hlaAttrBagging: Build a HIBAG model
In HIBAG: HLA Genotype Imputation with Attribute Bagging
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
To build a HIBAG model for predicting HLA types with SNP markers.
Usage
1
2
3
Arguments
hla
the training HLA types, an object of
hlaAlleleClass
snp
the training SNP genotypes, an object of
hlaSNPGenoClass
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
na.rm
if TRUE, remove the samples with missing HLA alleles
mono.rm
if TRUE, remove monomorphic SNPs
nthread
specify the number of threads used in the model building;
if TRUE, use the number of threads returned from
RcppParallel::defaultNumThreads() (by default using all threads)
verbose
if TRUE, show information
verbose.detail
if TRUE, show more information
Details
mtry (the number of variables randomly sampled as candidates
for each selection, "sqrt" by default):
"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
on non-informative SNP markers.
hlaParallelAttrBagging extends hlaAttrBagging to allow
parallel computing with multiple compute nodes in a cluster. An autosave
function is available in hlaParallelAttrBagging when an new
individual classifier is built internally without completing the ensemble.
Value
Return an object of hlaAttrBagClass:
n.samp
the total number of training samples
n.snp
the total number of candidate SNP predictors
sample.id
the sample IDs
snp.id
the SNP IDs
snp.position
SNP position in basepair
snp.allele
a vector of characters with the format of
“A allele/B allele”
snp.allele.freq
the allele frequencies
hla.locus
the name of HLA locus
hla.allele
the HLA alleles used in the model
hla.freq
the HLA allele frequencies
assembly
the human genome reference, such like "hg19"
model
internal use
matching
matching proportion in the training set
Author(s)
Xiuwen Zheng
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.
https://www.nature.com/articles/tpj201318
See Also
hlaClose, hlaParallelAttrBagging,
summary.hlaAttrBagClass,
predict.hlaAttrBagClass, hlaPredict,
hlaSetKernelTarget
Examples
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# 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$validation, pred, allele.limit=model,
call.threshold=0.5))
# save the parameter file
mobj <- hlaModelToObj(model)
save(mobj, file="HIBAG_model.RData")
save(test.geno, file="testgeno.RData")
save(hlatab, file="HLASplit.RData")
# Clear Workspace
hlaClose(model) # release all resources of model
rm(list = ls())
######################################################################
# NOW, load a HIBAG model from the parameter file
mobj <- get(load("HIBAG_model.RData"))
model <- hlaModelFromObj(mobj)
# validation
test.geno <- get(load("testgeno.RData"))
hlatab <- get(load("HLASplit.RData"))
pred <- hlaPredict(model, test.geno, type="response")
summary(pred)
# compare
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
call.threshold=0))
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
call.threshold=0.5))
# 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)
[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
[-] 2021-02-09 13:42:59
=== 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] 2021-02-09 13:42:59, 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] 2021-02-09 13:42:59, 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] 2021-02-09 13:42:59, 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] 2021-02-09 13:42:59, 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 (2021-02-09 13:42:59) 0%
Predicting (2021-02-09 13:42:59) 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 (2021-02-09 13:42:59) 0%
Predicting (2021-02-09 13:42:59) 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 documentation built on March 24, 2021, 6 p.m.
R Package Documentation
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
To build a HIBAG model for predicting HLA types with SNP markers.
Usage
1 2 3 |
Arguments
hla |
the training HLA types, an object of
|
snp |
the training SNP genotypes, an object of
|
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 |
na.rm |
if TRUE, remove the samples with missing HLA alleles |
mono.rm |
if TRUE, remove monomorphic SNPs |
nthread |
specify the number of threads used in the model building;
if |
verbose |
if |
verbose.detail |
if |
Details
mtry (the number of variables randomly sampled as candidates
for each selection, "sqrt" by default):
"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
on non-informative SNP markers.
hlaParallelAttrBagging extends hlaAttrBagging to allow
parallel computing with multiple compute nodes in a cluster. An autosave
function is available in hlaParallelAttrBagging when an new
individual classifier is built internally without completing the ensemble.
Value
Return an object of hlaAttrBagClass:
n.samp |
the total number of training samples |
n.snp |
the total number of candidate SNP predictors |
sample.id |
the sample IDs |
snp.id |
the SNP IDs |
snp.position |
SNP position in basepair |
snp.allele |
a vector of characters with the format of “A allele/B allele” |
snp.allele.freq |
the allele frequencies |
hla.locus |
the name of HLA locus |
hla.allele |
the HLA alleles used in the model |
hla.freq |
the HLA allele frequencies |
assembly |
the human genome reference, such like "hg19" |
model |
internal use |
matching |
matching proportion in the training set |
Author(s)
Xiuwen Zheng
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. https://www.nature.com/articles/tpj201318
See Also
hlaClose, hlaParallelAttrBagging,
summary.hlaAttrBagClass,
predict.hlaAttrBagClass, hlaPredict,
hlaSetKernelTarget
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 | # 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, type="response")
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))
# 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)
[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
[-] 2021-02-09 13:42:59
=== 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] 2021-02-09 13:42:59, 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] 2021-02-09 13:42:59, 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] 2021-02-09 13:42:59, 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] 2021-02-09 13:42:59, 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 (2021-02-09 13:42:59) 0%
Predicting (2021-02-09 13:42:59) 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 (2021-02-09 13:42:59) 0%
Predicting (2021-02-09 13:42:59) 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
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