Xiuwen Zheng
| Snapshot Date: 2022-10-18 13:55:19 -0400 (Tue, 18 Oct 2022) |
| git_url: https://git.bioconductor.org/packages/HIBAG |
| git_branch: RELEASE_3_15 |
| git_last_commit: fc2997f |
| git_last_commit_date: 2022-04-26 11:23:45 -0400 (Tue, 26 Apr 2022) |
| Back to Multiple platform build/check report for BioC 3.15 |
|
This page was generated on 2022-10-19 13:21:38 -0400 (Wed, 19 Oct 2022).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo1 | Linux (Ubuntu 20.04.5 LTS) | x86_64 | 4.2.1 (2022-06-23) -- "Funny-Looking Kid" | 4386 |
| palomino3 | Windows Server 2022 Datacenter | x64 | 4.2.1 (2022-06-23 ucrt) -- "Funny-Looking Kid" | 4138 |
| merida1 | macOS 10.14.6 Mojave | x86_64 | 4.2.1 (2022-06-23) -- "Funny-Looking Kid" | 4205 |
| Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X | ||||
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To the developers/maintainers of the HIBAG package: - Please allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/HIBAG.git to reflect on this report. See How and When does the builder pull? When will my changes propagate? for more information. - Make sure to use the following settings in order to reproduce any error or warning you see on this page. |
| Package 890/2140 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| HIBAG 1.32.0 (landing page) Xiuwen Zheng
| nebbiolo1 | Linux (Ubuntu 20.04.5 LTS) / x86_64 | OK | OK | OK | | ||||||||
| palomino3 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | | ||||||||
| merida1 | macOS 10.14.6 Mojave / x86_64 | OK | OK | OK | OK | | ||||||||
| Package: HIBAG |
| Version: 1.32.0 |
| Command: F:\biocbuild\bbs-3.15-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:HIBAG.install-out.txt --library=F:\biocbuild\bbs-3.15-bioc\R\library --no-vignettes --timings HIBAG_1.32.0.tar.gz |
| StartedAt: 2022-10-19 00:59:09 -0400 (Wed, 19 Oct 2022) |
| EndedAt: 2022-10-19 01:00:48 -0400 (Wed, 19 Oct 2022) |
| EllapsedTime: 99.7 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: HIBAG.Rcheck |
| Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### F:\biocbuild\bbs-3.15-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:HIBAG.install-out.txt --library=F:\biocbuild\bbs-3.15-bioc\R\library --no-vignettes --timings HIBAG_1.32.0.tar.gz ### ############################################################################## ############################################################################## * using log directory 'F:/biocbuild/bbs-3.15-bioc/meat/HIBAG.Rcheck' * using R version 4.2.1 (2022-06-23 ucrt) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: UTF-8 * using option '--no-vignettes' * checking for file 'HIBAG/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'HIBAG' version '1.32.0' * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'HIBAG' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... OK * checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking line endings in Makefiles ... OK * checking compilation flags in Makevars ... OK * checking for GNU extensions in Makefiles ... NOTE GNU make is a SystemRequirements. * checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK * checking use of PKG_*FLAGS in Makefiles ... OK * checking compiled code ... NOTE Note: information on .o files for x64 is not available File 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/libs/x64/HIBAG.dll': Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran) Found 'exit', possibly from 'exit' (C), 'stop' (Fortran) Compiled code should not call entry points which might terminate R nor write to stdout/stderr instead of to the console, nor use Fortran I/O nor system RNGs. The detected symbols are linked into the code but might come from libraries and not actually be called. See 'Writing portable packages' in the 'Writing R Extensions' manual. * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... Running 'runTests.R' OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes in 'inst/doc' ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 2 NOTEs See 'F:/biocbuild/bbs-3.15-bioc/meat/HIBAG.Rcheck/00check.log' for details.
HIBAG.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### F:\biocbuild\bbs-3.15-bioc\R\bin\R.exe CMD INSTALL HIBAG ### ############################################################################## ############################################################################## * installing to library 'F:/biocbuild/bbs-3.15-bioc/R/library' * installing *source* package 'HIBAG' ... ** using staged installation ** libs g++ -std=gnu++11 -I"F:/biocbuild/bbs-3.15-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.15-bioc/R/library/RcppParallel/include' -I"C:/rtools42/x86_64-w64-mingw32.static.posix/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c HIBAG.cpp -o HIBAG.o g++ -std=gnu++11 -I"F:/biocbuild/bbs-3.15-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.15-bioc/R/library/RcppParallel/include' -I"C:/rtools42/x86_64-w64-mingw32.static.posix/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA.cpp -o LibHLA.o g++ -std=gnu++11 -I"F:/biocbuild/bbs-3.15-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.15-bioc/R/library/RcppParallel/include' -I"C:/rtools42/x86_64-w64-mingw32.static.posix/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_avx.cpp -o LibHLA_ext_avx.o g++ -std=gnu++11 -I"F:/biocbuild/bbs-3.15-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.15-bioc/R/library/RcppParallel/include' -I"C:/rtools42/x86_64-w64-mingw32.static.posix/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_avx2.cpp -o LibHLA_ext_avx2.o g++ -std=gnu++11 -I"F:/biocbuild/bbs-3.15-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.15-bioc/R/library/RcppParallel/include' -I"C:/rtools42/x86_64-w64-mingw32.static.posix/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -ffixed-xmm16 -ffixed-xmm17 -ffixed-xmm18 -ffixed-xmm19 -ffixed-xmm20 -ffixed-xmm21 -ffixed-xmm22 -ffixed-xmm23 -ffixed-xmm24 -ffixed-xmm25 -ffixed-xmm26 -ffixed-xmm27 -ffixed-xmm28 -ffixed-xmm29 -ffixed-xmm30 -ffixed-xmm31 LibHLA_ext_avx512bw.cpp -c -o LibHLA_ext_avx512bw.o g++ -std=gnu++11 -I"F:/biocbuild/bbs-3.15-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.15-bioc/R/library/RcppParallel/include' -I"C:/rtools42/x86_64-w64-mingw32.static.posix/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -ffixed-xmm16 -ffixed-xmm17 -ffixed-xmm18 -ffixed-xmm19 -ffixed-xmm20 -ffixed-xmm21 -ffixed-xmm22 -ffixed-xmm23 -ffixed-xmm24 -ffixed-xmm25 -ffixed-xmm26 -ffixed-xmm27 -ffixed-xmm28 -ffixed-xmm29 -ffixed-xmm30 -ffixed-xmm31 LibHLA_ext_avx512f.cpp -c -o LibHLA_ext_avx512f.o g++ -std=gnu++11 -I"F:/biocbuild/bbs-3.15-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.15-bioc/R/library/RcppParallel/include' -I"C:/rtools42/x86_64-w64-mingw32.static.posix/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -ffixed-xmm16 -ffixed-xmm17 -ffixed-xmm18 -ffixed-xmm19 -ffixed-xmm20 -ffixed-xmm21 -ffixed-xmm22 -ffixed-xmm23 -ffixed-xmm24 -ffixed-xmm25 -ffixed-xmm26 -ffixed-xmm27 -ffixed-xmm28 -ffixed-xmm29 -ffixed-xmm30 -ffixed-xmm31 LibHLA_ext_avx512vpopcnt.cpp -c -o LibHLA_ext_avx512vpopcnt.o g++ -std=gnu++11 -I"F:/biocbuild/bbs-3.15-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.15-bioc/R/library/RcppParallel/include' -I"C:/rtools42/x86_64-w64-mingw32.static.posix/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_sse2.cpp -o LibHLA_ext_sse2.o g++ -std=gnu++11 -I"F:/biocbuild/bbs-3.15-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.15-bioc/R/library/RcppParallel/include' -I"C:/rtools42/x86_64-w64-mingw32.static.posix/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_sse4_2.cpp -o LibHLA_ext_sse4_2.o g++ -shared -s -static-libgcc -o HIBAG.dll tmp.def HIBAG.o LibHLA.o LibHLA_ext_avx.o LibHLA_ext_avx2.o LibHLA_ext_avx512bw.o LibHLA_ext_avx512f.o LibHLA_ext_avx512vpopcnt.o LibHLA_ext_sse2.o LibHLA_ext_sse4_2.o -LF:/biocbuild/bbs-3.15-bioc/R/library/RcppParallel/lib/x64 -ltbb -ltbbmalloc -LC:/rtools42/x86_64-w64-mingw32.static.posix/lib/x64 -LC:/rtools42/x86_64-w64-mingw32.static.posix/lib -LF:/biocbuild/bbs-3.15-bioc/R/bin/x64 -lR installing to F:/biocbuild/bbs-3.15-bioc/R/library/00LOCK-HIBAG/00new/HIBAG/libs/x64 ** R ** data *** moving datasets to lazyload DB ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (HIBAG)
HIBAG.Rcheck/tests/runTests.Rout
R version 4.2.1 (2022-06-23 ucrt) -- "Funny-Looking Kid"
Copyright (C) 2022 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> #############################################################
> #
> # DESCRIPTION: Unit tests in the HIBAG package
> #
>
> # load the HIBAG package
> library(HIBAG)
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.5 (32-bit, AVX512BW)
>
>
> #############################################################
>
> # a list of HLA genes
> hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1")
>
> # pre-defined lower bound of prediction accuracy
> hla.acc <- c(0.9, 0.8, 0.8, 0.8, 0.8, 0.7)
>
>
> for (hla.idx in seq_along(hla.list))
+ {
+ hla.id <- hla.list[hla.idx]
+
+ # make a "hlaAlleleClass" object
+ 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)
+
+ # 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")
+
+ # 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$sample.id))
+
+
+ # train a HIBAG model
+ set.seed(100)
+ model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10)
+ summary(model)
+
+ # validation
+ pred <- hlaPredict(model, test.geno, type="response")
+ summary(pred)
+
+ # compare
+ comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
+ call.threshold=0)
+ print(comp$overall)
+
+ # check
+ if (comp$overall$acc.haplo < hla.acc[hla.idx])
+ stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ", hla.acc[hla.idx], ".")
+
+ cat("\n\n")
+ }
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:15
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2022-10-19 01:00:15, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2022-10-19 01:00:15, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
[3] 2022-10-19 01:00:15, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
[4] 2022-10-19 01:00:15, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 5, out-of-bag (17/50.0%) ===
[5] 2022-10-19 01:00:15, oob acc: 79.41%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 6, out-of-bag (11/32.4%) ===
[6] 2022-10-19 01:00:15, oob acc: 100.00%, # of SNPs: 19, # of haplo: 72
=== building individual classifier 7, out-of-bag (9/26.5%) ===
[7] 2022-10-19 01:00:15, oob acc: 100.00%, # of SNPs: 17, # of haplo: 37
=== building individual classifier 8, out-of-bag (13/38.2%) ===
[8] 2022-10-19 01:00:15, oob acc: 88.46%, # of SNPs: 15, # of haplo: 57
=== building individual classifier 9, out-of-bag (15/44.1%) ===
[9] 2022-10-19 01:00:15, oob acc: 93.33%, # of SNPs: 13, # of haplo: 22
=== building individual classifier 10, out-of-bag (13/38.2%) ===
[10] 2022-10-19 01:00:15, oob acc: 80.77%, # of SNPs: 14, # of haplo: 24
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0005514978 0.0005592583 0.0006291033 0.0043053528 0.0091076487 0.0245841398
Max. Mean SD
0.4332369854 0.0431831923 0.0993605214
Accuracy with training data: 98.53%
Out-of-bag accuracy: 86.84%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 10
total # of SNPs used: 94
avg. # of SNPs in an individual classifier: 14.00
(sd: 2.40, min: 11, max: 19, median: 13.00)
avg. # of haplotypes in an individual classifier: 35.40
(sd: 18.39, min: 14, max: 72, median: 29.00)
avg. out-of-bag accuracy: 86.84%
(sd: 8.94%, min: 75.00%, max: 100.00%, median: 87.09%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0005514978 0.0005592583 0.0006291033 0.0043053528 0.0091076487 0.0245841398
Max. Mean SD
0.4332369854 0.0431831923 0.0993605214
Genome assembly: hg19
HIBAG model for HLA-A:
10 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:16) 0%
Predicting (2022-10-19 01:00:16) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 3 (11.5%) 4 (15.4%) 18 (69.2%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002500 0.007023 0.031426 0.026829 0.433237
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 25 51 0.9615385 0.9807692 0
n.call call.rate
1 26 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 340
# of samples: 28
# of unique HLA alleles: 22
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:16
=== building individual classifier 1, out-of-bag (12/42.9%) ===
[1] 2022-10-19 01:00:16, oob acc: 58.33%, # of SNPs: 17, # of haplo: 52
=== building individual classifier 2, out-of-bag (11/39.3%) ===
[2] 2022-10-19 01:00:16, oob acc: 63.64%, # of SNPs: 18, # of haplo: 51
=== building individual classifier 3, out-of-bag (13/46.4%) ===
[3] 2022-10-19 01:00:16, oob acc: 50.00%, # of SNPs: 15, # of haplo: 29
=== building individual classifier 4, out-of-bag (11/39.3%) ===
[4] 2022-10-19 01:00:16, oob acc: 59.09%, # of SNPs: 12, # of haplo: 57
=== building individual classifier 5, out-of-bag (11/39.3%) ===
[5] 2022-10-19 01:00:16, oob acc: 63.64%, # of SNPs: 15, # of haplo: 94
=== building individual classifier 6, out-of-bag (12/42.9%) ===
[6] 2022-10-19 01:00:16, oob acc: 79.17%, # of SNPs: 18, # of haplo: 66
=== building individual classifier 7, out-of-bag (12/42.9%) ===
[7] 2022-10-19 01:00:16, oob acc: 70.83%, # of SNPs: 15, # of haplo: 86
=== building individual classifier 8, out-of-bag (9/32.1%) ===
[8] 2022-10-19 01:00:16, oob acc: 77.78%, # of SNPs: 16, # of haplo: 117
=== building individual classifier 9, out-of-bag (9/32.1%) ===
[9] 2022-10-19 01:00:16, oob acc: 77.78%, # of SNPs: 18, # of haplo: 92
=== building individual classifier 10, out-of-bag (9/32.1%) ===
[10] 2022-10-19 01:00:16, oob acc: 61.11%, # of SNPs: 15, # of haplo: 72
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.567411e-05 9.622439e-05 1.011769e-04 3.069782e-03 7.279682e-03 1.186415e-02
Max. Mean SD
1.196521e-01 1.281183e-02 2.267335e-02
Accuracy with training data: 100.00%
Out-of-bag accuracy: 66.14%
Gene: HLA-B
Training dataset: 28 samples X 340 SNPs
# of HLA alleles: 22
# of individual classifiers: 10
total # of SNPs used: 118
avg. # of SNPs in an individual classifier: 15.90
(sd: 1.91, min: 12, max: 18, median: 15.50)
avg. # of haplotypes in an individual classifier: 71.60
(sd: 25.94, min: 29, max: 117, median: 69.00)
avg. out-of-bag accuracy: 66.14%
(sd: 9.84%, min: 50.00%, max: 79.17%, median: 63.64%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.567411e-05 9.622439e-05 1.011769e-04 3.069782e-03 7.279682e-03 1.186415e-02
Max. Mean SD
1.196521e-01 1.281183e-02 2.267335e-02
Genome assembly: hg19
HIBAG model for HLA-B:
10 individual classifiers
340 SNPs
22 unique HLA alleles: 07:02, 08:01, 13:02, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 15
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:16) 0%
Predicting (2022-10-19 01:00:16) 100%
Gene: HLA-B
Range: [31321649bp, 31324989bp] on hg19
# of samples: 15
# of unique HLA alleles: 9
# of unique HLA genotypes: 12
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
3 (20.0%) 5 (33.3%) 3 (20.0%) 4 (26.7%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.000e-08 4.068e-05 2.934e-03 1.789e-02 6.076e-03 1.326e-01
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 15 11 25 0.7333333 0.8333333 0
n.call call.rate
1 15 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 2 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 354
# of samples: 36
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:16
=== building individual classifier 1, out-of-bag (13/36.1%) ===
[1] 2022-10-19 01:00:16, oob acc: 80.77%, # of SNPs: 19, # of haplo: 40
=== building individual classifier 2, out-of-bag (11/30.6%) ===
[2] 2022-10-19 01:00:17, oob acc: 90.91%, # of SNPs: 32, # of haplo: 32
=== building individual classifier 3, out-of-bag (14/38.9%) ===
[3] 2022-10-19 01:00:17, oob acc: 89.29%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 4, out-of-bag (13/36.1%) ===
[4] 2022-10-19 01:00:17, oob acc: 84.62%, # of SNPs: 19, # of haplo: 100
=== building individual classifier 5, out-of-bag (9/25.0%) ===
[5] 2022-10-19 01:00:17, oob acc: 94.44%, # of SNPs: 22, # of haplo: 58
=== building individual classifier 6, out-of-bag (17/47.2%) ===
[6] 2022-10-19 01:00:17, oob acc: 79.41%, # of SNPs: 27, # of haplo: 63
=== building individual classifier 7, out-of-bag (12/33.3%) ===
[7] 2022-10-19 01:00:17, oob acc: 75.00%, # of SNPs: 19, # of haplo: 40
=== building individual classifier 8, out-of-bag (13/36.1%) ===
[8] 2022-10-19 01:00:17, oob acc: 80.77%, # of SNPs: 17, # of haplo: 60
=== building individual classifier 9, out-of-bag (11/30.6%) ===
[9] 2022-10-19 01:00:17, oob acc: 86.36%, # of SNPs: 20, # of haplo: 33
=== building individual classifier 10, out-of-bag (10/27.8%) ===
[10] 2022-10-19 01:00:18, oob acc: 90.00%, # of SNPs: 32, # of haplo: 77
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002218427 0.0002248414 0.0002518298 0.0017561996 0.0033916297 0.0102775434
Max. Mean SD
0.0909115957 0.0113752369 0.0199373731
Accuracy with training data: 100.00%
Out-of-bag accuracy: 85.16%
Gene: HLA-C
Training dataset: 36 samples X 354 SNPs
# of HLA alleles: 17
# of individual classifiers: 10
total # of SNPs used: 141
avg. # of SNPs in an individual classifier: 22.60
(sd: 5.64, min: 17, max: 32, median: 19.50)
avg. # of haplotypes in an individual classifier: 54.60
(sd: 21.63, min: 32, max: 100, median: 50.50)
avg. out-of-bag accuracy: 85.16%
(sd: 6.11%, min: 75.00%, max: 94.44%, median: 85.49%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002218427 0.0002248414 0.0002518298 0.0017561996 0.0033916297 0.0102775434
Max. Mean SD
0.0909115957 0.0113752369 0.0199373731
Genome assembly: hg19
HIBAG model for HLA-C:
10 individual classifiers
354 SNPs
17 unique HLA alleles: 01:02, 02:02, 03:03, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 24
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:18) 0%
Predicting (2022-10-19 01:00:18) 100%
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 24
# of unique HLA alleles: 14
# of unique HLA genotypes: 19
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (4.2%) 6 (25.0%) 3 (12.5%) 14 (58.3%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000e+00 1.000e-08 3.503e-04 7.608e-03 4.485e-03 7.769e-02
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 24 16 39 0.6666667 0.8125 0
n.call call.rate
1 24 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 4 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 345
# of samples: 31
# of unique HLA alleles: 7
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:18
=== building individual classifier 1, out-of-bag (11/35.5%) ===
[1] 2022-10-19 01:00:18, oob acc: 95.45%, # of SNPs: 11, # of haplo: 22
=== building individual classifier 2, out-of-bag (11/35.5%) ===
[2] 2022-10-19 01:00:18, oob acc: 100.00%, # of SNPs: 13, # of haplo: 22
=== building individual classifier 3, out-of-bag (15/48.4%) ===
[3] 2022-10-19 01:00:18, oob acc: 83.33%, # of SNPs: 15, # of haplo: 23
=== building individual classifier 4, out-of-bag (14/45.2%) ===
[4] 2022-10-19 01:00:18, oob acc: 82.14%, # of SNPs: 8, # of haplo: 14
=== building individual classifier 5, out-of-bag (13/41.9%) ===
[5] 2022-10-19 01:00:18, oob acc: 88.46%, # of SNPs: 11, # of haplo: 34
=== building individual classifier 6, out-of-bag (10/32.3%) ===
[6] 2022-10-19 01:00:18, oob acc: 90.00%, # of SNPs: 11, # of haplo: 21
=== building individual classifier 7, out-of-bag (13/41.9%) ===
[7] 2022-10-19 01:00:18, oob acc: 92.31%, # of SNPs: 14, # of haplo: 23
=== building individual classifier 8, out-of-bag (13/41.9%) ===
[8] 2022-10-19 01:00:18, oob acc: 96.15%, # of SNPs: 11, # of haplo: 16
=== building individual classifier 9, out-of-bag (14/45.2%) ===
[9] 2022-10-19 01:00:18, oob acc: 89.29%, # of SNPs: 12, # of haplo: 19
=== building individual classifier 10, out-of-bag (11/35.5%) ===
[10] 2022-10-19 01:00:18, oob acc: 86.36%, # of SNPs: 8, # of haplo: 13
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530
Max. Mean SD
0.537093886 0.028877632 0.094687228
Accuracy with training data: 96.77%
Out-of-bag accuracy: 90.35%
Gene: HLA-DQA1
Training dataset: 31 samples X 345 SNPs
# of HLA alleles: 7
# of individual classifiers: 10
total # of SNPs used: 80
avg. # of SNPs in an individual classifier: 11.40
(sd: 2.27, min: 8, max: 15, median: 11.00)
avg. # of haplotypes in an individual classifier: 20.70
(sd: 5.96, min: 13, max: 34, median: 21.50)
avg. out-of-bag accuracy: 90.35%
(sd: 5.72%, min: 82.14%, max: 100.00%, median: 89.64%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530
Max. Mean SD
0.537093886 0.028877632 0.094687228
Genome assembly: hg19
HIBAG model for HLA-DQA1:
10 individual classifiers
345 SNPs
7 unique HLA alleles: 01:01, 01:02, 01:03, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 29
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:18) 0%
Predicting (2022-10-19 01:00:18) 100%
Gene: HLA-DQA1
Range: [32605169bp, 32612152bp] on hg19
# of samples: 29
# of unique HLA alleles: 6
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
5 (17.2%) 5 (17.2%) 2 (6.9%) 17 (58.6%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000001 0.0019253 0.0069908 0.0532601 0.0167536 0.5404845
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 29 21 49 0.7241379 0.8448276 0
n.call call.rate
1 29 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 6 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 350
# of samples: 34
# of unique HLA alleles: 12
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:18
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2022-10-19 01:00:18, oob acc: 86.36%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2022-10-19 01:00:18, oob acc: 76.92%, # of SNPs: 21, # of haplo: 42
=== building individual classifier 3, out-of-bag (13/38.2%) ===
[3] 2022-10-19 01:00:18, oob acc: 80.77%, # of SNPs: 10, # of haplo: 17
=== building individual classifier 4, out-of-bag (13/38.2%) ===
[4] 2022-10-19 01:00:18, oob acc: 92.31%, # of SNPs: 22, # of haplo: 78
=== building individual classifier 5, out-of-bag (13/38.2%) ===
[5] 2022-10-19 01:00:19, oob acc: 92.31%, # of SNPs: 11, # of haplo: 40
=== building individual classifier 6, out-of-bag (14/41.2%) ===
[6] 2022-10-19 01:00:19, oob acc: 71.43%, # of SNPs: 8, # of haplo: 22
=== building individual classifier 7, out-of-bag (14/41.2%) ===
[7] 2022-10-19 01:00:19, oob acc: 71.43%, # of SNPs: 14, # of haplo: 53
=== building individual classifier 8, out-of-bag (11/32.4%) ===
[8] 2022-10-19 01:00:19, oob acc: 86.36%, # of SNPs: 14, # of haplo: 40
=== building individual classifier 9, out-of-bag (14/41.2%) ===
[9] 2022-10-19 01:00:19, oob acc: 100.00%, # of SNPs: 16, # of haplo: 56
=== building individual classifier 10, out-of-bag (13/38.2%) ===
[10] 2022-10-19 01:00:19, oob acc: 88.46%, # of SNPs: 14, # of haplo: 34
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003282346 0.0003687353 0.0007332412 0.0038570393 0.0073528147 0.0148594626
Max. Mean SD
0.3073781820 0.0225078064 0.0573939534
Accuracy with training data: 98.53%
Out-of-bag accuracy: 84.64%
Gene: HLA-DQB1
Training dataset: 34 samples X 350 SNPs
# of HLA alleles: 12
# of individual classifiers: 10
total # of SNPs used: 99
avg. # of SNPs in an individual classifier: 14.30
(sd: 4.45, min: 8, max: 22, median: 14.00)
avg. # of haplotypes in an individual classifier: 41.60
(sd: 17.55, min: 17, max: 78, median: 40.00)
avg. out-of-bag accuracy: 84.64%
(sd: 9.41%, min: 71.43%, max: 100.00%, median: 86.36%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003282346 0.0003687353 0.0007332412 0.0038570393 0.0073528147 0.0148594626
Max. Mean SD
0.3073781820 0.0225078064 0.0573939534
Genome assembly: hg19
HIBAG model for HLA-DQB1:
10 individual classifiers
350 SNPs
12 unique HLA alleles: 02:01, 02:02, 03:01, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:19) 0%
Predicting (2022-10-19 01:00:19) 100%
Gene: HLA-DQB1
Range: [32627241bp, 32634466bp] on hg19
# of samples: 26
# of unique HLA alleles: 10
# of unique HLA genotypes: 17
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
3 (11.5%) 7 (26.9%) 5 (19.2%) 11 (42.3%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0002253 0.0018486 0.0308488 0.0099906 0.4023552
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 21 46 0.8076923 0.8846154 0
n.call call.rate
1 26 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 5 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 18
# of SNPs: 322
# of samples: 35
# of unique HLA alleles: 20
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:19
=== building individual classifier 1, out-of-bag (15/42.9%) ===
[1] 2022-10-19 01:00:19, oob acc: 70.00%, # of SNPs: 17, # of haplo: 77
=== building individual classifier 2, out-of-bag (16/45.7%) ===
[2] 2022-10-19 01:00:19, oob acc: 56.25%, # of SNPs: 19, # of haplo: 92
=== building individual classifier 3, out-of-bag (15/42.9%) ===
[3] 2022-10-19 01:00:19, oob acc: 70.00%, # of SNPs: 11, # of haplo: 32
=== building individual classifier 4, out-of-bag (15/42.9%) ===
[4] 2022-10-19 01:00:20, oob acc: 73.33%, # of SNPs: 20, # of haplo: 138
=== building individual classifier 5, out-of-bag (14/40.0%) ===
[5] 2022-10-19 01:00:20, oob acc: 75.00%, # of SNPs: 17, # of haplo: 73
=== building individual classifier 6, out-of-bag (12/34.3%) ===
[6] 2022-10-19 01:00:20, oob acc: 66.67%, # of SNPs: 20, # of haplo: 154
=== building individual classifier 7, out-of-bag (11/31.4%) ===
[7] 2022-10-19 01:00:20, oob acc: 63.64%, # of SNPs: 15, # of haplo: 38
=== building individual classifier 8, out-of-bag (11/31.4%) ===
[8] 2022-10-19 01:00:21, oob acc: 68.18%, # of SNPs: 19, # of haplo: 115
=== building individual classifier 9, out-of-bag (12/34.3%) ===
[9] 2022-10-19 01:00:21, oob acc: 83.33%, # of SNPs: 21, # of haplo: 141
=== building individual classifier 10, out-of-bag (11/31.4%) ===
[10] 2022-10-19 01:00:21, oob acc: 81.82%, # of SNPs: 15, # of haplo: 89
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
5.613590e-05 7.650057e-05 2.597826e-04 1.561236e-03 3.938807e-03 8.196339e-03
Max. Mean SD
4.747113e-01 4.329295e-02 1.276469e-01
Accuracy with training data: 92.86%
Out-of-bag accuracy: 70.82%
Gene: HLA-DRB1
Training dataset: 35 samples X 322 SNPs
# of HLA alleles: 20
# of individual classifiers: 10
total # of SNPs used: 119
avg. # of SNPs in an individual classifier: 17.40
(sd: 3.06, min: 11, max: 21, median: 18.00)
avg. # of haplotypes in an individual classifier: 94.90
(sd: 42.05, min: 32, max: 154, median: 90.50)
avg. out-of-bag accuracy: 70.82%
(sd: 8.10%, min: 56.25%, max: 83.33%, median: 70.00%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
5.613590e-05 7.650057e-05 2.597826e-04 1.561236e-03 3.938807e-03 8.196339e-03
Max. Mean SD
4.747113e-01 4.329295e-02 1.276469e-01
Genome assembly: hg19
HIBAG model for HLA-DRB1:
10 individual classifiers
322 SNPs
20 unique HLA alleles: 01:01, 01:03, 03:01, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 25
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:21) 0%
Predicting (2022-10-19 01:00:21) 100%
Gene: HLA-DRB1
Range: [32546546bp, 32557613bp] on hg19
# of samples: 25
# of unique HLA alleles: 11
# of unique HLA genotypes: 18
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
5 (20.0%) 3 (12.0%) 11 (44.0%) 6 (24.0%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
3.062e-05 3.538e-04 2.041e-03 7.954e-03 3.179e-03 1.275e-01
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 25 16 40 0.64 0.8 0
n.call call.rate
1 25 1
>
>
>
> #############################################################
>
> {
+ function.list <- readRDS(
+ system.file("Meta", "Rd.rds", package="HIBAG"))$Name
+
+ sapply(function.list, FUN = function(func.name)
+ {
+ args <- list(
+ topic = func.name,
+ package = "HIBAG",
+ echo = FALSE,
+ verbose = FALSE,
+ ask = FALSE
+ )
+ suppressWarnings(do.call(example, args))
+ NULL
+ })
+ invisible()
+ }
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
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:21
=== 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] 2022-10-19 01:00:21, 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] 2022-10-19 01:00:21, 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] 2022-10-19 01:00:21, 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] 2022-10-19 01:00:21, 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 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: HLA-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 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:21) 0%
Predicting (2022-10-19 01:00:21) 100%
Gene: HLA-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 0.032190 0.028032 0.471142
Dosages:
$dosage - num [1:14, 1:26] 2.50e-01 2.16e-04 2.50e-06 7.31e-01 1.11e-14 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:21) 0%
Predicting (2022-10-19 01:00:21) 100%
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 90
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:21) 0%
Predicting (2022-10-19 01:00:21) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
using the default genome assembly (assembly="hg19")
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 12
# of unique HLA genotypes: 28
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 100
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 32 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 40
# of SNPs: 1532
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:21
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2022-10-19 01:00:22, oob acc: 78.26%, # of SNPs: 18, # of haplo: 90
=== building individual classifier 2, out-of-bag (23/38.3%) ===
[2] 2022-10-19 01:00:23, oob acc: 95.65%, # of SNPs: 27, # of haplo: 97
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.799523e-15 7.953970e-15 9.343990e-15 1.231675e-04 6.160649e-04 2.017820e-03
Max. Mean SD
1.865571e-01 9.016315e-03 3.172989e-02
Accuracy with training data: 98.33%
Out-of-bag accuracy: 86.96%
Gene: HLA-A
Training dataset: 60 samples X 1532 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 43
avg. # of SNPs in an individual classifier: 22.50
(sd: 6.36, min: 18, max: 27, median: 22.50)
avg. # of haplotypes in an individual classifier: 93.50
(sd: 4.95, min: 90, max: 97, median: 93.50)
avg. out-of-bag accuracy: 86.96%
(sd: 12.30%, min: 78.26%, max: 95.65%, median: 86.96%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.799523e-15 7.953970e-15 9.343990e-15 1.231675e-04 6.160649e-04 2.017820e-03
Max. Mean SD
1.865571e-01 9.016315e-03 3.172989e-02
Genome assembly: hg19
HIBAG model for HLA-A:
2 individual classifiers
1532 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 60
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:23) 0%
Predicting (2022-10-19 01:00:23) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 13
# of unique HLA genotypes: 28
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
2 (3.3%) 7 (11.7%) 5 (8.3%) 46 (76.7%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0001232 0.0006161 0.0090163 0.0020178 0.1865571
Dosages:
$dosage - num [1:14, 1:60] 1.0 3.2e-01 2.0e-12 9.2e-15 1.8e-01 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:60] "NA11882" "NA11881" "NA11993" "NA11992" ...
Convert to dosage VCF format:
# of samples: 4
# of unique HLA alleles: 5
output: <connection>
##fileformat=VCFv4.0
##fileDate=20221019
##source=HIBAG
##FILTER=<ID=PASS,Description="All filters passed">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=DS,Number=1,Type=Float,Description="Dosage of HLA allele">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA11882 NA11881 NA11993 NA11992
6 29911954 HLA-A*01:01 A P_0101 . PASS . GT:DS 1/0:9.9998e-01 0/0:2.4401e-15 0/0:1.0000e-15 1/0:1.0000e+00
6 29911954 HLA-A*02:01 A P_0201 . PASS . GT:DS 0/0:3.1975e-01 0/0:2.5339e-06 0/0:4.2766e-01 0/1:1.0000e+00
6 29911954 HLA-A*03:01 A P_0301 . PASS . GT:DS 0/0:9.1998e-15 1/0:1.0000e+00 0/0:7.2339e-02 0/0:1.6412e-25
6 29911954 HLA-A*26:01 A P_2601 . PASS . GT:DS 0/0:1.9985e-12 0/1:7.5000e-01 1/0:5.4276e-01 0/0:8.4434e-13
6 29911954 HLA-A*29:02 A P_2902 . PASS . GT:DS 0/1:5.0000e-01 0/0:2.5000e-21 0/1:5.0000e-01 0/0:3.1934e-26
dominant model:
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042*
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000
02:01 25 35 52.0 48.6 0.0000 1.000 1.000
02:06 59 1 50.8 0.0 0.0000 1.000 1.000
03:01 51 9 49.0 55.6 0.0000 1.000 1.000
11:01 55 5 50.9 40.0 0.0000 1.000 1.000
23:01 58 2 50.0 50.0 0.0000 1.000 1.000
24:03 59 1 50.8 0.0 0.0000 1.000 1.000
25:01 55 5 52.7 20.0 0.8727 0.350 0.353
26:01 57 3 52.6 0.0 1.4035 0.236 0.237
29:02 56 4 51.8 25.0 0.2679 0.605 0.612
31:01 57 3 49.1 66.7 0.0000 1.000 1.000
32:01 56 4 46.4 100.0 2.4107 0.121 0.112
68:01 57 3 52.6 0.0 1.4035 0.236 0.237
additive model:
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p
01:01 95 25 50.5 48.0 0.0000 1.000 1.000
02:01 77 43 48.1 53.5 0.1450 0.703 0.704
02:06 119 1 50.4 0.0 0.0000 1.000 1.000
03:01 111 9 49.5 55.6 0.0000 1.000 1.000
11:01 115 5 50.4 40.0 0.0000 1.000 1.000
23:01 117 3 50.4 33.3 0.0000 1.000 1.000
24:02 109 11 46.8 81.8 3.6030 0.058 0.053
24:03 119 1 50.4 0.0 0.0000 1.000 1.000
25:01 115 5 51.3 20.0 0.8348 0.361 0.364
26:01 117 3 51.3 0.0 1.3675 0.242 0.244
29:02 116 4 50.9 25.0 0.2586 0.611 0.619
31:01 117 3 49.6 66.7 0.0000 1.000 1.000
32:01 116 4 48.3 100.0 2.3276 0.127 0.119
68:01 117 3 51.3 0.0 1.3675 0.242 0.244
recessive model:
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p
01:01 59 1 50.8 0 0.000 1.000 1.000
02:01 52 8 46.2 75 1.298 0.255 0.254
02:06 60 0 50.0 . . . .
03:01 60 0 50.0 . . . .
11:01 60 0 50.0 . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000
24:02 60 0 50.0 . . . .
24:03 60 0 50.0 . . . .
25:01 60 0 50.0 . . . .
26:01 60 0 50.0 . . . .
29:02 60 0 50.0 . . . .
31:01 60 0 50.0 . . . .
32:01 60 0 50.0 . . . .
68:01 60 0 50.0 . . . .
genotype model:
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
dominant model:
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p
01:01 36 24 -0.14684 -0.117427 0.909
02:01 25 35 -0.32331 -0.000618 0.190
02:06 59 1 -0.14024 0.170057 .
03:01 51 9 -0.05600 -0.583178 0.147
11:01 55 5 -0.19188 0.489815 0.287
23:01 58 2 -0.15400 0.413687 0.281
24:02 49 11 -0.10486 -0.269664 0.537
24:03 59 1 -0.11409 -1.373118 .
25:01 55 5 -0.12237 -0.274749 0.742
26:01 57 3 -0.12473 -0.331558 0.690
29:02 56 4 -0.13044 -0.199941 0.789
31:01 57 3 -0.10097 -0.783003 0.607
32:01 56 4 -0.07702 -0.947791 0.092
68:01 57 3 -0.16915 0.512457 0.196
genotype model:
[-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p
01:01 36 23 1 -0.14684 -0.08833 -0.78655 0.784
02:01 25 27 8 -0.32331 -0.02341 0.07631 0.446
02:06 59 1 0 -0.14024 0.17006 . 0.756
03:01 51 9 0 -0.05600 -0.58318 . 0.138
11:01 55 5 0 -0.19188 0.48981 . 0.137
23:01 58 1 1 -0.15400 0.10762 0.71975 0.663
24:02 49 11 0 -0.10486 -0.26966 . 0.618
24:03 59 1 0 -0.11409 -1.37312 . 0.205
25:01 55 5 0 -0.12237 -0.27475 . 0.742
26:01 57 3 0 -0.12473 -0.33156 . 0.725
29:02 56 4 0 -0.13044 -0.19994 . 0.892
31:01 57 3 0 -0.10097 -0.78300 . 0.243
32:01 56 4 0 -0.07702 -0.94779 . 0.086
68:01 57 3 0 -0.16915 0.51246 . 0.243
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.792e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -8.777e-16
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.372e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.624e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.418e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 2.874e-15
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.495e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.170e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.282e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.771e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
h.2.5% h.97.5% h.pval
24:02 0.1585 3.4251 0.032*
-----
01:01 -1.0330 1.0330 1.000
02:01 -1.1643 0.8899 0.793
02:06 -2868.1268 2836.9268 0.991
03:01 -1.1624 1.6872 0.718
11:01 -2.3074 1.4237 0.643
23:01 -2.8192 2.8192 1.000
24:03 -2868.1268 2836.9268 0.991
25:01 -3.7498 0.7588 0.194
26:01 -2731.9621 2698.6192 0.990
29:02 -3.4931 1.1530 0.324
31:01 -1.7277 3.1842 0.561
32:01 -3859.2763 3894.6947 0.993
68:01 -2731.9621 2698.6192 0.990
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.793e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -2.268e-04
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.370e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.562e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.686e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.451e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 -3.062e-03
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.501e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.189e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.289e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.781e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.673e+01
h.2.5% h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 0.1587 3.4264 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 -1.0334 1.0330 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 -1.1652 0.8913 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 -2868.1460 2836.9076 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 -1.1813 1.7185 0.717 0.011958 -0.5044 0.5283 0.964
11:01 -2.3225 1.4322 0.642 0.008025 -0.5026 0.5186 0.975
23:01 -2.8348 2.8287 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 -2868.1286 2836.9250 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 -3.7579 0.7568 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 -2731.8901 2698.5450 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 -3.5309 1.1526 0.320 0.033234 -0.4796 0.5461 0.899
31:01 -1.7274 3.1851 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 -3845.6317 3881.2510 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 -2721.2124 2687.7497 0.990 -0.086589 -0.6512 0.4781 0.764
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 6.005e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 9.998e-01
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 8.720e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 1.647e-07
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 1.308e+00
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 6.407e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 9.969e-01
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 1.676e-07
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 2.230e-01
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.744e-08
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 3.045e-01
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 2.073e+00
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 5.428e+07
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.416e-08
h.2.5%_OR h.97.5%_OR h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 1.17200 30.766 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 0.35579 2.809 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 0.31185 2.438 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 0.00000 Inf 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 0.30687 5.576 0.717 0.011958 -0.5044 0.5283 0.964
11:01 0.09803 4.188 0.642 0.008025 -0.5026 0.5186 0.975
23:01 0.05873 16.923 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 0.00000 Inf 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 0.02333 2.131 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 0.00000 Inf 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 0.02928 3.167 0.320 0.033234 -0.4796 0.5461 0.899
31:01 0.17774 24.171 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 0.00000 Inf 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 0.00000 Inf 0.990 -0.086589 -0.6512 0.4781 0.764
Linear regression (dominant model) with 60 individuals:
glm(y ~ h, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.02941 -0.4805 0.5393
02:01 25 35 -0.32331 -0.000618 0.190 0.32269 -0.1772 0.8226
02:06 59 1 -0.14024 0.170057 . 0.31030 -1.6397 2.2603
03:01 51 9 -0.05600 -0.583178 0.147 -0.52718 -1.2136 0.1592
11:01 55 5 -0.19188 0.489815 0.287 0.68170 -0.2051 1.5685
23:01 58 2 -0.15400 0.413687 0.281 0.56768 -0.8165 1.9518
24:02 49 11 -0.10486 -0.269664 0.537 -0.16481 -0.8091 0.4795
24:03 59 1 -0.11409 -1.373118 . -1.25903 -3.1835 0.6655
25:01 55 5 -0.12237 -0.274749 0.742 -0.15237 -1.0555 0.7507
26:01 57 3 -0.12473 -0.331558 0.690 -0.20683 -1.3519 0.9383
29:02 56 4 -0.13044 -0.199941 0.789 -0.06950 -1.0709 0.9319
31:01 57 3 -0.10097 -0.783003 0.607 -0.68203 -1.8149 0.4508
32:01 56 4 -0.07702 -0.947791 0.092 -0.87077 -1.8470 0.1054
68:01 57 3 -0.16915 0.512457 0.196 0.68161 -0.4512 1.8145
h.pval
01:01 0.910
02:01 0.211
02:06 0.756
03:01 0.138
11:01 0.137
23:01 0.425
24:02 0.618
24:03 0.205
25:01 0.742
26:01 0.725
29:02 0.892
31:01 0.243
32:01 0.086
68:01 0.243
Linear regression (dominant model) with 60 individuals:
glm(y ~ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Linear regression (dominant model) with 60 individuals:
glm(y ~ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Logistic regression (additive model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5%
24:02 109 11 46.8 81.8 3.6030 0.058 0.053 1.7918 0.1585
-----
01:01 95 25 50.5 48.0 0.0000 1.000 1.000 -0.1207 -1.0843
02:01 77 43 48.1 53.5 0.1450 0.703 0.704 0.2137 -0.5289
02:06 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
03:01 111 9 49.5 55.6 0.0000 1.000 1.000 0.2624 -1.1624
11:01 115 5 50.4 40.0 0.0000 1.000 1.000 -0.4418 -2.3074
23:01 117 3 50.4 33.3 0.0000 1.000 1.000 -0.4323 -2.3435
24:03 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
25:01 115 5 51.3 20.0 0.8348 0.361 0.364 -1.4955 -3.7498
26:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
29:02 116 4 50.9 25.0 0.2586 0.611 0.619 -1.1701 -3.4931
31:01 117 3 49.6 66.7 0.0000 1.000 1.000 0.7282 -1.7277
32:01 116 4 48.3 100.0 2.3276 0.127 0.119 17.7092 -3859.2763
68:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
h.97.5% h.pval
24:02 3.4251 0.032*
-----
01:01 0.8430 0.806
02:01 0.9563 0.573
02:06 2836.9268 0.991
03:01 1.6872 0.718
11:01 1.4237 0.643
23:01 1.4789 0.658
24:03 2836.9268 0.991
25:01 0.7588 0.194
26:01 2698.6192 0.990
29:02 1.1530 0.324
31:01 3.1842 0.561
32:01 3894.6947 0.993
68:01 2698.6192 0.990
Logistic regression (recessive model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p h.est
01:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
02:01 52 8 46.2 75 1.298 0.255 0.254 1.253
02:06 60 0 50.0 . . . . .
03:01 60 0 50.0 . . . . .
11:01 60 0 50.0 . . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
24:02 60 0 50.0 . . . . .
24:03 60 0 50.0 . . . . .
25:01 60 0 50.0 . . . . .
26:01 60 0 50.0 . . . . .
29:02 60 0 50.0 . . . . .
31:01 60 0 50.0 . . . . .
32:01 60 0 50.0 . . . . .
68:01 60 0 50.0 . . . . .
h.2.5% h.97.5% h.pval
01:01 -2868.1268 2836.927 0.991
02:01 -0.4379 2.943 0.146
02:06 . . .
03:01 . . .
11:01 . . .
23:01 -2868.1268 2836.927 0.991
24:02 . . .
24:03 . . .
25:01 . . .
26:01 . . .
29:02 . . .
31:01 . . .
32:01 . . .
68:01 . . .
Logistic regression (genotype model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
h1.est h1.2.5% h1.97.5% h1.pval h2.est h2.2.5% h2.97.5%
24:02 1.79176 0.1585 3.4251 0.032* . . .
-----
01:01 0.08701 -0.9600 1.1340 0.871 -15.566 -2868.0929 2836.961
02:01 -0.45474 -1.5524 0.6430 0.417 1.019 -0.7637 2.801
02:06 -15.59997 -2868.1268 2836.9268 0.991 . . .
03:01 0.26236 -1.1624 1.6872 0.718 . . .
11:01 -0.44183 -2.3074 1.4237 0.643 . . .
23:01 16.56607 -4686.4552 4719.5873 0.994 -16.566 -4719.5873 4686.455
24:03 -15.59997 -2868.1268 2836.9268 0.991 . . .
25:01 -1.49549 -3.7498 0.7588 0.194 . . .
26:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
29:02 -1.17007 -3.4931 1.1530 0.324 . . .
31:01 0.72824 -1.7277 3.1842 0.561 . . .
32:01 17.70917 -3859.2763 3894.6947 0.993 . . .
68:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
h2.pval
24:02 .
-----
01:01 0.991
02:01 0.263
02:06 .
03:01 .
11:01 .
23:01 0.994
24:03 .
25:01 .
26:01 .
29:02 .
31:01 .
32:01 .
68:01 .
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:24
=== 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] 2022-10-19 01:00:24, 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] 2022-10-19 01:00:24, 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] 2022-10-19 01:00:24, 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] 2022-10-19 01:00:24, 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 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: HLA-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 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:24) 0%
Predicting (2022-10-19 01:00:24) 100%
Gene: HLA-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 0.032190 0.028032 0.471142
Dosages:
$dosage - num [1:14, 1:26] 2.50e-01 2.16e-04 2.50e-06 7.31e-01 1.11e-14 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:24) 0%
Predicting (2022-10-19 01:00:24) 100%
Gene: HLA-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 0.032190 0.028032 0.471142
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
SNP genotypes:
90 samples X 3932 SNPs
SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
A/G C/T G/T A/C C/G A/T
1567 1510 348 332 111 64
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 5316 SNPs from chromosome 6
SNP genotypes:
90 samples X 5316 SNPs
SNPs range from 25651262bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674
Missing rate per sample:
min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735
Minor allele frequency:
min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475
Allelic information:
A/G C/T G/T A/C C/G A/T
2102 2046 480 471 134 83
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77
# of samples: 60
# of unique HLA alleles: 12
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:24
=== building individual classifier 1, out-of-bag (25/41.7%) ===
[1] 2022-10-19 01:00:24, oob acc: 98.00%, # of SNPs: 13, # of haplo: 20
=== building individual classifier 2, out-of-bag (22/36.7%) ===
[2] 2022-10-19 01:00:24, 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 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Accuracy with training data: 95.00%
Out-of-bag accuracy: 94.45%
Gene: HLA-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 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Genome assembly: hg19
The HIBAG model:
There are 77 SNP predictors in total.
There are 2 individual classifiers.
Summarize the missing fractions of SNP predictors per classifier:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 60
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 0
# of unique HLA genotypes: 0
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 200
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:24
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2022-10-19 01:00:24, oob acc: 89.13%, # of SNPs: 13, # of haplo: 49
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.479687e-10 4.543785e-10 1.412066e-09 1.030115e-03 1.953276e-02 1.350847e-01
Max. Mean SD
6.267394e-01 7.586913e-02 1.432682e-01
Accuracy with training data: 95.00%
Out-of-bag accuracy: 89.13%
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:24
=== building individual classifier 1, out-of-bag (24/40.0%) ===
[1] 2022-10-19 01:00:25, oob acc: 89.58%, # of SNPs: 21, # of haplo: 45
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.388896e-28 1.783259e-28 5.332524e-28 1.674146e-04 2.844265e-03 1.527780e-02
Max. Mean SD
2.750054e-01 2.409134e-02 6.196090e-02
Accuracy with training data: 95.83%
Out-of-bag accuracy: 89.58%
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 30
avg. # of SNPs in an individual classifier: 17.00
(sd: 5.66, min: 13, max: 21, median: 17.00)
avg. # of haplotypes in an individual classifier: 47.00
(sd: 2.83, min: 45, max: 49, median: 47.00)
avg. out-of-bag accuracy: 89.36%
(sd: 0.32%, min: 89.13%, max: 89.58%, median: 89.36%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.740230e-10 1.065174e-09 9.085536e-09 1.634294e-03 1.128283e-02 6.784132e-02
Max. Mean SD
4.508724e-01 4.998023e-02 1.012466e-01
Genome assembly: hg19
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:25
=== 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] 2022-10-19 01:00:25, 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] 2022-10-19 01:00:25, 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] 2022-10-19 01:00:25, 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] 2022-10-19 01:00:25, 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 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: HLA-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 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:25) 0%
Predicting (2022-10-19 01:00:25) 100%
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 09:01
Allelic ambiguity: 09:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num * - A D E F G H I K L M N Q R S T V W Y
1 120 120 . . . . . . . . . . . . . . . . . . .
9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17
44 120 . 25 . . . . . . . . . . . . 95 . . . . .
56 120 . 117 . . . . . . . . . . . . 3 . . . . .
62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . .
63 120 . 105 . . . . . . . . . . 11 4 . . . . . .
65 120 . 105 . . . . 15 . . . . . . . . . . . . .
66 120 . 61 . . . . . . . 59 . . . . . . . . . .
67 120 . 25 . . . . . . . . . . . . . . . 95 . .
70 120 . 99 . . . . . . . . . . . 21 . . . . . .
73 120 . 117 . . . . . . 3 . . . . . . . . . . .
74 120 . 76 . . . . . 44 . . . . . . . . . . . .
76 120 . 32 . . 24 . . . . . . . . . . . . 64 . .
77 120 . 47 . 64 . . . . . . . . . . . 9 . . . .
79 120 . 96 . . . . . . . . . . . . 24 . . . . .
80 120 . 96 . . . . . . 24 . . . . . . . . . . .
81 120 . 96 24 . . . . . . . . . . . . . . . . .
82 120 . 96 . . . . . . . . 24 . . . . . . . . .
83 120 . 96 . . . . . . . . . . . . 24 . . . . .
90 120 . 38 82 . . . . . . . . . . . . . . . . .
95 120 . 61 . . . . . . . . 15 . . . . . . 44 . .
97 120 . 39 . . . . . . . . . 29 . . 52 . . . . .
99 120 . 105 . . . 15 . . . . . . . . . . . . . .
105 120 . 42 . . . . . . . . . . . . . 78 . . . .
107 120 . 76 . . . . . . . . . . . . . . . . 44 .
109 120 . 116 . . . . . . . . 4 . . . . . . . . .
114 120 . 46 . . . . . 59 . . . . . 15 . . . . . .
116 120 . 61 . . . . . . . . . . . . . . . . . 59
127 120 . 58 . . . . . . . 62 . . . . . . . . . .
142 120 . 73 . . . . . . . . . . . . . . 47 . . .
144 120 . 98 . . . . . . . . . . . 22 . . . . . .
145 120 . 73 . . . . . 47 . . . . . . . . . . . .
149 120 . 112 . . . . . . . . . . . . . . 8 . . .
150 120 . 25 95 . . . . . . . . . . . . . . . . .
151 120 . 106 . . . . . . . . . . . . 14 . . . . .
152 120 . 30 . . 17 . . . . . . . . . . . . 73 . .
156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 .
158 120 . 25 95 . . . . . . . . . . . . . . . . .
161 120 . 111 . 9 . . . . . . . . . . . . . . . .
163 120 . 38 . . . . . . . . . . . . . . 82 . . .
166 120 . 39 . . 81 . . . . . . . . . . . . . . .
167 120 . 39 . . . . . . . . . . . . . . . . 81 .
183 120 120 . . . . . . . . . . . . . . . . . . .
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num * - A D E F G H I K L M N Q R S T V W Y
-23 120 120 . . . . . . . . . . . . . . . . . . .
-22 120 120 . . . . . . . . . . . . . . . . . . .
-21 120 120 . . . . . . . . . . . . . . . . . . .
-20 120 120 . . . . . . . . . . . . . . . . . . .
-19 120 120 . . . . . . . . . . . . . . . . . . .
-18 120 120 . . . . . . . . . . . . . . . . . . .
-17 120 120 . . . . . . . . . . . . . . . . . . .
-16 120 120 . . . . . . . . . . . . . . . . . . .
-15 120 120 . . . . . . . . . . . . . . . . . . .
-14 120 120 . . . . . . . . . . . . . . . . . . .
-13 120 120 . . . . . . . . . . . . . . . . . . .
-12 120 120 . . . . . . . . . . . . . . . . . . .
-11 120 120 . . . . . . . . . . . . . . . . . . .
-10 120 120 . . . . . . . . . . . . . . . . . . .
-9 120 120 . . . . . . . . . . . . . . . . . . .
-8 120 120 . . . . . . . . . . . . . . . . . . .
-7 120 120 . . . . . . . . . . . . . . . . . . .
-6 120 120 . . . . . . . . . . . . . . . . . . .
-5 120 120 . . . . . . . . . . . . . . . . . . .
-4 120 120 . . . . . . . . . . . . . . . . . . .
-3 120 120 . . . . . . . . . . . . . . . . . . .
-2 120 120 . . . . . . . . . . . . . . . . . . .
-1 120 120 . . . . . . . . . . . . . . . . . . .
. 120 120 . . . . . . . . . . . . . . . . . . .
1 120 120 . . . . . . . . . . . . . . . . . . .
9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17
44 120 . 25 . . . . . . . . . . . . 95 . . . . .
56 120 . 117 . . . . . . . . . . . . 3 . . . . .
62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . .
63 120 . 105 . . . . . . . . . . 11 4 . . . . . .
65 120 . 105 . . . . 15 . . . . . . . . . . . . .
66 120 . 61 . . . . . . . 59 . . . . . . . . . .
67 120 . 25 . . . . . . . . . . . . . . . 95 . .
70 120 . 99 . . . . . . . . . . . 21 . . . . . .
73 120 . 117 . . . . . . 3 . . . . . . . . . . .
74 120 . 76 . . . . . 44 . . . . . . . . . . . .
76 120 . 32 . . 24 . . . . . . . . . . . . 64 . .
77 120 . 47 . 64 . . . . . . . . . . . 9 . . . .
79 120 . 96 . . . . . . . . . . . . 24 . . . . .
80 120 . 96 . . . . . . 24 . . . . . . . . . . .
81 120 . 96 24 . . . . . . . . . . . . . . . . .
82 120 . 96 . . . . . . . . 24 . . . . . . . . .
83 120 . 96 . . . . . . . . . . . . 24 . . . . .
90 120 . 38 82 . . . . . . . . . . . . . . . . .
95 120 . 61 . . . . . . . . 15 . . . . . . 44 . .
97 120 . 39 . . . . . . . . . 29 . . 52 . . . . .
99 120 . 105 . . . 15 . . . . . . . . . . . . . .
105 120 . 42 . . . . . . . . . . . . . 78 . . . .
107 120 . 76 . . . . . . . . . . . . . . . . 44 .
109 120 . 116 . . . . . . . . 4 . . . . . . . . .
114 120 . 46 . . . . . 59 . . . . . 15 . . . . . .
116 120 . 61 . . . . . . . . . . . . . . . . . 59
127 120 . 58 . . . . . . . 62 . . . . . . . . . .
142 120 . 73 . . . . . . . . . . . . . . 47 . . .
144 120 . 98 . . . . . . . . . . . 22 . . . . . .
145 120 . 73 . . . . . 47 . . . . . . . . . . . .
149 120 . 112 . . . . . . . . . . . . . . 8 . . .
150 120 . 25 95 . . . . . . . . . . . . . . . . .
151 120 . 106 . . . . . . . . . . . . 14 . . . . .
152 120 . 30 . . 17 . . . . . . . . . . . . 73 . .
156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 .
158 120 . 25 95 . . . . . . . . . . . . . . . . .
161 120 . 111 . 9 . . . . . . . . . . . . . . . .
163 120 . 38 . . . . . . . . . . . . . . 82 . . .
166 120 . 39 . . 81 . . . . . . . . . . . . . . .
167 120 . 39 . . . . . . . . . . . . . . . . 81 .
183 120 120 . . . . . . . . . . . . . . . . . . .
184 120 120 . . . . . . . . . . . . . . . . . . .
185 120 120 . . . . . . . . . . . . . . . . . . .
186 120 120 . . . . . . . . . . . . . . . . . . .
187 120 120 . . . . . . . . . . . . . . . . . . .
188 120 120 . . . . . . . . . . . . . . . . . . .
189 120 120 . . . . . . . . . . . . . . . . . . .
190 120 120 . . . . . . . . . . . . . . . . . . .
191 120 120 . . . . . . . . . . . . . . . . . . .
192 120 120 . . . . . . . . . . . . . . . . . . .
193 120 120 . . . . . . . . . . . . . . . . . . .
194 120 120 . . . . . . . . . . . . . . . . . . .
195 120 120 . . . . . . . . . . . . . . . . . . .
196 120 120 . . . . . . . . . . . . . . . . . . .
197 120 120 . . . . . . . . . . . . . . . . . . .
198 120 120 . . . . . . . . . . . . . . . . . . .
199 120 120 . . . . . . . . . . . . . . . . . . .
200 120 120 . . . . . . . . . . . . . . . . . . .
201 120 120 . . . . . . . . . . . . . . . . . . .
202 120 120 . . . . . . . . . . . . . . . . . . .
203 120 120 . . . . . . . . . . . . . . . . . . .
204 120 120 . . . . . . . . . . . . . . . . . . .
205 120 120 . . . . . . . . . . . . . . . . . . .
206 120 120 . . . . . . . . . . . . . . . . . . .
207 120 120 . . . . . . . . . . . . . . . . . . .
208 120 120 . . . . . . . . . . . . . . . . . . .
209 120 120 . . . . . . . . . . . . . . . . . . .
210 120 120 . . . . . . . . . . . . . . . . . . .
211 120 120 . . . . . . . . . . . . . . . . . . .
212 120 120 . . . . . . . . . . . . . . . . . . .
213 120 120 . . . . . . . . . . . . . . . . . . .
214 120 120 . . . . . . . . . . . . . . . . . . .
215 120 120 . . . . . . . . . . . . . . . . . . .
216 120 120 . . . . . . . . . . . . . . . . . . .
217 120 120 . . . . . . . . . . . . . . . . . . .
218 120 120 . . . . . . . . . . . . . . . . . . .
219 120 120 . . . . . . . . . . . . . . . . . . .
220 120 120 . . . . . . . . . . . . . . . . . . .
221 120 120 . . . . . . . . . . . . . . . . . . .
222 120 120 . . . . . . . . . . . . . . . . . . .
223 120 120 . . . . . . . . . . . . . . . . . . .
224 120 120 . . . . . . . . . . . . . . . . . . .
225 120 120 . . . . . . . . . . . . . . . . . . .
226 120 120 . . . . . . . . . . . . . . . . . . .
227 120 120 . . . . . . . . . . . . . . . . . . .
228 120 120 . . . . . . . . . . . . . . . . . . .
229 120 120 . . . . . . . . . . . . . . . . . . .
230 120 120 . . . . . . . . . . . . . . . . . . .
231 120 120 . . . . . . . . . . . . . . . . . . .
232 120 120 . . . . . . . . . . . . . . . . . . .
233 120 120 . . . . . . . . . . . . . . . . . . .
234 120 120 . . . . . . . . . . . . . . . . . . .
235 120 120 . . . . . . . . . . . . . . . . . . .
236 120 120 . . . . . . . . . . . . . . . . . . .
237 120 120 . . . . . . . . . . . . . . . . . . .
238 120 120 . . . . . . . . . . . . . . . . . . .
239 120 120 . . . . . . . . . . . . . . . . . . .
240 120 120 . . . . . . . . . . . . . . . . . . .
241 120 120 . . . . . . . . . . . . . . . . . . .
242 120 120 . . . . . . . . . . . . . . . . . . .
243 120 120 . . . . . . . . . . . . . . . . . . .
244 120 120 . . . . . . . . . . . . . . . . . . .
245 120 120 . . . . . . . . . . . . . . . . . . .
246 120 120 . . . . . . . . . . . . . . . . . . .
247 120 120 . . . . . . . . . . . . . . . . . . .
248 120 120 . . . . . . . . . . . . . . . . . . .
249 120 120 . . . . . . . . . . . . . . . . . . .
250 120 120 . . . . . . . . . . . . . . . . . . .
251 120 120 . . . . . . . . . . . . . . . . . . .
252 120 120 . . . . . . . . . . . . . . . . . . .
253 120 120 . . . . . . . . . . . . . . . . . . .
254 120 120 . . . . . . . . . . . . . . . . . . .
255 120 120 . . . . . . . . . . . . . . . . . . .
256 120 120 . . . . . . . . . . . . . . . . . . .
257 120 120 . . . . . . . . . . . . . . . . . . .
258 120 120 . . . . . . . . . . . . . . . . . . .
259 120 120 . . . . . . . . . . . . . . . . . . .
260 120 120 . . . . . . . . . . . . . . . . . . .
261 120 120 . . . . . . . . . . . . . . . . . . .
262 120 120 . . . . . . . . . . . . . . . . . . .
263 120 120 . . . . . . . . . . . . . . . . . . .
264 120 120 . . . . . . . . . . . . . . . . . . .
265 120 120 . . . . . . . . . . . . . . . . . . .
266 120 120 . . . . . . . . . . . . . . . . . . .
267 120 120 . . . . . . . . . . . . . . . . . . .
268 120 120 . . . . . . . . . . . . . . . . . . .
269 120 120 . . . . . . . . . . . . . . . . . . .
270 120 120 . . . . . . . . . . . . . . . . . . .
271 120 120 . . . . . . . . . . . . . . . . . . .
272 120 120 . . . . . . . . . . . . . . . . . . .
273 120 120 . . . . . . . . . . . . . . . . . . .
274 120 120 . . . . . . . . . . . . . . . . . . .
275 120 120 . . . . . . . . . . . . . . . . . . .
276 120 120 . . . . . . . . . . . . . . . . . . .
277 120 120 . . . . . . . . . . . . . . . . . . .
278 120 120 . . . . . . . . . . . . . . . . . . .
279 120 120 . . . . . . . . . . . . . . . . . . .
280 120 120 . . . . . . . . . . . . . . . . . . .
281 120 120 . . . . . . . . . . . . . . . . . . .
282 120 120 . . . . . . . . . . . . . . . . . . .
283 120 120 . . . . . . . . . . . . . . . . . . .
284 120 120 . . . . . . . . . . . . . . . . . . .
285 120 120 . . . . . . . . . . . . . . . . . . .
286 120 120 . . . . . . . . . . . . . . . . . . .
287 120 120 . . . . . . . . . . . . . . . . . . .
288 120 120 . . . . . . . . . . . . . . . . . . .
289 120 120 . . . . . . . . . . . . . . . . . . .
290 120 120 . . . . . . . . . . . . . . . . . . .
291 120 120 . . . . . . . . . . . . . . . . . . .
292 120 120 . . . . . . . . . . . . . . . . . . .
293 120 120 . . . . . . . . . . . . . . . . . . .
294 120 120 . . . . . . . . . . . . . . . . . . .
295 120 120 . . . . . . . . . . . . . . . . . . .
296 120 120 . . . . . . . . . . . . . . . . . . .
297 120 120 . . . . . . . . . . . . . . . . . . .
298 120 120 . . . . . . . . . . . . . . . . . . .
299 120 120 . . . . . . . . . . . . . . . . . . .
300 120 120 . . . . . . . . . . . . . . . . . . .
301 120 120 . . . . . . . . . . . . . . . . . . .
302 120 120 . . . . . . . . . . . . . . . . . . .
303 120 120 . . . . . . . . . . . . . . . . . . .
304 120 120 . . . . . . . . . . . . . . . . . . .
305 120 120 . . . . . . . . . . . . . . . . . . .
306 120 120 . . . . . . . . . . . . . . . . . . .
307 120 120 . . . . . . . . . . . . . . . . . . .
308 120 120 . . . . . . . . . . . . . . . . . . .
309 120 120 . . . . . . . . . . . . . . . . . . .
310 120 120 . . . . . . . . . . . . . . . . . . .
311 120 120 . . . . . . . . . . . . . . . . . . .
312 120 120 . . . . . . . . . . . . . . . . . . .
313 120 120 . . . . . . . . . . . . . . . . . . .
314 120 120 . . . . . . . . . . . . . . . . . . .
315 120 120 . . . . . . . . . . . . . . . . . . .
316 120 120 . . . . . . . . . . . . . . . . . . .
317 120 120 . . . . . . . . . . . . . . . . . . .
318 120 120 . . . . . . . . . . . . . . . . . . .
319 120 120 . . . . . . . . . . . . . . . . . . .
320 120 120 . . . . . . . . . . . . . . . . . . .
321 120 120 . . . . . . . . . . . . . . . . . . .
322 120 120 . . . . . . . . . . . . . . . . . . .
323 120 120 . . . . . . . . . . . . . . . . . . .
324 120 120 . . . . . . . . . . . . . . . . . . .
325 120 120 . . . . . . . . . . . . . . . . . . .
326 120 120 . . . . . . . . . . . . . . . . . . .
327 120 120 . . . . . . . . . . . . . . . . . . .
328 120 120 . . . . . . . . . . . . . . . . . . .
329 120 120 . . . . . . . . . . . . . . . . . . .
330 120 120 . . . . . . . . . . . . . . . . . . .
331 120 120 . . . . . . . . . . . . . . . . . . .
332 120 120 . . . . . . . . . . . . . . . . . . .
333 120 120 . . . . . . . . . . . . . . . . . . .
334 120 120 . . . . . . . . . . . . . . . . . . .
335 120 120 . . . . . . . . . . . . . . . . . . .
336 120 120 . . . . . . . . . . . . . . . . . . .
337 120 120 . . . . . . . . . . . . . . . . . . .
338 120 120 . . . . . . . . . . . . . . . . . . .
339 120 120 . . . . . . . . . . . . . . . . . . .
340 120 120 . . . . . . . . . . . . . . . . . . .
341 120 120 . . . . . . . . . . . . . . . . . . .
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num * - A D E F G I K L M N P Q R S T Y
5 120 112 . . . . . . . . . . 8 . . . . . .
6 120 20 92 8 . . . . . . . . . . . . . . .
7 112 20 92 . . . . . . . . . . . . . . . .
8 112 20 92 . . . . . . . . . . . . . . . .
9 112 3 76 . . . 33 . . . . . . . . . . . .
10 112 3 109 . . . . . . . . . . . . . . . .
11 112 3 109 . . . . . . . . . . . . . . . .
12 112 3 109 . . . . . . . . . . . . . . . .
13 112 3 93 16 . . . . . . . . . . . . . . .
14 112 3 14 . . . . . . . . 95 . . . . . . .
15 112 3 109 . . . . . . . . . . . . . . . .
16 112 3 109 . . . . . . . . . . . . . . . .
17 112 3 109 . . . . . . . . . . . . . . . .
18 112 3 109 . . . . . . . . . . . . . . . .
19 112 3 109 . . . . . . . . . . . . . . . .
20 112 3 109 . . . . . . . . . . . . . . . .
26 112 . 20 . . . . . . . 76 . . . . . . . 16
28 112 . 100 . . . . . . . . . . . . . 12 . .
30 112 . 24 . . . . . . . . . . . . . 12 . 76
37 112 . 100 . . . . . 12 . . . . . . . . . .
38 112 . 29 83 . . . . . . . . . . . . . . .
45 112 . 96 . . 16 . . . . . . . . . . . . .
46 112 . 100 . . 12 . . . . . . . . . . . . .
47 112 . 100 . . . 12 . . . . . . . . . . . .
52 112 . 100 . . . . . . . 12 . . . . . . . .
53 112 . 54 . . . . . . . 58 . . . . . . . .
55 112 . 57 . . . . . . . 12 . . 43 . . . . .
56 112 . 109 . . . . . . . 3 . . . . . . . .
57 112 . 14 33 64 . . . . . . . . . . . 1 . .
66 112 . 97 . 15 . . . . . . . . . . . . . .
67 112 . 97 . . . . . 15 . . . . . . . . . .
70 112 3 50 . . 3 . . . . . . . . . 56 . . .
71 112 3 14 . 3 . . . . 12 . . . . . . . 80 .
72 112 3 109 . . . . . . . . . . . . . . . .
73 112 3 109 . . . . . . . . . . . . . . . .
74 112 3 17 12 . 80 . . . . . . . . . . . . .
75 112 3 29 . . . . . . . 80 . . . . . . . .
76 112 3 109 . . . . . . . . . . . . . . . .
77 112 3 26 . . . . . . . . . . . . . . 83 .
78 112 3 109 . . . . . . . . . . . . . . . .
79 112 3 109 . . . . . . . . . . . . . . . .
80 112 3 109 . . . . . . . . . . . . . . . .
81 112 3 109 . . . . . . . . . . . . . . . .
82 112 3 109 . . . . . . . . . . . . . . . .
83 112 3 109 . . . . . . . . . . . . . . . .
84 112 3 51 . . . . . . . . . . . 58 . . . .
85 112 3 51 . . . . . . . 58 . . . . . . . .
86 112 3 50 . . 58 . 1 . . . . . . . . . . .
87 112 3 15 . . . 36 . . . 58 . . . . . . . .
88 112 3 109 . . . . . . . . . . . . . . . .
89 112 3 51 . . . . . . . . . . . . . . 58 .
90 112 3 51 . . . . . . . . . . . . . . 58 .
91 112 3 109 . . . . . . . . . . . . . . . .
92 112 3 109 . . . . . . . . . . . . . . . .
93 112 3 109 . . . . . . . . . . . . . . . .
94 112 17 95 . . . . . . . . . . . . . . . .
95 112 112 . . . . . . . . . . . . . . . . .
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num * - A D E F G I K L M N P Q R S T Y
-31 120 112 . . . . . . . . . . 8 . . . . . .
-30 120 112 . 8 . . . . . . . . . . . . . . .
-29 112 112 . . . . . . . . . . . . . . . . .
-28 112 112 . . . . . . . . . . . . . . . . .
-27 112 112 . . . . . . . . . . . . . . . . .
-26 112 112 . . . . . . . . . . . . . . . . .
-25 112 112 . . . . . . . . . . . . . . . . .
-24 112 112 . . . . . . . . . . . . . . . . .
-23 112 112 . . . . . . . . . . . . . . . . .
-22 112 112 . . . . . . . . . . . . . . . . .
-21 112 112 . . . . . . . . . . . . . . . . .
-20 112 112 . . . . . . . . . . . . . . . . .
-19 112 112 . . . . . . . . . . . . . . . . .
-18 112 112 . . . . . . . . . . . . . . . . .
-17 112 112 . . . . . . . . . . . . . . . . .
-16 112 112 . . . . . . . . . . . . . . . . .
-15 112 112 . . . . . . . . . . . . . . . . .
-14 112 112 . . . . . . . . . . . . . . . . .
-13 112 112 . . . . . . . . . . . . . . . . .
-12 112 112 . . . . . . . . . . . . . . . . .
-11 112 112 . . . . . . . . . . . . . . . . .
-10 112 112 . . . . . . . . . . . . . . . . .
-9 112 112 . . . . . . . . . . . . . . . . .
-8 112 112 . . . . . . . . . . . . . . . . .
-7 112 112 . . . . . . . . . . . . . . . . .
-6 112 112 . . . . . . . . . . . . . . . . .
-5 112 112 . . . . . . . . . . . . . . . . .
-4 112 112 . . . . . . . . . . . . . . . . .
-3 112 112 . . . . . . . . . . . . . . . . .
-2 112 112 . . . . . . . . . . . . . . . . .
-1 112 112 . . . . . . . . . . . . . . . . .
. 112 112 . . . . . . . . . . . . . . . . .
1 112 112 . . . . . . . . . . . . . . . . .
2 112 112 . . . . . . . . . . . . . . . . .
3 112 112 . . . . . . . . . . . . . . . . .
4 112 112 . . . . . . . . . . . . . . . . .
5 112 112 . . . . . . . . . . . . . . . . .
6 112 20 92 . . . . . . . . . . . . . . . .
7 112 20 92 . . . . . . . . . . . . . . . .
8 112 20 92 . . . . . . . . . . . . . . . .
9 112 3 76 . . . 33 . . . . . . . . . . . .
10 112 3 109 . . . . . . . . . . . . . . . .
11 112 3 109 . . . . . . . . . . . . . . . .
12 112 3 109 . . . . . . . . . . . . . . . .
13 112 3 93 16 . . . . . . . . . . . . . . .
14 112 3 14 . . . . . . . . 95 . . . . . . .
15 112 3 109 . . . . . . . . . . . . . . . .
16 112 3 109 . . . . . . . . . . . . . . . .
17 112 3 109 . . . . . . . . . . . . . . . .
18 112 3 109 . . . . . . . . . . . . . . . .
19 112 3 109 . . . . . . . . . . . . . . . .
20 112 3 109 . . . . . . . . . . . . . . . .
26 112 . 20 . . . . . . . 76 . . . . . . . 16
28 112 . 100 . . . . . . . . . . . . . 12 . .
30 112 . 24 . . . . . . . . . . . . . 12 . 76
37 112 . 100 . . . . . 12 . . . . . . . . . .
38 112 . 29 83 . . . . . . . . . . . . . . .
45 112 . 96 . . 16 . . . . . . . . . . . . .
46 112 . 100 . . 12 . . . . . . . . . . . . .
47 112 . 100 . . . 12 . . . . . . . . . . . .
52 112 . 100 . . . . . . . 12 . . . . . . . .
53 112 . 54 . . . . . . . 58 . . . . . . . .
55 112 . 57 . . . . . . . 12 . . 43 . . . . .
56 112 . 109 . . . . . . . 3 . . . . . . . .
57 112 . 14 33 64 . . . . . . . . . . . 1 . .
66 112 . 97 . 15 . . . . . . . . . . . . . .
67 112 . 97 . . . . . 15 . . . . . . . . . .
70 112 3 50 . . 3 . . . . . . . . . 56 . . .
71 112 3 14 . 3 . . . . 12 . . . . . . . 80 .
72 112 3 109 . . . . . . . . . . . . . . . .
73 112 3 109 . . . . . . . . . . . . . . . .
74 112 3 17 12 . 80 . . . . . . . . . . . . .
75 112 3 29 . . . . . . . 80 . . . . . . . .
76 112 3 109 . . . . . . . . . . . . . . . .
77 112 3 26 . . . . . . . . . . . . . . 83 .
78 112 3 109 . . . . . . . . . . . . . . . .
79 112 3 109 . . . . . . . . . . . . . . . .
80 112 3 109 . . . . . . . . . . . . . . . .
81 112 3 109 . . . . . . . . . . . . . . . .
82 112 3 109 . . . . . . . . . . . . . . . .
83 112 3 109 . . . . . . . . . . . . . . . .
84 112 3 51 . . . . . . . . . . . 58 . . . .
85 112 3 51 . . . . . . . 58 . . . . . . . .
86 112 3 50 . . 58 . 1 . . . . . . . . . . .
87 112 3 15 . . . 36 . . . 58 . . . . . . . .
88 112 3 109 . . . . . . . . . . . . . . . .
89 112 3 51 . . . . . . . . . . . . . . 58 .
90 112 3 51 . . . . . . . . . . . . . . 58 .
91 112 3 109 . . . . . . . . . . . . . . . .
92 112 3 109 . . . . . . . . . . . . . . . .
93 112 3 109 . . . . . . . . . . . . . . . .
94 112 17 95 . . . . . . . . . . . . . . . .
95 112 112 . . . . . . . . . . . . . . . . .
96 112 112 . . . . . . . . . . . . . . . . .
97 112 112 . . . . . . . . . . . . . . . . .
98 112 112 . . . . . . . . . . . . . . . . .
99 112 112 . . . . . . . . . . . . . . . . .
100 112 112 . . . . . . . . . . . . . . . . .
101 112 112 . . . . . . . . . . . . . . . . .
102 112 112 . . . . . . . . . . . . . . . . .
103 112 112 . . . . . . . . . . . . . . . . .
104 112 112 . . . . . . . . . . . . . . . . .
105 112 112 . . . . . . . . . . . . . . . . .
106 112 112 . . . . . . . . . . . . . . . . .
107 112 112 . . . . . . . . . . . . . . . . .
108 112 112 . . . . . . . . . . . . . . . . .
109 112 112 . . . . . . . . . . . . . . . . .
110 112 112 . . . . . . . . . . . . . . . . .
111 112 112 . . . . . . . . . . . . . . . . .
112 112 112 . . . . . . . . . . . . . . . . .
113 112 112 . . . . . . . . . . . . . . . . .
114 112 112 . . . . . . . . . . . . . . . . .
115 112 112 . . . . . . . . . . . . . . . . .
116 112 112 . . . . . . . . . . . . . . . . .
117 112 112 . . . . . . . . . . . . . . . . .
118 112 112 . . . . . . . . . . . . . . . . .
119 112 112 . . . . . . . . . . . . . . . . .
120 112 112 . . . . . . . . . . . . . . . . .
121 112 112 . . . . . . . . . . . . . . . . .
122 112 112 . . . . . . . . . . . . . . . . .
123 112 112 . . . . . . . . . . . . . . . . .
124 112 112 . . . . . . . . . . . . . . . . .
125 112 112 . . . . . . . . . . . . . . . . .
126 112 112 . . . . . . . . . . . . . . . . .
127 112 112 . . . . . . . . . . . . . . . . .
128 112 112 . . . . . . . . . . . . . . . . .
129 112 112 . . . . . . . . . . . . . . . . .
130 112 112 . . . . . . . . . . . . . . . . .
131 112 112 . . . . . . . . . . . . . . . . .
132 112 112 . . . . . . . . . . . . . . . . .
133 112 112 . . . . . . . . . . . . . . . . .
134 112 112 . . . . . . . . . . . . . . . . .
135 112 112 . . . . . . . . . . . . . . . . .
136 112 112 . . . . . . . . . . . . . . . . .
137 112 112 . . . . . . . . . . . . . . . . .
138 112 112 . . . . . . . . . . . . . . . . .
139 112 112 . . . . . . . . . . . . . . . . .
140 112 112 . . . . . . . . . . . . . . . . .
141 112 112 . . . . . . . . . . . . . . . . .
142 112 112 . . . . . . . . . . . . . . . . .
143 112 112 . . . . . . . . . . . . . . . . .
144 112 112 . . . . . . . . . . . . . . . . .
145 112 112 . . . . . . . . . . . . . . . . .
146 112 112 . . . . . . . . . . . . . . . . .
147 112 112 . . . . . . . . . . . . . . . . .
148 112 112 . . . . . . . . . . . . . . . . .
149 112 112 . . . . . . . . . . . . . . . . .
150 112 112 . . . . . . . . . . . . . . . . .
151 112 112 . . . . . . . . . . . . . . . . .
152 112 112 . . . . . . . . . . . . . . . . .
153 112 112 . . . . . . . . . . . . . . . . .
154 112 112 . . . . . . . . . . . . . . . . .
155 112 112 . . . . . . . . . . . . . . . . .
156 112 112 . . . . . . . . . . . . . . . . .
157 112 112 . . . . . . . . . . . . . . . . .
158 112 112 . . . . . . . . . . . . . . . . .
159 112 112 . . . . . . . . . . . . . . . . .
160 112 112 . . . . . . . . . . . . . . . . .
161 112 112 . . . . . . . . . . . . . . . . .
162 112 112 . . . . . . . . . . . . . . . . .
163 112 112 . . . . . . . . . . . . . . . . .
164 112 112 . . . . . . . . . . . . . . . . .
165 112 112 . . . . . . . . . . . . . . . . .
166 112 112 . . . . . . . . . . . . . . . . .
167 112 112 . . . . . . . . . . . . . . . . .
168 112 112 . . . . . . . . . . . . . . . . .
169 112 112 . . . . . . . . . . . . . . . . .
170 112 112 . . . . . . . . . . . . . . . . .
171 112 112 . . . . . . . . . . . . . . . . .
172 112 112 . . . . . . . . . . . . . . . . .
173 112 112 . . . . . . . . . . . . . . . . .
174 112 112 . . . . . . . . . . . . . . . . .
175 112 112 . . . . . . . . . . . . . . . . .
176 112 112 . . . . . . . . . . . . . . . . .
177 112 112 . . . . . . . . . . . . . . . . .
178 112 112 . . . . . . . . . . . . . . . . .
179 112 112 . . . . . . . . . . . . . . . . .
180 112 112 . . . . . . . . . . . . . . . . .
181 112 112 . . . . . . . . . . . . . . . . .
182 112 112 . . . . . . . . . . . . . . . . .
183 112 112 . . . . . . . . . . . . . . . . .
184 112 112 . . . . . . . . . . . . . . . . .
185 112 112 . . . . . . . . . . . . . . . . .
186 112 112 . . . . . . . . . . . . . . . . .
187 112 112 . . . . . . . . . . . . . . . . .
188 112 112 . . . . . . . . . . . . . . . . .
189 112 112 . . . . . . . . . . . . . . . . .
190 112 112 . . . . . . . . . . . . . . . . .
191 112 112 . . . . . . . . . . . . . . . . .
192 112 112 . . . . . . . . . . . . . . . . .
193 112 112 . . . . . . . . . . . . . . . . .
194 112 112 . . . . . . . . . . . . . . . . .
195 112 112 . . . . . . . . . . . . . . . . .
196 112 112 . . . . . . . . . . . . . . . . .
197 112 112 . . . . . . . . . . . . . . . . .
198 112 112 . . . . . . . . . . . . . . . . .
199 112 112 . . . . . . . . . . . . . . . . .
200 112 112 . . . . . . . . . . . . . . . . .
201 112 112 . . . . . . . . . . . . . . . . .
202 112 112 . . . . . . . . . . . . . . . . .
203 112 112 . . . . . . . . . . . . . . . . .
204 112 112 . . . . . . . . . . . . . . . . .
205 112 112 . . . . . . . . . . . . . . . . .
206 112 112 . . . . . . . . . . . . . . . . .
207 112 112 . . . . . . . . . . . . . . . . .
208 112 112 . . . . . . . . . . . . . . . . .
209 112 112 . . . . . . . . . . . . . . . . .
210 112 112 . . . . . . . . . . . . . . . . .
211 112 112 . . . . . . . . . . . . . . . . .
212 112 112 . . . . . . . . . . . . . . . . .
213 112 112 . . . . . . . . . . . . . . . . .
214 112 112 . . . . . . . . . . . . . . . . .
215 112 112 . . . . . . . . . . . . . . . . .
216 112 112 . . . . . . . . . . . . . . . . .
217 112 112 . . . . . . . . . . . . . . . . .
218 112 112 . . . . . . . . . . . . . . . . .
219 112 112 . . . . . . . . . . . . . . . . .
220 112 112 . . . . . . . . . . . . . . . . .
221 112 112 . . . . . . . . . . . . . . . . .
222 112 112 . . . . . . . . . . . . . . . . .
223 112 112 . . . . . . . . . . . . . . . . .
224 112 112 . . . . . . . . . . . . . . . . .
225 112 112 . . . . . . . . . . . . . . . . .
226 112 112 . . . . . . . . . . . . . . . . .
227 112 112 . . . . . . . . . . . . . . . . .
228 112 112 . . . . . . . . . . . . . . . . .
229 112 112 . . . . . . . . . . . . . . . . .
230 112 112 . . . . . . . . . . . . . . . . .
231 112 112 . . . . . . . . . . . . . . . . .
232 112 112 . . . . . . . . . . . . . . . . .
233 112 112 . . . . . . . . . . . . . . . . .
234 112 112 . . . . . . . . . . . . . . . . .
235 112 112 . . . . . . . . . . . . . . . . .
236 112 112 . . . . . . . . . . . . . . . . .
237 112 112 . . . . . . . . . . . . . . . . .
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:27
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2022-10-19 01:00:27, oob acc: 89.13%, # of SNPs: 13, # of haplo: 49
=== building individual classifier 2, out-of-bag (24/40.0%) ===
[2] 2022-10-19 01:00:27, oob acc: 89.58%, # of SNPs: 21, # of haplo: 45
=== building individual classifier 3, out-of-bag (24/40.0%) ===
[3] 2022-10-19 01:00:28, oob acc: 91.67%, # of SNPs: 19, # of haplo: 38
=== building individual classifier 4, out-of-bag (19/31.7%) ===
[4] 2022-10-19 01:00:28, oob acc: 97.37%, # of SNPs: 17, # of haplo: 44
=== building individual classifier 5, out-of-bag (22/36.7%) ===
[5] 2022-10-19 01:00:28, oob acc: 90.91%, # of SNPs: 15, # of haplo: 60
=== building individual classifier 6, out-of-bag (23/38.3%) ===
[6] 2022-10-19 01:00:28, oob acc: 93.48%, # of SNPs: 19, # of haplo: 79
=== building individual classifier 7, out-of-bag (18/30.0%) ===
[7] 2022-10-19 01:00:28, oob acc: 100.00%, # of SNPs: 18, # of haplo: 47
=== building individual classifier 8, out-of-bag (20/33.3%) ===
[8] 2022-10-19 01:00:28, oob acc: 95.00%, # of SNPs: 12, # of haplo: 23
=== building individual classifier 9, out-of-bag (23/38.3%) ===
[9] 2022-10-19 01:00:28, oob acc: 100.00%, # of SNPs: 14, # of haplo: 48
=== building individual classifier 10, out-of-bag (22/36.7%) ===
[10] 2022-10-19 01:00:28, oob acc: 100.00%, # of SNPs: 16, # of haplo: 38
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001840333 0.0001887449 0.0002311489 0.0033116516 0.0112003631 0.0256476008
Max. Mean SD
0.4572385232 0.0419991093 0.1000703516
Accuracy with training data: 98.33%
Out-of-bag accuracy: 94.71%
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 10
total # of SNPs used: 106
avg. # of SNPs in an individual classifier: 16.40
(sd: 2.91, min: 12, max: 21, median: 16.50)
avg. # of haplotypes in an individual classifier: 47.10
(sd: 14.72, min: 23, max: 79, median: 46.00)
avg. out-of-bag accuracy: 94.71%
(sd: 4.39%, min: 89.13%, max: 100.00%, median: 94.24%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001840333 0.0001887449 0.0002311489 0.0033116516 0.0112003631 0.0256476008
Max. Mean SD
0.4572385232 0.0419991093 0.1000703516
Genome assembly: hg19
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
Loading required namespace: gdsfmt
Loading required namespace: SNPRelate
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU_Chr6.gds'
Import 1668 SNPs within the xMHC region on chromosome 6
2 SNPs with invalid alleles have been removed.
SNP genotypes:
165 samples X 1666 SNPs
SNPs range from 28837960bp to 33524089bp on hg18
Missing rate per SNP:
min: 0, max: 0.0484848, mean: 0.00175707, median: 0, sd: 0.00515153
Missing rate per sample:
min: 0, max: 0.0120048, mean: 0.00175707, median: 0.00120048, sd: 0.00210091
Minor allele frequency:
min: 0, max: 0.5, mean: 0.19767, median: 0.175758, sd: 0.150469
Allelic information:
A/G T/C G/A C/T T/G A/C C/A G/T A/T C/G G/C T/A
412 318 299 285 79 76 75 56 20 19 16 11
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
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
No allelic strand or A/B allele is flipped.
SNP genotypes:
150 samples X 1214 SNPs
SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841
Missing rate per sample:
min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119
Minor allele frequency:
min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855
Allelic information:
A/G C/T G/T A/C
505 496 109 104
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
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
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
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
SNP genotypes:
60 samples X 1197 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446
Missing rate per sample:
min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786
Minor allele frequency:
min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338
Allelic information:
A/G C/T A/C G/T
511 476 105 105
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
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'F:/biocbuild/bbs-3.15-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
SNP genotypes:
90 samples X 3932 SNPs
SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
A/G C/T G/T A/C C/G A/T
1567 1510 348 332 111 64
No allelic strand or A/B allele is flipped.
SNP genotypes:
60 samples X 1214 SNPs
SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381
Missing rate per sample:
min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373
Minor allele frequency:
min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833
Allelic information:
A/G C/T G/T A/C
505 496 109 104
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
MAF filter (>=0.01), excluding 9 SNP(s)
using the default genome assembly (assembly="hg19")
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
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
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:31
=== building individual classifier 1, out-of-bag (25/41.7%) ===
[1] 2022-10-19 01:00:31, oob acc: 92.00%, # of SNPs: 24, # of haplo: 29
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02
Max. Mean SD
9.105734e-02 2.054649e-02 2.598603e-02
Accuracy with training data: 96.67%
Out-of-bag accuracy: 92.00%
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:31
=== building individual classifier 1, out-of-bag (20/33.3%) ===
[1] 2022-10-19 01:00:31, oob acc: 97.50%, # of SNPs: 18, # of haplo: 34
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02
Max. Mean SD
9.784316e-02 1.490504e-02 1.947399e-02
Accuracy with training data: 97.50%
Out-of-bag accuracy: 97.50%
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:31
=== building individual classifier 1, out-of-bag (18/30.0%) ===
[1] 2022-10-19 01:00:31, oob acc: 88.89%, # of SNPs: 14, # of haplo: 38
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02
Max. Mean SD
1.808372e-01 2.422083e-02 3.699146e-02
Accuracy with training data: 95.83%
Out-of-bag accuracy: 88.89%
Gene: HLA-C
Training dataset: 60 samples X 83 SNPs
# of HLA alleles: 17
# of individual classifiers: 3
total # of SNPs used: 40
avg. # of SNPs in an individual classifier: 18.67
(sd: 5.03, min: 14, max: 24, median: 18.00)
avg. # of haplotypes in an individual classifier: 33.67
(sd: 4.51, min: 29, max: 38, median: 34.00)
avg. out-of-bag accuracy: 92.80%
(sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02
Max. Mean SD
1.210500e-01 1.989079e-02 2.507466e-02
Genome assembly: hg19
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77
# of samples: 60
# of unique HLA alleles: 12
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:31
=== building individual classifier 1, out-of-bag (25/41.7%) ===
[1] 2022-10-19 01:00:31, oob acc: 98.00%, # of SNPs: 13, # of haplo: 20
=== building individual classifier 2, out-of-bag (22/36.7%) ===
[2] 2022-10-19 01:00:31, 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 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Accuracy with training data: 95.00%
Out-of-bag accuracy: 94.45%
Gene: HLA-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 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Genome assembly: hg19
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:31
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2022-10-19 01:00:31, oob acc: 89.13%, # of SNPs: 13, # of haplo: 49
=== building individual classifier 2, out-of-bag (24/40.0%) ===
[2] 2022-10-19 01:00:31, oob acc: 89.58%, # of SNPs: 21, # of haplo: 45
=== building individual classifier 3, out-of-bag (24/40.0%) ===
[3] 2022-10-19 01:00:31, oob acc: 91.67%, # of SNPs: 19, # of haplo: 38
=== building individual classifier 4, out-of-bag (19/31.7%) ===
[4] 2022-10-19 01:00:32, oob acc: 97.37%, # of SNPs: 17, # of haplo: 44
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001388903 0.0001450361 0.0002003484 0.0024541490 0.0129509766 0.0377797811
Max. Mean SD
0.4302198785 0.0406177754 0.0924307480
Accuracy with training data: 98.33%
Out-of-bag accuracy: 91.94%
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 57
avg. # of SNPs in an individual classifier: 17.50
(sd: 3.42, min: 13, max: 21, median: 18.00)
avg. # of haplotypes in an individual classifier: 44.00
(sd: 4.55, min: 38, max: 49, median: 44.50)
avg. out-of-bag accuracy: 91.94%
(sd: 3.79%, min: 89.13%, max: 97.37%, median: 90.62%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001388903 0.0001450361 0.0002003484 0.0024541490 0.0129509766 0.0377797811
Max. Mean SD
0.4302198785 0.0406177754 0.0924307480
Genome assembly: hg19
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 57
avg. # of SNPs in an individual classifier: 17.50
(sd: 3.42, min: 13, max: 21, median: 18.00)
avg. # of haplotypes in an individual classifier: 44.00
(sd: 4.55, min: 38, max: 49, median: 44.50)
avg. out-of-bag accuracy: 91.94%
(sd: 3.79%, min: 89.13%, max: 97.37%, median: 90.62%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001388903 0.0001450361 0.0002003484 0.0024541490 0.0129509766 0.0377797811
Max. Mean SD
0.4302198785 0.0406177754 0.0924307480
Genome assembly: hg19
Wed Oct 19 01:00:32 2022, passing the 1/4 classifiers.
Wed Oct 19 01:00:32 2022, passing the 2/4 classifiers.
Wed Oct 19 01:00:32 2022, passing the 3/4 classifiers.
Wed Oct 19 01:00:32 2022, passing the 4/4 classifiers.
Allele Num. Freq. CR ACC SEN SPE PPV NPV Miscall
Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
----
Overall accuracy: 91.9%, Call rate: 100.0%
01:01 25 0.2083 100.0 98.4 100.0 97.9 95.0 100.0 --
02:01 43 0.3583 100.0 98.9 100.0 98.5 96.8 100.0 --
02:06 1 0.0083 25.0 97.9 0.0 100.0 -- 97.9 02:01 (100)
03:01 9 0.0750 100.0 100.0 100.0 100.0 100.0 100.0 --
11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 3 0.0250 75.0 97.9 66.7 100.0 100.0 97.9 24:02 (100)
24:02 11 0.0917 100.0 97.9 100.0 97.7 87.5 100.0 --
24:03 1 0.0083 50.0 97.9 0.0 100.0 -- 97.9 02:01 (50)
25:01 5 0.0417 100.0 98.3 100.0 98.2 74.2 100.0 --
26:01 3 0.0250 100.0 98.8 62.5 100.0 100.0 98.8 25:01 (100)
29:02 4 0.0333 100.0 97.8 75.0 100.0 100.0 97.8 02:06 (75)
31:01 3 0.0250 75.0 97.9 66.7 100.0 100.0 97.9 01:01 (100)
32:01 4 0.0333 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 3 0.0250 100.0 100.0 100.0 100.0 100.0 100.0 --
\title{Imputation Evaluation}
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{longtable}
\begin{document}
\maketitle
\setlength{\LTcapwidth}{6.5in}
% -------- BEGIN TABLE --------
\begin{longtable}{rrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{10}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{10}{l}{\it Overall accuracy: 91.9\%, Call rate: 100.0\%} \\
01:01 & 25 & 0.2083 & 100.0 & 98.4 & 100.0 & 97.9 & 95.0 & 100.0 & -- \\
02:01 & 43 & 0.3583 & 100.0 & 98.9 & 100.0 & 98.5 & 96.8 & 100.0 & -- \\
02:06 & 1 & 0.0083 & 25.0 & 97.9 & 0.0 & 100.0 & -- & 97.9 & 02:01 (100) \\
03:01 & 9 & 0.0750 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 3 & 0.0250 & 75.0 & 97.9 & 66.7 & 100.0 & 100.0 & 97.9 & 24:02 (100) \\
24:02 & 11 & 0.0917 & 100.0 & 97.9 & 100.0 & 97.7 & 87.5 & 100.0 & -- \\
24:03 & 1 & 0.0083 & 50.0 & 97.9 & 0.0 & 100.0 & -- & 97.9 & 02:01 (50) \\
25:01 & 5 & 0.0417 & 100.0 & 98.3 & 100.0 & 98.2 & 74.2 & 100.0 & -- \\
26:01 & 3 & 0.0250 & 100.0 & 98.8 & 62.5 & 100.0 & 100.0 & 98.8 & 25:01 (100) \\
29:02 & 4 & 0.0333 & 100.0 & 97.8 & 75.0 & 100.0 & 100.0 & 97.8 & 02:06 (75) \\
31:01 & 3 & 0.0250 & 75.0 & 97.9 & 66.7 & 100.0 & 100.0 & 97.9 & 01:01 (100) \\
32:01 & 4 & 0.0333 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 3 & 0.0250 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------
\end{document}
<!DOCTYPE html>
<html>
<head>
<title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1" CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="10">
<i> Overall accuracy: 91.9%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>25</td> <td>0.2083</td> <td>100.0</td> <td>98.4</td> <td>100.0</td> <td>97.9</td> <td>95.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>43</td> <td>0.3583</td> <td>100.0</td> <td>98.9</td> <td>100.0</td> <td>98.5</td> <td>96.8</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0083</td> <td>25.0</td> <td>97.9</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.9</td> <td>02:01 (100)</td>
</tr>
<tr>
<td>03:01</td> <td>9</td> <td>0.0750</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>97.9</td> <td>66.7</td> <td>100.0</td> <td>100.0</td> <td>97.9</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>11</td> <td>0.0917</td> <td>100.0</td> <td>97.9</td> <td>100.0</td> <td>97.7</td> <td>87.5</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0083</td> <td>50.0</td> <td>97.9</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.9</td> <td>02:01 (50)</td>
</tr>
<tr>
<td>25:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>98.3</td> <td>100.0</td> <td>98.2</td> <td>74.2</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.8</td> <td>62.5</td> <td>100.0</td> <td>100.0</td> <td>98.8</td> <td>25:01 (100)</td>
</tr>
<tr>
<td>29:02</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>97.8</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>97.8</td> <td>02:06 (75)</td>
</tr>
<tr>
<td>31:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>97.9</td> <td>66.7</td> <td>100.0</td> <td>100.0</td> <td>97.9</td> <td>01:01 (100)</td>
</tr>
<tr>
<td>32:01</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>
</body>
</html>
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Building a HIBAG model:
4 individual classifiers
run in parallel with 1 compute node
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 2
[-] 2022-10-19 01:00:32
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2022-10-19 01:00:32, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2022-10-19 01:00:32, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
[3] 2022-10-19 01:00:32, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
[4] 2022-10-19 01:00:32, 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 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Building a HIBAG model:
4 individual classifiers
run in parallel with 2 compute nodes
autosave to 'tmp_model.RData'
[-] 2022-10-19 01:00:32
[1] 2022-10-19 01:00:32, worker 2, # of SNPs: 12, # of haplo: 53, oob acc: 90.9%
==Saved== #1, avg oob acc: 90.91%, sd: NA%, min: 90.91%, max: 90.91%
[2] 2022-10-19 01:00:32, worker 1, # of SNPs: 16, # of haplo: 65, oob acc: 90.9%
==Saved== #2, avg oob acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91%
[3] 2022-10-19 01:00:32, worker 2, # of SNPs: 14, # of haplo: 26, oob acc: 90.9%
Stop "job 2".
==Saved== #3, avg oob acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91%
[4] 2022-10-19 01:00:32, worker 1, # of SNPs: 17, # of haplo: 39, oob acc: 91.7%
Stop "job 1".
==Saved== #4, avg oob acc: 91.10%, sd: 0.38%, min: 90.91%, max: 91.67%
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003784648 0.0003819148 0.0004129651 0.0019631167 0.0085707691 0.0266411005
Max. Mean SD
0.4365845543 0.0464002581 0.1100966316
Accuracy with training data: 98.53%
Out-of-bag accuracy: 91.10%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 52
avg. # of SNPs in an individual classifier: 14.75
(sd: 2.22, min: 12, max: 17, median: 15.00)
avg. # of haplotypes in an individual classifier: 45.75
(sd: 16.92, min: 26, max: 65, median: 46.00)
avg. out-of-bag accuracy: 91.10%
(sd: 0.38%, min: 90.91%, max: 91.67%, median: 90.91%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003784648 0.0003819148 0.0004129651 0.0019631167 0.0085707691 0.0266411005
Max. Mean SD
0.4365845543 0.0464002581 0.1100966316
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:32) 0%
Predicting (2022-10-19 01:00:32) 100%
Gene: HLA-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%) 2 (7.7%) 2 (7.7%) 21 (80.8%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.001535 0.003090 0.029469 0.025921 0.436585
Dosages:
$dosage - num [1:14, 1:26] 1.84e-06 1.03e-01 2.50e-11 8.96e-01 2.08e-15 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
run in parallel with 2 compute nodes
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
Gene: HLA-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%) 2 (7.7%) 2 (7.7%) 21 (80.8%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.001535 0.003090 0.029469 0.025921 0.436585
Dosages:
$dosage - num [1:14, 1:26] 1.84e-06 1.03e-01 2.50e-11 8.96e-01 2.08e-15 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:33
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2022-10-19 01:00:33, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2022-10-19 01:00:33, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.275953e-07 1.742509e-05 1.731025e-04 2.811482e-03 8.650597e-03 1.989621e-02
Max. Mean SD
5.990492e-02 1.464043e-02 1.658610e-02
Accuracy with training data: 100.00%
Out-of-bag accuracy: 94.95%
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:33
=== building individual classifier 1, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[1] 2022-10-19 01:00:33, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[2] 2022-10-19 01:00:33, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002703521 0.0002971139 0.0005379705 0.0036521203 0.0131584084 0.0415528465
Max. Mean SD
0.5087413114 0.0420589840 0.0891771528
Accuracy with training data: 97.06%
Out-of-bag accuracy: 90.80%
HIBAG model for HLA-A:
2 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
by voting from all individual classifiers
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:33) 0%
Predicting (2022-10-19 01:00:33) 100%
HIBAG model for HLA-A:
2 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
by voting from all individual classifiers
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:33) 0%
Predicting (2022-10-19 01:00:33) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:33
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2022-10-19 01:00:33, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2022-10-19 01:00:33, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[3] 2022-10-19 01:00:33, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 4, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[4] 2022-10-19 01:00:33, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Accuracy with training data: 97.06%
Out-of-bag accuracy: 92.87%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 49
avg. # of SNPs in an individual classifier: 13.25
(sd: 1.71, min: 11, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 47.25
(sd: 28.72, min: 30, max: 90, median: 34.50)
avg. out-of-bag accuracy: 92.87%
(sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:34) 0%
Predicting (2022-10-19 01:00:34) 100%
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 13
# of SNPs: 158
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:34
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 141, loss: 378.06, oob acc: 52.08%, # of haplo: 14
2, SNP: 74, loss: 262.497, oob acc: 58.33%, # of haplo: 15
3, SNP: 78, loss: 162.497, oob acc: 68.75%, # of haplo: 19
4, SNP: 118, loss: 70.0426, oob acc: 72.92%, # of haplo: 23
5, SNP: 82, loss: 45.8279, oob acc: 83.33%, # of haplo: 23
6, SNP: 95, loss: 35.4414, oob acc: 89.58%, # of haplo: 27
7, SNP: 89, loss: 32.6134, oob acc: 89.58%, # of haplo: 35
8, SNP: 83, loss: 31.7921, oob acc: 89.58%, # of haplo: 51
9, SNP: 151, loss: 31.0653, oob acc: 91.67%, # of haplo: 55
10, SNP: 107, loss: 30.2634, oob acc: 91.67%, # of haplo: 58
11, SNP: 154, loss: 18.9485, oob acc: 91.67%, # of haplo: 58
12, SNP: 93, loss: 17.5074, oob acc: 93.75%, # of haplo: 58
13, SNP: 17, loss: 17.02, oob acc: 93.75%, # of haplo: 60
14, SNP: 15, loss: 14.3651, oob acc: 93.75%, # of haplo: 61
[1] 2022-10-19 01:00:34, oob acc: 93.75%, # of SNPs: 14, # of haplo: 61
=== building individual classifier 2, out-of-bag (19/31.7%) ===
1, SNP: 94, loss: 403.365, oob acc: 63.16%, # of haplo: 15
2, SNP: 82, loss: 294.053, oob acc: 71.05%, # of haplo: 18
3, SNP: 57, loss: 226.142, oob acc: 76.32%, # of haplo: 23
4, SNP: 155, loss: 197.199, oob acc: 84.21%, # of haplo: 29
5, SNP: 44, loss: 132.804, oob acc: 86.84%, # of haplo: 40
6, SNP: 30, loss: 122.507, oob acc: 92.11%, # of haplo: 40
7, SNP: 109, loss: 72.0179, oob acc: 92.11%, # of haplo: 41
8, SNP: 72, loss: 59.3281, oob acc: 92.11%, # of haplo: 41
9, SNP: 36, loss: 54.939, oob acc: 94.74%, # of haplo: 43
10, SNP: 127, loss: 48.1392, oob acc: 94.74%, # of haplo: 43
11, SNP: 53, loss: 44.7676, oob acc: 94.74%, # of haplo: 43
12, SNP: 148, loss: 43.047, oob acc: 94.74%, # of haplo: 44
13, SNP: 152, loss: 40.2104, oob acc: 94.74%, # of haplo: 45
14, SNP: 125, loss: 39.8862, oob acc: 94.74%, # of haplo: 45
15, SNP: 77, loss: 39.5652, oob acc: 94.74%, # of haplo: 45
16, SNP: 3, loss: 39.0621, oob acc: 94.74%, # of haplo: 47
17, SNP: 141, loss: 37.6822, oob acc: 94.74%, # of haplo: 47
18, SNP: 1, loss: 36.5022, oob acc: 94.74%, # of haplo: 50
[2] 2022-10-19 01:00:34, oob acc: 94.74%, # of SNPs: 18, # of haplo: 50
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.548712e-09 5.370348e-08 4.510964e-07 2.299046e-03 1.410115e-02 3.767877e-02
Max. Mean SD
4.764725e-01 4.879021e-02 1.089441e-01
Accuracy with training data: 96.67%
Out-of-bag accuracy: 94.24%
Gene: HLA-A
Training dataset: 60 samples X 158 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 30
avg. # of SNPs in an individual classifier: 16.00
(sd: 2.83, min: 14, max: 18, median: 16.00)
avg. # of haplotypes in an individual classifier: 55.50
(sd: 7.78, min: 50, max: 61, median: 55.50)
avg. out-of-bag accuracy: 94.24%
(sd: 0.70%, min: 93.75%, max: 94.74%, median: 94.24%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.548712e-09 5.370348e-08 4.510964e-07 2.299046e-03 1.410115e-02 3.767877e-02
Max. Mean SD
4.764725e-01 4.879021e-02 1.089441e-01
Genome assembly: hg19
Remove 128 unused SNPs.
Gene: HLA-A
Training dataset: 60 samples X 30 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 30
avg. # of SNPs in an individual classifier: 16.00
(sd: 2.83, min: 14, max: 18, median: 16.00)
avg. # of haplotypes in an individual classifier: 55.50
(sd: 7.78, min: 50, max: 61, median: 55.50)
avg. out-of-bag accuracy: 94.24%
(sd: 0.70%, min: 93.75%, max: 94.74%, median: 94.24%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.548712e-09 5.370348e-08 4.510964e-07 2.299046e-03 1.410115e-02 3.767877e-02
Max. Mean SD
4.764725e-01 4.879021e-02 1.089441e-01
Genome assembly: hg19
Platform: Illumina 1M Duo
Information: Training set -- HapMap Phase II
HIBAG model for HLA-A:
2 individual classifiers
158 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 60
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:34) 0%
Predicting (2022-10-19 01:00:34) 100%
HIBAG model for HLA-A:
2 individual classifiers
30 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: Illumina 1M Duo
No allelic strand or A/B allele is flipped.
# of samples: 60
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:34) 0%
Predicting (2022-10-19 01:00:34) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:34
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2022-10-19 01:00:34, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2022-10-19 01:00:34, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[3] 2022-10-19 01:00:34, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 4, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[4] 2022-10-19 01:00:34, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Accuracy with training data: 97.06%
Out-of-bag accuracy: 92.87%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 49
avg. # of SNPs in an individual classifier: 13.25
(sd: 1.71, min: 11, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 47.25
(sd: 28.72, min: 30, max: 90, median: 34.50)
avg. out-of-bag accuracy: 92.87%
(sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:34) 0%
Predicting (2022-10-19 01:00:34) 100%
Allele Num. Freq. Num. Freq. CR ACC SEN SPE PPV NPV Miscall
Train Train Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
----
Overall accuracy: 88.5%, Call rate: 100.0%
01:01 13 0.1912 12 0.2308 100.0 96.2 100.0 95.0 85.7 100.0 --
02:01 25 0.3676 18 0.3462 100.0 98.1 94.4 100.0 100.0 97.1 01:01 (100)
02:06 1 0.0147 0 0 -- -- -- -- -- -- --
03:01 4 0.0588 5 0.0962 100.0 98.1 100.0 97.9 83.3 100.0 --
11:01 2 0.0294 3 0.0577 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 1 0.0147 2 0.0385 100.0 96.2 0.0 100.0 -- 96.2 24:02 (100)
24:02 6 0.0882 5 0.0962 100.0 92.3 60.0 95.7 60.0 95.7 01:01 (50)
24:03 1 0.0147 0 0 -- -- -- -- -- -- --
25:01 4 0.0588 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
26:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
29:02 3 0.0441 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 03:01 (50)
31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
32:01 2 0.0294 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 --
\title{Imputation Evaluation}
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{longtable}
\begin{document}
\maketitle
\setlength{\LTcapwidth}{6.5in}
% -------- BEGIN TABLE --------
\begin{longtable}{rrrrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{12}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{12}{l}{\it Overall accuracy: 88.5\%, Call rate: 100.0\%} \\
01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 96.2 & 100.0 & 95.0 & 85.7 & 100.0 & -- \\
02:01 & 25 & 0.3676 & 18 & 0.3462 & 100.0 & 98.1 & 94.4 & 100.0 & 100.0 & 97.1 & 01:01 (100) \\
02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 98.1 & 100.0 & 97.9 & 83.3 & 100.0 & -- \\
11:01 & 2 & 0.0294 & 3 & 0.0577 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 1 & 0.0147 & 2 & 0.0385 & 100.0 & 96.2 & 0.0 & 100.0 & -- & 96.2 & 24:02 (100) \\
24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 92.3 & 60.0 & 95.7 & 60.0 & 95.7 & 01:01 (50) \\
24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
25:01 & 4 & 0.0588 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 03:01 (50) \\
31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 2 & 0.0294 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------
\end{document}
<!DOCTYPE html>
<html>
<head>
<title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1" CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Train</th> <th>Freq. Train</th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="12">
<i> Overall accuracy: 88.5%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>13</td> <td>0.1912</td> <td>12</td> <td>0.2308</td> <td>100.0</td> <td>96.2</td> <td>100.0</td> <td>95.0</td> <td>85.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>25</td> <td>0.3676</td> <td>18</td> <td>0.3462</td> <td>100.0</td> <td>98.1</td> <td>94.4</td> <td>100.0</td> <td>100.0</td> <td>97.1</td> <td>01:01 (100)</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>03:01</td> <td>4</td> <td>0.0588</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>97.9</td> <td>83.3</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>2</td> <td>0.0294</td> <td>3</td> <td>0.0577</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>1</td> <td>0.0147</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>96.2</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>96.2</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>6</td> <td>0.0882</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>92.3</td> <td>60.0</td> <td>95.7</td> <td>60.0</td> <td>95.7</td> <td>01:01 (50)</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>25:01</td> <td>4</td> <td>0.0588</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>29:02</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>98.1</td> <td>03:01 (50)</td>
</tr>
<tr>
<td>31:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>2</td> <td>0.0294</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>50.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>
</body>
</html>
**Overall accuracy: 88.5%, Call rate: 100.0%**
| Allele | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) |
|:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--|
| 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 96.2 | 100.0 | 95.0 | 85.7 | 100.0 | -- |
| 02:01 | 25 | 0.3676 | 18 | 0.3462 | 100.0 | 98.1 | 94.4 | 100.0 | 100.0 | 97.1 | 01:01 (100) |
| 02:06 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 03:01 | 4 | 0.0588 | 5 | 0.0962 | 100.0 | 98.1 | 100.0 | 97.9 | 83.3 | 100.0 | -- |
| 11:01 | 2 | 0.0294 | 3 | 0.0577 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 23:01 | 1 | 0.0147 | 2 | 0.0385 | 100.0 | 96.2 | 0.0 | 100.0 | -- | 96.2 | 24:02 (100) |
| 24:02 | 6 | 0.0882 | 5 | 0.0962 | 100.0 | 92.3 | 60.0 | 95.7 | 60.0 | 95.7 | 01:01 (50) |
| 24:03 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 25:01 | 4 | 0.0588 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 26:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 29:02 | 3 | 0.0441 | 1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 03:01 (50) |
| 31:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 32:01 | 2 | 0.0294 | 2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 68:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- |
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX512BW
# of threads: 1
[-] 2022-10-19 01:00:34
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2022-10-19 01:00:34, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2022-10-19 01:00:34, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[3] 2022-10-19 01:00:34, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 4, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[4] 2022-10-19 01:00:34, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Accuracy with training data: 97.06%
Out-of-bag accuracy: 92.87%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 49
avg. # of SNPs in an individual classifier: 13.25
(sd: 1.71, min: 11, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 47.25
(sd: 28.72, min: 30, max: 90, median: 34.50)
avg. out-of-bag accuracy: 92.87%
(sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX512BW
# of threads: 1
Predicting (2022-10-19 01:00:34) 0%
Predicting (2022-10-19 01:00:34) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 8
# of SNPs: 51
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2022-10-19 01:00:35
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 13, loss: 391.274, oob acc: 41.67%, # of haplo: 17
2, SNP: 2, loss: 321.685, oob acc: 52.08%, # of haplo: 18
3, SNP: 36, loss: 232.846, oob acc: 58.33%, # of haplo: 19
4, SNP: 28, loss: 178.077, oob acc: 62.50%, # of haplo: 20
5, SNP: 35, loss: 107.151, oob acc: 68.75%, # of haplo: 20
6, SNP: 3, loss: 72.2736, oob acc: 72.92%, # of haplo: 23
7, SNP: 19, loss: 50.8439, oob acc: 77.08%, # of haplo: 25
8, SNP: 4, loss: 47.2744, oob acc: 83.33%, # of haplo: 29
9, SNP: 42, loss: 47.0092, oob acc: 85.42%, # of haplo: 37
10, SNP: 33, loss: 41.5486, oob acc: 85.42%, # of haplo: 41
11, SNP: 5, loss: 39.769, oob acc: 85.42%, # of haplo: 51
12, SNP: 10, loss: 34.0977, oob acc: 85.42%, # of haplo: 51
13, SNP: 37, loss: 32.3969, oob acc: 85.42%, # of haplo: 52
14, SNP: 7, loss: 28.1492, oob acc: 85.42%, # of haplo: 52
15, SNP: 15, loss: 27.2163, oob acc: 85.42%, # of haplo: 55
[1] 2022-10-19 01:00:35, oob acc: 85.42%, # of SNPs: 15, # of haplo: 55
=== building individual classifier 2, out-of-bag (17/28.3%) ===
1, SNP: 18, loss: 453.852, oob acc: 44.12%, # of haplo: 17
2, SNP: 4, loss: 358.517, oob acc: 50.00%, # of haplo: 18
3, SNP: 49, loss: 258.495, oob acc: 52.94%, # of haplo: 18
4, SNP: 5, loss: 172.555, oob acc: 67.65%, # of haplo: 21
5, SNP: 42, loss: 144.905, oob acc: 76.47%, # of haplo: 21
6, SNP: 38, loss: 98.7462, oob acc: 79.41%, # of haplo: 21
7, SNP: 36, loss: 83.4743, oob acc: 82.35%, # of haplo: 24
8, SNP: 19, loss: 60.2385, oob acc: 88.24%, # of haplo: 24
9, SNP: 46, loss: 49.1775, oob acc: 88.24%, # of haplo: 24
10, SNP: 20, loss: 42.3205, oob acc: 88.24%, # of haplo: 24
11, SNP: 12, loss: 41.1299, oob acc: 91.18%, # of haplo: 25
12, SNP: 1, loss: 33.8332, oob acc: 91.18%, # of haplo: 25
13, SNP: 37, loss: 32.8313, oob acc: 91.18%, # of haplo: 26
14, SNP: 7, loss: 38.8398, oob acc: 94.12%, # of haplo: 27
15, SNP: 15, loss: 35.0817, oob acc: 94.12%, # of haplo: 32
16, SNP: 39, loss: 33.7063, oob acc: 94.12%, # of haplo: 30
[2] 2022-10-19 01:00:35, oob acc: 94.12%, # of SNPs: 16, # of haplo: 30
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.464062e-10 4.686489e-10 1.568833e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.739941e-02 2.429599e-02 2.696412e-02
Accuracy with training data: 95.83%
Out-of-bag accuracy: 89.77%
Gene: HLA-C
Training dataset: 60 samples X 51 SNPs
# of HLA alleles: 17
# of individual classifiers: 2
total # of SNPs used: 23
avg. # of SNPs in an individual classifier: 15.50
(sd: 0.71, min: 15, max: 16, median: 15.50)
avg. # of haplotypes in an individual classifier: 42.50
(sd: 17.68, min: 30, max: 55, median: 42.50)
avg. out-of-bag accuracy: 89.77%
(sd: 6.15%, min: 85.42%, max: 94.12%, median: 89.77%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.464062e-10 4.686489e-10 1.568833e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.739941e-02 2.429599e-02 2.696412e-02
Genome assembly: hg19
Gene: HLA-C
Training dataset: 60 samples X 51 SNPs
# of HLA alleles: 17
# of individual classifiers: 1
total # of SNPs used: 15
avg. # of SNPs in an individual classifier: 15.00
(sd: NA, min: 15, max: 15, median: 15.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: NA, min: 55, max: 55, median: 55.00)
avg. out-of-bag accuracy: 85.42%
(sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.464062e-10 4.686489e-10 1.568833e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.739941e-02 2.429599e-02 2.696412e-02
Genome assembly: hg19
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2022-10-19 01:00:35
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 44, loss: 396.506, oob acc: 41.67%, # of haplo: 17
2, SNP: 58, loss: 291.148, oob acc: 50.00%, # of haplo: 17
3, SNP: 80, loss: 211.469, oob acc: 56.25%, # of haplo: 20
4, SNP: 18, loss: 138.615, oob acc: 60.42%, # of haplo: 20
5, SNP: 29, loss: 111.977, oob acc: 62.50%, # of haplo: 22
6, SNP: 62, loss: 90.976, oob acc: 68.75%, # of haplo: 24
7, SNP: 13, loss: 70.2962, oob acc: 72.92%, # of haplo: 24
8, SNP: 14, loss: 54.5685, oob acc: 77.08%, # of haplo: 22
9, SNP: 72, loss: 35.1951, oob acc: 77.08%, # of haplo: 24
10, SNP: 3, loss: 23.2868, oob acc: 79.17%, # of haplo: 24
11, SNP: 70, loss: 21.089, oob acc: 79.17%, # of haplo: 28
12, SNP: 5, loss: 20.9664, oob acc: 79.17%, # of haplo: 29
13, SNP: 40, loss: 20.9662, oob acc: 83.33%, # of haplo: 37
14, SNP: 24, loss: 20.2385, oob acc: 85.42%, # of haplo: 38
15, SNP: 2, loss: 20.2383, oob acc: 87.50%, # of haplo: 39
16, SNP: 74, loss: 20.0601, oob acc: 87.50%, # of haplo: 40
17, SNP: 57, loss: 17.8846, oob acc: 87.50%, # of haplo: 41
18, SNP: 56, loss: 14.7377, oob acc: 89.58%, # of haplo: 43
19, SNP: 27, loss: 10.7709, oob acc: 89.58%, # of haplo: 43
[1] 2022-10-19 01:00:35, oob acc: 89.58%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 2, out-of-bag (17/28.3%) ===
1, SNP: 66, loss: 434.369, oob acc: 44.12%, # of haplo: 19
2, SNP: 28, loss: 337.451, oob acc: 58.82%, # of haplo: 21
3, SNP: 30, loss: 302.194, oob acc: 73.53%, # of haplo: 21
4, SNP: 59, loss: 209.932, oob acc: 73.53%, # of haplo: 21
5, SNP: 69, loss: 146.631, oob acc: 82.35%, # of haplo: 21
6, SNP: 73, loss: 96.4111, oob acc: 91.18%, # of haplo: 21
7, SNP: 70, loss: 81.5466, oob acc: 91.18%, # of haplo: 21
8, SNP: 3, loss: 71.8294, oob acc: 91.18%, # of haplo: 22
9, SNP: 5, loss: 66.5825, oob acc: 94.12%, # of haplo: 23
10, SNP: 27, loss: 46.6959, oob acc: 94.12%, # of haplo: 23
11, SNP: 72, loss: 39.0572, oob acc: 94.12%, # of haplo: 23
12, SNP: 6, loss: 35.0674, oob acc: 94.12%, # of haplo: 24
13, SNP: 78, loss: 34.8741, oob acc: 94.12%, # of haplo: 32
14, SNP: 82, loss: 33.4558, oob acc: 94.12%, # of haplo: 38
15, SNP: 57, loss: 30.709, oob acc: 94.12%, # of haplo: 41
16, SNP: 32, loss: 26.6513, oob acc: 94.12%, # of haplo: 42
17, SNP: 23, loss: 26.6236, oob acc: 94.12%, # of haplo: 46
18, SNP: 2, loss: 25.7938, oob acc: 94.12%, # of haplo: 56
[2] 2022-10-19 01:00:35, oob acc: 94.12%, # of SNPs: 18, # of haplo: 56
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.736136e-08 5.138167e-06 5.122542e-05 1.574056e-03 6.457411e-03 3.142989e-02
Max. Mean SD
8.812257e-02 1.848522e-02 2.222954e-02
Accuracy with training data: 96.67%
Out-of-bag accuracy: 91.85%
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2022-10-19 01:00:35
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 44, loss: 396.506, oob acc: 41.67%, # of haplo: 17
2, SNP: 58, loss: 291.148, oob acc: 50.00%, # of haplo: 17
3, SNP: 80, loss: 211.469, oob acc: 56.25%, # of haplo: 20
4, SNP: 18, loss: 138.615, oob acc: 60.42%, # of haplo: 20
5, SNP: 29, loss: 111.977, oob acc: 62.50%, # of haplo: 22
6, SNP: 62, loss: 90.976, oob acc: 68.75%, # of haplo: 24
7, SNP: 13, loss: 70.2962, oob acc: 72.92%, # of haplo: 24
8, SNP: 14, loss: 54.5685, oob acc: 77.08%, # of haplo: 22
9, SNP: 72, loss: 35.1951, oob acc: 77.08%, # of haplo: 24
10, SNP: 3, loss: 23.2868, oob acc: 79.17%, # of haplo: 24
11, SNP: 70, loss: 21.089, oob acc: 79.17%, # of haplo: 28
12, SNP: 5, loss: 20.9664, oob acc: 79.17%, # of haplo: 29
13, SNP: 40, loss: 20.9662, oob acc: 83.33%, # of haplo: 37
14, SNP: 24, loss: 20.2385, oob acc: 85.42%, # of haplo: 38
15, SNP: 2, loss: 20.2383, oob acc: 87.50%, # of haplo: 39
16, SNP: 74, loss: 20.0601, oob acc: 87.50%, # of haplo: 40
17, SNP: 57, loss: 17.8846, oob acc: 87.50%, # of haplo: 41
18, SNP: 56, loss: 14.7377, oob acc: 89.58%, # of haplo: 43
19, SNP: 27, loss: 10.7709, oob acc: 89.58%, # of haplo: 43
[1] 2022-10-19 01:00:35, oob acc: 89.58%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 2, out-of-bag (17/28.3%) ===
1, SNP: 66, loss: 434.369, oob acc: 44.12%, # of haplo: 19
2, SNP: 28, loss: 337.451, oob acc: 58.82%, # of haplo: 21
3, SNP: 30, loss: 302.194, oob acc: 73.53%, # of haplo: 21
4, SNP: 59, loss: 209.932, oob acc: 73.53%, # of haplo: 21
5, SNP: 69, loss: 146.631, oob acc: 82.35%, # of haplo: 21
6, SNP: 73, loss: 96.4111, oob acc: 91.18%, # of haplo: 21
7, SNP: 70, loss: 81.5466, oob acc: 91.18%, # of haplo: 21
8, SNP: 3, loss: 71.8294, oob acc: 91.18%, # of haplo: 22
9, SNP: 5, loss: 66.5825, oob acc: 94.12%, # of haplo: 23
10, SNP: 27, loss: 46.6959, oob acc: 94.12%, # of haplo: 23
11, SNP: 72, loss: 39.0572, oob acc: 94.12%, # of haplo: 23
12, SNP: 6, loss: 35.0674, oob acc: 94.12%, # of haplo: 24
13, SNP: 78, loss: 34.8741, oob acc: 94.12%, # of haplo: 32
14, SNP: 82, loss: 33.4558, oob acc: 94.12%, # of haplo: 38
15, SNP: 57, loss: 30.709, oob acc: 94.12%, # of haplo: 41
16, SNP: 32, loss: 26.6513, oob acc: 94.12%, # of haplo: 42
17, SNP: 23, loss: 26.6236, oob acc: 94.12%, # of haplo: 46
18, SNP: 2, loss: 25.7938, oob acc: 94.12%, # of haplo: 56
[2] 2022-10-19 01:00:35, oob acc: 94.12%, # of SNPs: 18, # of haplo: 56
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.736136e-08 5.138167e-06 5.122542e-05 1.574056e-03 6.457411e-03 3.142989e-02
Max. Mean SD
8.812257e-02 1.848522e-02 2.222954e-02
Accuracy with training data: 96.67%
Out-of-bag accuracy: 91.85%
Gene: HLA-C
Training dataset: 60 samples X 83 SNPs
# of HLA alleles: 17
# of individual classifiers: 2
total # of SNPs used: 30
avg. # of SNPs in an individual classifier: 18.50
(sd: 0.71, min: 18, max: 19, median: 18.50)
avg. # of haplotypes in an individual classifier: 49.50
(sd: 9.19, min: 43, max: 56, median: 49.50)
avg. out-of-bag accuracy: 91.85%
(sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.736136e-08 5.138167e-06 5.122542e-05 1.574056e-03 6.457411e-03 3.142989e-02
Max. Mean SD
8.812257e-02 1.848522e-02 2.222954e-02
Genome assembly: hg19
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
>
> proc.time()
user system elapsed
18.75 0.89 20.54
HIBAG.Rcheck/HIBAG-Ex.timings
| name | user | system | elapsed | |
| HIBAG-package | 0.49 | 0.02 | 0.56 | |
| hlaAllele | 0.01 | 0.00 | 0.02 | |
| hlaAlleleDigit | 0.02 | 0.00 | 0.02 | |
| hlaAlleleSubset | 0.01 | 0.00 | 0.01 | |
| hlaAlleleToVCF | 1.89 | 0.01 | 1.91 | |
| hlaAssocTest | 0.93 | 0.00 | 0.92 | |
| hlaAttrBagging | 0.39 | 0.02 | 0.41 | |
| hlaBED2Geno | 0.09 | 0.01 | 0.10 | |
| hlaCheckAllele | 0 | 0 | 0 | |
| hlaCheckSNPs | 0.08 | 0.00 | 0.08 | |
| hlaCombineAllele | 0.01 | 0.00 | 0.02 | |
| hlaCombineModelObj | 0.21 | 0.00 | 0.20 | |
| hlaCompareAllele | 0.18 | 0.02 | 0.20 | |
| hlaConvSequence | 2.94 | 0.36 | 3.30 | |
| hlaDistance | 1.27 | 0.00 | 1.27 | |
| hlaFlankingSNP | 0.03 | 0.00 | 0.03 | |
| hlaGDS2Geno | 0.08 | 0.01 | 0.09 | |
| hlaGeno2PED | 0.06 | 0.00 | 0.06 | |
| hlaGenoAFreq | 0 | 0 | 0 | |
| hlaGenoCombine | 0.03 | 0.00 | 0.04 | |
| hlaGenoLD | 0.69 | 0.00 | 0.68 | |
| hlaGenoMFreq | 0 | 0 | 0 | |
| hlaGenoMRate | 0 | 0 | 0 | |
| hlaGenoMRate_Samp | 0 | 0 | 0 | |
| hlaGenoSubset | 0 | 0 | 0 | |
| hlaGenoSwitchStrand | 0.05 | 0.02 | 0.07 | |
| hlaLDMatrix | 1.61 | 0.20 | 1.81 | |
| hlaLociInfo | 0 | 0 | 0 | |
| hlaMakeSNPGeno | 0.03 | 0.00 | 0.03 | |
| hlaModelFiles | 0.20 | 0.00 | 0.22 | |
| hlaModelFromObj | 0.07 | 0.00 | 0.06 | |
| hlaOutOfBag | 0.51 | 0.02 | 0.53 | |
| hlaParallelAttrBagging | 0.44 | 0.09 | 1.10 | |
| hlaPredMerge | 0.25 | 0.02 | 0.26 | |
| hlaPredict | 0.25 | 0.00 | 0.25 | |
| hlaPublish | 0.34 | 0.01 | 0.36 | |
| hlaReport | 0.22 | 0.00 | 0.22 | |
| hlaReportPlot | 1.17 | 0.05 | 1.22 | |
| hlaSNPID | 0 | 0 | 0 | |
| hlaSampleAllele | 0.02 | 0.00 | 0.02 | |
| hlaSetKernelTarget | 0 | 0 | 0 | |
| hlaSplitAllele | 0.03 | 0.00 | 0.03 | |
| hlaSubModelObj | 0.05 | 0.00 | 0.04 | |
| hlaUniqueAllele | 0 | 0 | 0 | |
| plot.hlaAttrBagObj | 0.22 | 0.00 | 0.22 | |
| print.hlaAttrBagClass | 0.08 | 0.00 | 0.08 | |
| summary.hlaSNPGenoClass | 0 | 0 | 0 | |