaN NaN NaN <NA> NaN
4 1 1 1 1 <NA> NaN
5 1 1 1 1 <NA> NaN
6 NaN NaN NaN NaN <NA> NaN
7 1 1 1 1 <NA> NaN
8 NaN NaN NaN NaN <NA> NaN
9 NaN NaN NaN NaN <NA> NaN
10 NaN NaN NaN NaN <NA> NaN
11 1 1 1 1 <NA> NaN
12 1 1 1 1 <NA> NaN
13 1 1 1 1 <NA> NaN
14 1 1 1 1 <NA> NaN
Open "/usr/lib/R/site-library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "/usr/lib/R/site-library/HIBAG/extdata/HapMap_CEU.fam".
Open "/usr/lib/R/site-library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
HIBAG model:
4 individual classifiers
264 SNPs
14 unique HLA alleles
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
# of samples: 90
CPU flags: 64-bit, AVX2
# of threads: 1
Predicting (2021-01-12 21:26:48) 0%
Predicting (2021-01-12 21:26:48) 100%
sample.id allele1 allele2 prob matching
1 NA10859 01:01 03:01 0.9374958 0.0050823317
2 NA11882 01:01 29:02 0.9999996 0.0112456881
3 NA11881 03:01 31:01 0.2962896 0.0002162734
4 NA10860 02:01 02:01 0.3478309 0.4392334299
5 NA11993 25:01 29:02 0.7499603 0.0003245102
6 NA11992 01:01 02:01 0.9920706 0.0466853516
HIBAG documentation built on March 24, 2021, 6 p.m.
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Description Details Author(s) References Examples
Description
To impute HLA types from unphased SNP data using an attribute bagging method.
Details
| Package: | HIBAG |
| Type: | R/Bioconductor Package |
| License: | GPL version 3 |
| Kernel Version: | v1.4 |
HIBAG is a state of the art software package for imputing HLA types using SNP data, and it uses the R statistical programming language. HIBAG is highly accurate, computationally tractable, and can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection.
Features:
1) HIBAG can be used by researchers with published parameter estimates
(http://www.biostat.washington.edu/~bsweir/HIBAG/) instead of
requiring access to large training sample datasets.
2) A typical HIBAG parameter file contains only haplotype frequencies at
different SNP subsets rather than individual training genotypes.
3) SNPs within the xMHC region (chromosome 6) are used for imputation.
4) HIBAG employs unphased genotypes of unrelated individuals as a training
set.
5) HIBAG supports parallel computing with R.
Author(s)
Xiuwen Zheng [aut, cre, cph] zhengx@u.washington.edu, Bruce S. Weir [ctb, ths] bsweir@u.washington.edu
References
Zheng X, Shen J, Cox C, Wakefield J, Ehm M, Nelson M, Weir BS; HIBAG – HLA Genotype Imputation with Attribute Bagging. The Pharmacogenomics Journal. doi: 10.1038/tpj.2013.18. https://www.nature.com/articles/tpj201318
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | # HLA_Type_Table data
head(HLA_Type_Table)
dim(HLA_Type_Table) # 60 13
# HapMap_CEU_Geno data
summary(HapMap_CEU_Geno)
######################################################################
# 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$sample.id))
# train a HIBAG model
set.seed(100)
# please use "nclassifier=100" when you use HIBAG for real data
model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=4,
verbose.detail=TRUE)
summary(model)
# validation
pred <- hlaPredict(model, test.geno)
summary(pred)
# compare
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
call.threshold=0))
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
call.threshold=0.5))
# save the parameter file
mobj <- hlaModelToObj(model)
save(mobj, file="HIBAG_model.RData")
save(test.geno, file="testgeno.RData")
save(hlatab, file="HLASplit.RData")
# Clear Workspace
hlaClose(model) # release all resources of model
rm(list = ls())
######################################################################
# NOW, load a HIBAG model from the parameter file
mobj <- get(load("HIBAG_model.RData"))
model <- hlaModelFromObj(mobj)
# validation
test.geno <- get(load("testgeno.RData"))
hlatab <- get(load("HLASplit.RData"))
pred <- hlaPredict(model, test.geno)
# compare
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
call.threshold=0.5))
#########################################################################
# import a PLINK BED file
#
bed.fn <- system.file("extdata", "HapMap_CEU.bed", package="HIBAG")
fam.fn <- system.file("extdata", "HapMap_CEU.fam", package="HIBAG")
bim.fn <- system.file("extdata", "HapMap_CEU.bim", package="HIBAG")
hapmap.ceu <- hlaBED2Geno(bed.fn, fam.fn, bim.fn, assembly="hg19")
#########################################################################
# predict
#
pred <- hlaPredict(model, hapmap.ceu, type="response")
head(pred$value)
# sample.id allele1 allele2 prob
# 1 NA10859 01:01 03:01 0.9999992
# 2 NA11882 01:01 29:02 1.0000000
# ...
# delete the temporary files
unlink(c("HIBAG_model.RData", "testgeno.RData", "HLASplit.RData"), force=TRUE)
|
Example output
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.5 (64-bit, AVX2)
sample.id A.1 A.2 B.1 B.2 C.1 C.2 DQA1.1 DQA1.2 DQB1.1 DQB1.2
1 NA11882 01:01 29:02 15:01 44:03 06:02 16:01 01:02 03:01 03:02 06:02
2 NA11881 03:01 26:01 07:02 07:02 07:02 07:02 01:02 01:02 06:02 06:02
3 NA11993 26:01 29:02 44:03 <NA> 16:01 16:01 01:01 01:02 05:01 06:02
4 NA11992 01:01 02:01 08:01 35:01 04:01 07:01 01:01 05:01 02:01 05:01
5 NA11995 01:01 01:01 08:01 57:01 06:02 07:01 01:02 01:03 06:02 06:03
6 NA11994 01:01 11:01 07:02 51:01 07:02 15:02 03:01 03:01 03:02 03:02
DRB1.1 DRB1.2
1 04:01 15:01
2 15:01 15:01
3 01:01 15:01
4 01:01 03:01
5 13:01 15:01
6 04:02 04:04
[1] 60 13
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
[1] "training" "validation"
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
[1] 275
Exclude 11 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 64-bit, AVX2
# of threads: 1
[-] 2021-01-12 21:26:47
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13
2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13
3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13
4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13
5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13
6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13
7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16
8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18
9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19
10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21
11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23
12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23
13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23
[1] 2021-01-12 21:26:47, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17
2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23
3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31
4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31
5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32
6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34
7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38
8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36
9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42
10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42
11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44
12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48
[2] 2021-01-12 21:26:47, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11
2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12
3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12
4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12
5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12
6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12
7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12
8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14
9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14
10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14
11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14
[3] 2021-01-12 21:26:47, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12
2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13
3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16
4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18
5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18
6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20
7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20
8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21
9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21
10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21
11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22
12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23
13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23
[4] 2021-01-12 21:26:47, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470
Max. Mean SD
0.5261716555 0.0476729895 0.1180875414
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 38
avg. # of SNPs in an individual classifier: 12.25
(sd: 0.96, min: 11, max: 13, median: 12.50)
avg. # of haplotypes in an individual classifier: 27.00
(sd: 14.63, min: 14, max: 48, median: 23.00)
avg. out-of-bag accuracy: 81.61%
(sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470
Max. Mean SD
0.5261716555 0.0476729895 0.1180875414
Genome assembly: hg19
HIBAG model:
4 individual classifiers
264 SNPs
14 unique HLA alleles
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
# of samples: 26
CPU flags: 64-bit, AVX2
# of threads: 1
Predicting (2021-01-12 21:26:48) 0%
Predicting (2021-01-12 21:26:48) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002097 0.007711 0.034403 0.028032 0.526172
$overall
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 23 49 0.8846154 0.9423077 0
n.call call.rate
1 26 1
$confusion
True
Predict 01:01 02:01 02:06 03:01 11:01 23:01 24:02 24:03 25:01 26:01 29:02 31:01
01:01 12 1 0 0 0 0 0 0 0 0 0 0
02:01 0 21 0 0 0 0 0 0 0 0 0 0
02:06 0 0 0 0 0 0 0 0 0 0 0 0
03:01 0 0 0 5 0 0 0 0 0 0 0 0
11:01 0 0 0 0 2 0 0 0 0 0 0 0
23:01 0 0 0 0 0 1 0 0 0 0 0 0
24:02 0 0 0 0 0 1 3 0 0 0 0 0
24:03 0 0 0 0 0 0 0 0 0 0 0 0
25:01 0 0 0 0 0 0 0 0 1 0 0 0
26:01 0 0 0 0 0 0 0 0 0 0 0 0
29:02 0 0 0 0 0 0 0 0 0 0 1 0
31:01 0 0 0 0 0 0 0 0 0 1 0 1
32:01 0 0 0 0 0 0 0 0 0 0 0 0
68:01 0 0 0 0 0 0 0 0 0 0 0 0
... 0 0 0 0 0 0 0 0 0 0 0 0
True
Predict 32:01 68:01
01:01 0 0
02:01 0 0
02:06 0 0
03:01 0 0
11:01 0 0
23:01 0 0
24:02 0 0
24:03 0 0
25:01 0 0
26:01 0 0
29:02 0 0
31:01 0 0
32:01 1 0
68:01 0 1
... 0 0
$detail
allele train.num train.freq valid.num valid.freq call.rate accuracy
1 01:01 13 0.19117647 12 0.23076923 1 0.9807692
2 02:01 21 0.30882353 22 0.42307692 1 0.9807692
3 02:06 1 0.01470588 0 0.00000000 0 NaN
4 03:01 4 0.05882353 5 0.09615385 1 1.0000000
5 11:01 3 0.04411765 2 0.03846154 1 1.0000000
6 23:01 1 0.01470588 2 0.03846154 1 0.9807692
7 24:02 8 0.11764706 3 0.05769231 1 0.9807692
8 24:03 1 0.01470588 0 0.00000000 0 NaN
9 25:01 4 0.05882353 1 0.01923077 1 1.0000000
10 26:01 2 0.02941176 1 0.01923077 1 0.9807692
11 29:02 3 0.04411765 1 0.01923077 1 1.0000000
12 31:01 2 0.02941176 1 0.01923077 1 0.9807692
13 32:01 3 0.04411765 1 0.01923077 1 1.0000000
14 68:01 2 0.02941176 1 0.01923077 1 1.0000000
sensitivity specificity ppv npv miscall miscall.prop
1 1.0000000 0.9750000 0.9230769 1.0000000 <NA> NaN
2 0.9545455 1.0000000 1.0000000 0.9677419 01:01 1
3 NaN NaN NaN NaN <NA> NaN
4 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
5 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
6 0.5000000 1.0000000 1.0000000 0.9803922 24:02 1
7 1.0000000 0.9795918 0.7500000 1.0000000 <NA> NaN
8 NaN NaN NaN NaN <NA> NaN
9 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
10 0.0000000 1.0000000 NaN 0.9807692 31:01 1
11 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
12 1.0000000 0.9803922 0.5000000 1.0000000 <NA> NaN
13 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
14 1.0000000 1.0000000 1.0000000 1.0000000 <NA> NaN
$overall
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 21 42 1 1 0.5
n.call call.rate
1 21 0.8076923
$confusion
True
Predict 01:01 02:01 02:06 03:01 11:01 23:01 24:02 24:03 25:01 26:01 29:02 31:01
01:01 12 0 0 0 0 0 0 0 0 0 0 0
02:01 0 18 0 0 0 0 0 0 0 0 0 0
02:06 0 0 0 0 0 0 0 0 0 0 0 0
03:01 0 0 0 4 0 0 0 0 0 0 0 0
11:01 0 0 0 0 2 0 0 0 0 0 0 0
23:01 0 0 0 0 0 0 0 0 0 0 0 0
24:02 0 0 0 0 0 0 2 0 0 0 0 0
24:03 0 0 0 0 0 0 0 0 0 0 0 0
25:01 0 0 0 0 0 0 0 0 0 0 0 0
26:01 0 0 0 0 0 0 0 0 0 0 0 0
29:02 0 0 0 0 0 0 0 0 0 0 1 0
31:01 0 0 0 0 0 0 0 0 0 0 0 1
32:01 0 0 0 0 0 0 0 0 0 0 0 0
68:01 0 0 0 0 0 0 0 0 0 0 0 0
... 0 0 0 0 0 0 0 0 0 0 0 0
True
Predict 32:01 68:01
01:01 0 0
02:01 0 0
02:06 0 0
03:01 0 0
11:01 0 0
23:01 0 0
24:02 0 0
24:03 0 0
25:01 0 0
26:01 0 0
29:02 0 0
31:01 0 0
32:01 1 0
68:01 0 1
... 0 0
$detail
allele train.num train.freq valid.num valid.freq call.rate accuracy
1 01:01 13 0.19117647 12 0.23076923 1.0000000 1
2 02:01 21 0.30882353 22 0.42307692 0.8181818 1
3 02:06 1 0.01470588 0 0.00000000 0.0000000 NaN
4 03:01 4 0.05882353 5 0.09615385 0.8000000 1
5 11:01 3 0.04411765 2 0.03846154 1.0000000 1
6 23:01 1 0.01470588 2 0.03846154 0.0000000 NaN
7 24:02 8 0.11764706 3 0.05769231 0.6666667 1
8 24:03 1 0.01470588 0 0.00000000 0.0000000 NaN
9 25:01 4 0.05882353 1 0.01923077 0.0000000 NaN
10 26:01 2 0.02941176 1 0.01923077 0.0000000 NaN
11 29:02 3 0.04411765 1 0.01923077 1.0000000 1
12 31:01 2 0.02941176 1 0.01923077 1.0000000 1
13 32:01 3 0.04411765 1 0.01923077 1.0000000 1
14 68:01 2 0.02941176 1 0.01923077 1.0000000 1
sensitivity specificity ppv npv miscall miscall.prop
1 1 1 1 1 <NA> NaN
2 1 1 1 1 <NA> NaN
3 NaN NaN NaN NaN <NA> NaN
4 1 1 1 1 <NA> NaN
5 1 1 1 1 <NA> NaN
6 NaN NaN NaN NaN <NA> NaN
7 1 1 1 1 <NA> NaN
8 NaN NaN NaN NaN <NA> NaN
9 NaN NaN NaN NaN <NA> NaN
10 NaN NaN NaN NaN <NA> NaN
11 1 1 1 1 <NA> NaN
12 1 1 1 1 <NA> NaN
13 1 1 1 1 <NA> NaN
14 1 1 1 1 <NA> NaN
HIBAG model:
4 individual classifiers
264 SNPs
14 unique HLA alleles
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
# of samples: 26
CPU flags: 64-bit, AVX2
# of threads: 1
Predicting (2021-01-12 21:26:48) 0%
Predicting (2021-01-12 21:26:48) 100%
$overall
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 21 42 1 1 0.5
n.call call.rate
1 21 0.8076923
$confusion
True
Predict 01:01 02:01 02:06 03:01 11:01 23:01 24:02 24:03 25:01 26:01 29:02 31:01
01:01 12 0 0 0 0 0 0 0 0 0 0 0
02:01 0 18 0 0 0 0 0 0 0 0 0 0
02:06 0 0 0 0 0 0 0 0 0 0 0 0
03:01 0 0 0 4 0 0 0 0 0 0 0 0
11:01 0 0 0 0 2 0 0 0 0 0 0 0
23:01 0 0 0 0 0 0 0 0 0 0 0 0
24:02 0 0 0 0 0 0 2 0 0 0 0 0
24:03 0 0 0 0 0 0 0 0 0 0 0 0
25:01 0 0 0 0 0 0 0 0 0 0 0 0
26:01 0 0 0 0 0 0 0 0 0 0 0 0
29:02 0 0 0 0 0 0 0 0 0 0 1 0
31:01 0 0 0 0 0 0 0 0 0 0 0 1
32:01 0 0 0 0 0 0 0 0 0 0 0 0
68:01 0 0 0 0 0 0 0 0 0 0 0 0
... 0 0 0 0 0 0 0 0 0 0 0 0
True
Predict 32:01 68:01
01:01 0 0
02:01 0 0
02:06 0 0
03:01 0 0
11:01 0 0
23:01 0 0
24:02 0 0
24:03 0 0
25:01 0 0
26:01 0 0
29:02 0 0
31:01 0 0
32:01 1 0
68:01 0 1
... 0 0
$detail
allele train.num train.freq valid.num valid.freq call.rate accuracy
1 01:01 13 0.19117647 12 0.23076923 1.0000000 1
2 02:01 21 0.30882353 22 0.42307692 0.8181818 1
3 02:06 1 0.01470588 0 0.00000000 0.0000000 NaN
4 03:01 4 0.05882353 5 0.09615385 0.8000000 1
5 11:01 3 0.04411765 2 0.03846154 1.0000000 1
6 23:01 1 0.01470588 2 0.03846154 0.0000000 NaN
7 24:02 8 0.11764706 3 0.05769231 0.6666667 1
8 24:03 1 0.01470588 0 0.00000000 0.0000000 NaN
9 25:01 4 0.05882353 1 0.01923077 0.0000000 NaN
10 26:01 2 0.02941176 1 0.01923077 0.0000000 NaN
11 29:02 3 0.04411765 1 0.01923077 1.0000000 1
12 31:01 2 0.02941176 1 0.01923077 1.0000000 1
13 32:01 3 0.04411765 1 0.01923077 1.0000000 1
14 68:01 2 0.02941176 1 0.01923077 1.0000000 1
sensitivity specificity ppv npv miscall miscall.prop
1 1 1 1 1 <NA> NaN
2 1 1 1 1 <NA> NaN
3 NaN NaN NaN NaN <NA> NaN
4 1 1 1 1 <NA> NaN
5 1 1 1 1 <NA> NaN
6 NaN NaN NaN NaN <NA> NaN
7 1 1 1 1 <NA> NaN
8 NaN NaN NaN NaN <NA> NaN
9 NaN NaN NaN NaN <NA> NaN
10 NaN NaN NaN NaN <NA> NaN
11 1 1 1 1 <NA> NaN
12 1 1 1 1 <NA> NaN
13 1 1 1 1 <NA> NaN
14 1 1 1 1 <NA> NaN
Open "/usr/lib/R/site-library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "/usr/lib/R/site-library/HIBAG/extdata/HapMap_CEU.fam".
Open "/usr/lib/R/site-library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
HIBAG model:
4 individual classifiers
264 SNPs
14 unique HLA alleles
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
# of samples: 90
CPU flags: 64-bit, AVX2
# of threads: 1
Predicting (2021-01-12 21:26:48) 0%
Predicting (2021-01-12 21:26:48) 100%
sample.id allele1 allele2 prob matching
1 NA10859 01:01 03:01 0.9374958 0.0050823317
2 NA11882 01:01 29:02 0.9999996 0.0112456881
3 NA11881 03:01 31:01 0.2962896 0.0002162734
4 NA10860 02:01 02:01 0.3478309 0.4392334299
5 NA11993 25:01 29:02 0.7499603 0.0003245102
6 NA11992 01:01 02:01 0.9920706 0.0466853516
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