Sunyoung Shin
| Snapshot Date: 2019-10-15 17:01:26 -0400 (Tue, 15 Oct 2019) |
| URL: https://git.bioconductor.org/packages/atSNP |
| Branch: RELEASE_3_9 |
| Last Commit: a54ff2e |
| Last Changed Date: 2019-05-02 11:54:14 -0400 (Thu, 02 May 2019) |
| Back to Multiple platform build/check report for BioC 3.9 |
|
This page was generated on 2019-10-16 12:43:27 -0400 (Wed, 16 Oct 2019).
| Package 92/1741 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| atSNP 1.0.0 Sunyoung Shin
| malbec2 | Linux (Ubuntu 18.04.2 LTS) / x86_64 | OK | OK | OK | | ||||||
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | [ OK ] | OK | | ||||||
| celaya2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | OK | OK | |
| Package: atSNP |
| Version: 1.0.0 |
| Command: C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:atSNP.install-out.txt --library=C:\Users\biocbuild\bbs-3.9-bioc\R\library --no-vignettes --timings atSNP_1.0.0.tar.gz |
| StartedAt: 2019-10-16 02:01:18 -0400 (Wed, 16 Oct 2019) |
| EndedAt: 2019-10-16 02:10:21 -0400 (Wed, 16 Oct 2019) |
| EllapsedTime: 543.3 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: atSNP.Rcheck |
| Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:atSNP.install-out.txt --library=C:\Users\biocbuild\bbs-3.9-bioc\R\library --no-vignettes --timings atSNP_1.0.0.tar.gz ### ############################################################################## ############################################################################## * using log directory 'C:/Users/biocbuild/bbs-3.9-bioc/meat/atSNP.Rcheck' * using R version 3.6.1 (2019-07-05) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * using option '--no-vignettes' * checking for file 'atSNP/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'atSNP' version '1.0.0' * 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 'atSNP' 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 * loading checks for arch 'i386' ** 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 * loading checks for arch 'x64' ** 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 ... NOTE Namespace in Imports field not imported from: 'graphics' All declared Imports should be used. * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... NOTE ComputePValues: no visible binding for global variable 'motif' ComputePValues: no visible binding for global variable 'snpid' ComputePValues: no visible binding for global variable 'snpbase' ComputePValues: no visible binding for global variable 'pval_ref' ComputePValues: no visible binding for global variable 'pval_snp' ComputePValues: no visible binding for global variable 'pval_cond_ref' ComputePValues: no visible binding for global variable 'pval_cond_snp' ComputePValues: no visible binding for global variable 'pval_diff' ComputePValues: no visible binding for global variable 'pval_rank' LoadSNPData: no visible binding for global variable 'IUPAC_CODE_MAP' MatchSubsequence: no visible binding for global variable 'motif' MatchSubsequence: no visible binding for global variable 'snpid' MatchSubsequence: no visible binding for global variable 'snpbase' MatchSubsequence: no visible binding for global variable 'len_seq' MatchSubsequence: no visible binding for global variable 'ref_seq' MatchSubsequence :: no visible binding for global variable 'motif' dtMotifMatch: no visible binding for global variable 'ref_seq' dtMotifMatch: no visible binding for global variable 'len_seq' dtMotifMatch: no visible binding for global variable 'snp_ref_start' dtMotifMatch: no visible binding for global variable 'ref_start' dtMotifMatch: no visible binding for global variable 'snp_start' dtMotifMatch: no visible binding for global variable 'snp_ref_end' dtMotifMatch: no visible binding for global variable 'ref_end' dtMotifMatch: no visible binding for global variable 'snp_end' dtMotifMatch: no visible binding for global variable 'snp_ref_length' dtMotifMatch: no visible binding for global variable 'ref_aug_match_seq_forward' dtMotifMatch: no visible binding for global variable 'ref_aug_match_seq_reverse' dtMotifMatch: no visible binding for global variable 'snp_aug_match_seq_forward' dtMotifMatch: no visible binding for global variable 'snp_seq' dtMotifMatch: no visible binding for global variable 'snp_aug_match_seq_reverse' dtMotifMatch: no visible binding for global variable 'ref_strand' dtMotifMatch: no visible binding for global variable 'ref_location' dtMotifMatch: no visible binding for global variable 'snp_strand' dtMotifMatch: no visible binding for global variable 'snp_location' dtMotifMatch: no visible binding for global variable 'ref_extra_pwm_left' dtMotifMatch: no visible binding for global variable 'ref_extra_pwm_right' dtMotifMatch: no visible binding for global variable 'snp_extra_pwm_left' dtMotifMatch: no visible binding for global variable 'snp_extra_pwm_right' dtMotifMatch: no visible binding for global variable 'snpid' match_subseq_par: no visible binding for global variable 'snpid' match_subseq_par: no visible binding for global variable 'motif' match_subseq_par: no visible binding for global variable 'snpbase' match_subseq_par: no visible binding for global variable 'ref_strand' match_subseq_par: no visible binding for global variable 'ref_match_seq' match_subseq_par: no visible binding for global variable 'ref_seq' match_subseq_par: no visible binding for global variable 'ref_start' match_subseq_par: no visible binding for global variable 'ref_end' match_subseq_par: no visible binding for global variable 'ref_seq_rev' match_subseq_par: no visible binding for global variable 'len_seq' match_subseq_par: no visible binding for global variable 'snp_strand' match_subseq_par: no visible binding for global variable 'snp_match_seq' match_subseq_par: no visible binding for global variable 'snp_seq' match_subseq_par: no visible binding for global variable 'snp_start' match_subseq_par: no visible binding for global variable 'snp_end' match_subseq_par: no visible binding for global variable 'snp_seq_rev' match_subseq_par: no visible binding for global variable 'snp_seq_ref_match' match_subseq_par: no visible binding for global variable 'ref_seq_snp_match' match_subseq_par: no visible binding for global variable 'motif_len' match_subseq_par: no visible binding for global variable 'log_lik_ref' match_subseq_par: no visible binding for global variable 'log_lik_snp' match_subseq_par: no visible binding for global variable 'log_lik_ratio' match_subseq_par: no visible binding for global variable 'log_enhance_odds' match_subseq_par: no visible binding for global variable 'log_reduce_odds' match_subseq_par: no visible binding for global variable 'IUPAC' motif_score_par: no visible binding for global variable 'motif' motif_score_par: no visible binding for global variable 'snpbase' results_motif_par: no visible global function definition for 'ggplot' results_motif_par: no visible global function definition for 'aes' results_motif_par: no visible binding for global variable 'p.value' results_motif_par: no visible global function definition for 'geom_point' results_motif_par: no visible global function definition for 'scale_y_log10' results_motif_par: no visible global function definition for 'geom_errorbar' results_motif_par: no visible global function definition for 'ggtitle' Undefined global functions or variables: IUPAC IUPAC_CODE_MAP aes geom_errorbar geom_point ggplot ggtitle len_seq log_enhance_odds log_lik_ratio log_lik_ref log_lik_snp log_reduce_odds motif motif_len p.value pval_cond_ref pval_cond_snp pval_diff pval_rank pval_ref pval_snp ref_aug_match_seq_forward ref_aug_match_seq_reverse ref_end ref_extra_pwm_left ref_extra_pwm_right ref_location ref_match_seq ref_seq ref_seq_rev ref_seq_snp_match ref_start ref_strand scale_y_log10 snp_aug_match_seq_forward snp_aug_match_seq_reverse snp_end snp_extra_pwm_left snp_extra_pwm_right snp_location snp_match_seq snp_ref_end snp_ref_length snp_ref_start snp_seq snp_seq_ref_match snp_seq_rev snp_start snp_strand snpbase snpid * 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 data for ASCII and uncompressed saves ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking compiled code ... NOTE Note: information on .o files for i386 is not available Note: information on .o files for x64 is not available File 'C:/Users/biocbuild/bbs-3.9-bioc/R/library/atSNP/libs/i386/atSNP.dll': Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran) Found 'exit', possibly from 'exit' (C), 'stop' (Fortran) Found 'printf', possibly from 'printf' (C) File 'C:/Users/biocbuild/bbs-3.9-bioc/R/library/atSNP/libs/x64/atSNP.dll': Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran) Found 'exit', possibly from 'exit' (C), 'stop' (Fortran) Found 'printf', possibly from 'printf' (C) 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 files in 'vignettes' ... OK * checking examples ... ** running examples for arch 'i386' ... OK Examples with CPU or elapsed time > 5s user system elapsed MatchSubsequence 0.95 0.02 16.56 ComputePValues 0.87 0.03 13.03 dtMotifMatch 0.72 0.08 14.15 ComputeMotifScore 0.72 0.01 16.71 ** running examples for arch 'x64' ... OK Examples with CPU or elapsed time > 5s user system elapsed MatchSubsequence 1.17 0.00 13.57 dtMotifMatch 0.96 0.04 13.35 ComputePValues 0.96 0.01 14.19 ComputeMotifScore 0.95 0.00 16.20 * checking for unstated dependencies in 'tests' ... OK * checking tests ... ** running tests for arch 'i386' ... Running 'test.R' Running 'test_change.R' Running 'test_diff.R' Running 'test_is.R' OK ** running tests for arch 'x64' ... Running 'test.R' Running 'test_change.R' Running 'test_diff.R' Running 'test_is.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: 3 NOTEs See 'C:/Users/biocbuild/bbs-3.9-bioc/meat/atSNP.Rcheck/00check.log' for details.
atSNP.Rcheck/00install.out
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###
### Running command:
###
### C:\cygwin\bin\curl.exe -O https://malbec2.bioconductor.org/BBS/3.9/bioc/src/contrib/atSNP_1.0.0.tar.gz && rm -rf atSNP.buildbin-libdir && mkdir atSNP.buildbin-libdir && C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=atSNP.buildbin-libdir atSNP_1.0.0.tar.gz && C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD INSTALL atSNP_1.0.0.zip && rm atSNP_1.0.0.tar.gz atSNP_1.0.0.zip
###
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% Total % Received % Xferd Average Speed Time Time Time Current
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0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 512k 100 512k 0 0 7665k 0 --:--:-- --:--:-- --:--:-- 8544k
install for i386
* installing *source* package 'atSNP' ...
** using staged installation
** libs
C:/Rtools/mingw_32/bin/g++ -I"C:/Users/BIOCBU~1/BBS-3~1.9-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.9-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c ImportanceSample.cpp -o ImportanceSample.o
C:/Rtools/mingw_32/bin/g++ -I"C:/Users/BIOCBU~1/BBS-3~1.9-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.9-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c ImportanceSampleChange.cpp -o ImportanceSampleChange.o
C:/Rtools/mingw_32/bin/g++ -I"C:/Users/BIOCBU~1/BBS-3~1.9-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.9-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c ImportanceSampleDiff.cpp -o ImportanceSampleDiff.o
C:/Rtools/mingw_32/bin/g++ -I"C:/Users/BIOCBU~1/BBS-3~1.9-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.9-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c MotifScore.cpp -o MotifScore.o
C:/Rtools/mingw_32/bin/g++ -shared -s -static-libgcc -o atSNP.dll tmp.def ImportanceSample.o ImportanceSampleChange.o ImportanceSampleDiff.o MotifScore.o -LC:/extsoft/lib/i386 -LC:/extsoft/lib -LC:/Users/BIOCBU~1/BBS-3~1.9-B/R/bin/i386 -lR
installing to C:/Users/biocbuild/bbs-3.9-bioc/meat/atSNP.buildbin-libdir/00LOCK-atSNP/00new/atSNP/libs/i386
** R
** data
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
converting help for package 'atSNP'
finding HTML links ... done
ComputeMotifScore html
ComputePValues html
GetIUPACSequence html
LoadFastaData html
LoadMotifLibrary html
LoadSNPData html
MatchSubsequence html
atSNP-package html
dtMotifMatch html
encode_motif html
encode_motifinfo html
jaspar_motif html
jaspar_motifinfo html
motif_library html
motif_match html
motif_scores html
plotMotifMatch html
prior html
snpInfo html
snp_tbl html
transition html
** 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
install for x64
* installing *source* package 'atSNP' ...
** libs
C:/Rtools/mingw_64/bin/g++ -I"C:/Users/BIOCBU~1/BBS-3~1.9-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.9-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c ImportanceSample.cpp -o ImportanceSample.o
C:/Rtools/mingw_64/bin/g++ -I"C:/Users/BIOCBU~1/BBS-3~1.9-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.9-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c ImportanceSampleChange.cpp -o ImportanceSampleChange.o
C:/Rtools/mingw_64/bin/g++ -I"C:/Users/BIOCBU~1/BBS-3~1.9-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.9-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c ImportanceSampleDiff.cpp -o ImportanceSampleDiff.o
C:/Rtools/mingw_64/bin/g++ -I"C:/Users/BIOCBU~1/BBS-3~1.9-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.9-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c MotifScore.cpp -o MotifScore.o
C:/Rtools/mingw_64/bin/g++ -shared -s -static-libgcc -o atSNP.dll tmp.def ImportanceSample.o ImportanceSampleChange.o ImportanceSampleDiff.o MotifScore.o -LC:/extsoft/lib/x64 -LC:/extsoft/lib -LC:/Users/BIOCBU~1/BBS-3~1.9-B/R/bin/x64 -lR
installing to C:/Users/biocbuild/bbs-3.9-bioc/meat/atSNP.buildbin-libdir/atSNP/libs/x64
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'atSNP' as atSNP_1.0.0.zip
* DONE (atSNP)
* installing to library 'C:/Users/biocbuild/bbs-3.9-bioc/R/library'
package 'atSNP' successfully unpacked and MD5 sums checked
|
atSNP.Rcheck/tests_i386/test.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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.
> library(atSNP)
> library(BiocParallel)
> library(testthat)
>
> ## process the data
> data(example)
>
> motif_scores <- ComputeMotifScore(motif_library, snpInfo, ncores = 1)
>
> motif_scores <- MatchSubsequence(motif_scores$snp.tbl, motif_scores$motif.scores, ncores = 1, motif.lib = motif_library)
>
> motif_scores[which(motif_scores$snpid == "rs7412" & motif_scores$motif == "SIX5_disc1"), ]
snpid motif
4 rs7412 SIX5_disc1
ref_seq
4 CTCCTCCGCGATGCCGATGACCTGCAGAAGCGCCTGGCAGTGTACCAGGCCGGGGCCCGCG
snp_seq motif_len
4 CTCCTCCGCGATGCCGATGACCTGCAGAAGTGCCTGGCAGTGTACCAGGCCGGGGCCCGCG 10
ref_start ref_end ref_strand snp_start snp_end snp_strand log_lik_ref
4 29 38 - 22 31 + -42.60672
log_lik_snp log_lik_ratio log_enhance_odds log_reduce_odds IUPAC
4 -38.4083 -4.198418 23.013 -2.917768 GARWTGTAGT
ref_match_seq snp_match_seq ref_seq_snp_match snp_seq_ref_match snpbase
4 GCCAGGCGCT CTGCAGAAGT CTGCAGAAGC GCCAGGCACT T
>
> len_seq <- sapply(motif_scores$ref_seq, nchar)
> snp_pos <- as.integer(len_seq / 2) + 1
>
> i <- which(motif_scores$snpid == "rs7412" & motif_scores$motif == "SIX5_disc1")
>
> test_that("Error: reference bases are not the same as the sequence matrix.", {
+ expect_equal(sum(snpInfo$sequence_matrix[31, ] != snpInfo$ref_base), 0)
+ expect_equal(sum(snpInfo$sequence_matrix[31, ] == snpInfo$snp_base), 0)
+ })
>
> test_that("Error: log_lik_ratio is not correct.", {
+ expect_equal(motif_scores$log_lik_ref - motif_scores$log_lik_snp, motif_scores$log_lik_ratio)
+ })
>
> test_that("Error: log likelihoods are not correct.", {
+
+ log_lik <- sapply(seq(nrow(motif_scores)),
+ function(i) {
+ motif_mat <- motif_library[[motif_scores$motif[i]]]
+ colind<-which(snpInfo$snpids==motif_scores$snpid[i])
+ bases <- snpInfo$sequence_matrix[motif_scores$ref_start[i]:motif_scores$ref_end[i], colind]
+ if(motif_scores$ref_strand[i] == "-")
+ bases <- 5 - rev(bases)
+ log(prod(
+ motif_mat[cbind(seq(nrow(motif_mat)),
+ bases)]))
+ })
+
+ expect_equal(log_lik, motif_scores$log_lik_ref)
+
+ snp_mat <- snpInfo$sequence_matrix
+ snp_mat[cbind(snp_pos, seq(ncol(snp_mat)))] <- snpInfo$snp_base
+ log_lik <- sapply(seq(nrow(motif_scores)),
+ function(i) {
+ motif_mat <- motif_library[[motif_scores$motif[i]]]
+ colind<-which(snpInfo$snpids==motif_scores$snpid[i])
+ bases <- snp_mat[motif_scores$snp_start[i]:motif_scores$snp_end[i], colind]
+ if(motif_scores$snp_strand[i] == "-")
+ bases <- 5 - rev(bases)
+ log(prod(
+ motif_mat[cbind(seq(nrow(motif_mat)),
+ bases)]))
+ })
+
+ expect_equal(log_lik, motif_scores$log_lik_snp)
+ })
>
> test_that("Error: log_enhance_odds not correct.", {
+
+ len_seq <- sapply(motif_scores$ref_seq, nchar)
+ snp_pos <- as.integer(len_seq / 2) + 1
+
+ ## log odds for reduction in binding affinity
+
+ pos_in_pwm <- snp_pos - motif_scores$ref_start + 1
+ neg_ids <- which(motif_scores$ref_strand == "-")
+ pos_in_pwm[neg_ids] <- motif_scores$ref_end[neg_ids]- snp_pos[neg_ids] + 1
+ snp_base <- sapply(substr(motif_scores$snp_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
+ ref_base <- sapply(substr(motif_scores$ref_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
+ snp_base[neg_ids] <- 5 - snp_base[neg_ids]
+ ref_base[neg_ids] <- 5 - ref_base[neg_ids]
+ my_log_reduce_odds <- sapply(seq(nrow(motif_scores)),
+ function(i)
+ log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], ref_base[i]]) -
+ log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], snp_base[i]])
+ )
+
+ expect_equal(my_log_reduce_odds, motif_scores$log_reduce_odds)
+
+ ## log odds in enhancing binding affinity
+
+ pos_in_pwm <- snp_pos - motif_scores$snp_start + 1
+ neg_ids <- which(motif_scores$snp_strand == "-")
+ pos_in_pwm[neg_ids] <- motif_scores$snp_end[neg_ids]- snp_pos[neg_ids] + 1
+ snp_base <- sapply(substr(motif_scores$snp_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
+ ref_base <- sapply(substr(motif_scores$ref_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
+ snp_base[neg_ids] <- 5 - snp_base[neg_ids]
+ ref_base[neg_ids] <- 5 - ref_base[neg_ids]
+ my_log_enhance_odds <- sapply(seq(nrow(motif_scores)),
+ function(i)
+ log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], snp_base[i]]) -
+ log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], ref_base[i]])
+ )
+
+ expect_equal(my_log_enhance_odds, motif_scores$log_enhance_odds)
+
+
+ })
>
> test_that("Error: the maximum log likelihood computation is not correct.", {
+
+ snp_mat <- snpInfo$sequence_matrix
+ snp_mat[cbind(snp_pos, seq(ncol(snp_mat)))] <- snpInfo$snp_base
+
+ .findMaxLog <- function(seq_vec, pwm) {
+ snp_pos <- as.integer(length(seq_vec) / 2) + 1
+ start_pos <- snp_pos - nrow(pwm) + 1
+ end_pos <- snp_pos
+ rev_seq <- 5 - rev(seq_vec)
+
+ maxLogProb <- -Inf
+ for(i in start_pos : end_pos) {
+ LogProb <- log(prod(pwm[cbind(seq(nrow(pwm)),
+ seq_vec[i - 1 + seq(nrow(pwm))])]))
+ if(LogProb > maxLogProb)
+ maxLogProb <- LogProb
+ }
+ for(i in start_pos : end_pos) {
+ LogProb <- log(prod(pwm[cbind(seq(nrow(pwm)),
+ rev_seq[i - 1 + seq(nrow(pwm))])]))
+ if(LogProb > maxLogProb)
+ maxLogProb <- LogProb
+ }
+ return(maxLogProb)
+ }
+
+ ## find the maximum log likelihood on the reference sequence
+ my_log_lik_ref <- sapply(seq(nrow(motif_scores)),
+ function(x) {
+ colind<-which(snpInfo$snpids==motif_scores$snpid[x])
+ seq_vec<- snpInfo$sequence_matrix[, colind]
+ pwm <- motif_library[[motif_scores$motif[x]]]
+ return(.findMaxLog(seq_vec, pwm))
+ })
+
+ ## find the maximum log likelihood on the SNP sequence
+
+ my_log_lik_snp <- sapply(seq(nrow(motif_scores)),
+ function(x) {
+ colind<-which(snpInfo$snpids==motif_scores$snpid[x]) #ADDED
+ seq_vec<- snp_mat[, colind]
+ pwm <- motif_library[[motif_scores$motif[x]]]
+ return(.findMaxLog(seq_vec, pwm))
+ })
+
+ expect_equal(my_log_lik_ref, motif_scores$log_lik_ref)
+ expect_equal(my_log_lik_snp, motif_scores$log_lik_snp)
+
+ })
>
> proc.time()
user system elapsed
12.96 0.98 13.90
|
atSNP.Rcheck/tests_x64/test.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(atSNP)
> library(BiocParallel)
> library(testthat)
>
> ## process the data
> data(example)
>
> motif_scores <- ComputeMotifScore(motif_library, snpInfo, ncores = 1)
>
> motif_scores <- MatchSubsequence(motif_scores$snp.tbl, motif_scores$motif.scores, ncores = 1, motif.lib = motif_library)
>
> motif_scores[which(motif_scores$snpid == "rs7412" & motif_scores$motif == "SIX5_disc1"), ]
snpid motif
4 rs7412 SIX5_disc1
ref_seq
4 CTCCTCCGCGATGCCGATGACCTGCAGAAGCGCCTGGCAGTGTACCAGGCCGGGGCCCGCG
snp_seq motif_len
4 CTCCTCCGCGATGCCGATGACCTGCAGAAGTGCCTGGCAGTGTACCAGGCCGGGGCCCGCG 10
ref_start ref_end ref_strand snp_start snp_end snp_strand log_lik_ref
4 29 38 - 22 31 + -42.60672
log_lik_snp log_lik_ratio log_enhance_odds log_reduce_odds IUPAC
4 -38.4083 -4.198418 23.013 -2.917768 GARWTGTAGT
ref_match_seq snp_match_seq ref_seq_snp_match snp_seq_ref_match snpbase
4 GCCAGGCGCT CTGCAGAAGT CTGCAGAAGC GCCAGGCACT T
>
> len_seq <- sapply(motif_scores$ref_seq, nchar)
> snp_pos <- as.integer(len_seq / 2) + 1
>
> i <- which(motif_scores$snpid == "rs7412" & motif_scores$motif == "SIX5_disc1")
>
> test_that("Error: reference bases are not the same as the sequence matrix.", {
+ expect_equal(sum(snpInfo$sequence_matrix[31, ] != snpInfo$ref_base), 0)
+ expect_equal(sum(snpInfo$sequence_matrix[31, ] == snpInfo$snp_base), 0)
+ })
>
> test_that("Error: log_lik_ratio is not correct.", {
+ expect_equal(motif_scores$log_lik_ref - motif_scores$log_lik_snp, motif_scores$log_lik_ratio)
+ })
>
> test_that("Error: log likelihoods are not correct.", {
+
+ log_lik <- sapply(seq(nrow(motif_scores)),
+ function(i) {
+ motif_mat <- motif_library[[motif_scores$motif[i]]]
+ colind<-which(snpInfo$snpids==motif_scores$snpid[i])
+ bases <- snpInfo$sequence_matrix[motif_scores$ref_start[i]:motif_scores$ref_end[i], colind]
+ if(motif_scores$ref_strand[i] == "-")
+ bases <- 5 - rev(bases)
+ log(prod(
+ motif_mat[cbind(seq(nrow(motif_mat)),
+ bases)]))
+ })
+
+ expect_equal(log_lik, motif_scores$log_lik_ref)
+
+ snp_mat <- snpInfo$sequence_matrix
+ snp_mat[cbind(snp_pos, seq(ncol(snp_mat)))] <- snpInfo$snp_base
+ log_lik <- sapply(seq(nrow(motif_scores)),
+ function(i) {
+ motif_mat <- motif_library[[motif_scores$motif[i]]]
+ colind<-which(snpInfo$snpids==motif_scores$snpid[i])
+ bases <- snp_mat[motif_scores$snp_start[i]:motif_scores$snp_end[i], colind]
+ if(motif_scores$snp_strand[i] == "-")
+ bases <- 5 - rev(bases)
+ log(prod(
+ motif_mat[cbind(seq(nrow(motif_mat)),
+ bases)]))
+ })
+
+ expect_equal(log_lik, motif_scores$log_lik_snp)
+ })
>
> test_that("Error: log_enhance_odds not correct.", {
+
+ len_seq <- sapply(motif_scores$ref_seq, nchar)
+ snp_pos <- as.integer(len_seq / 2) + 1
+
+ ## log odds for reduction in binding affinity
+
+ pos_in_pwm <- snp_pos - motif_scores$ref_start + 1
+ neg_ids <- which(motif_scores$ref_strand == "-")
+ pos_in_pwm[neg_ids] <- motif_scores$ref_end[neg_ids]- snp_pos[neg_ids] + 1
+ snp_base <- sapply(substr(motif_scores$snp_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
+ ref_base <- sapply(substr(motif_scores$ref_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
+ snp_base[neg_ids] <- 5 - snp_base[neg_ids]
+ ref_base[neg_ids] <- 5 - ref_base[neg_ids]
+ my_log_reduce_odds <- sapply(seq(nrow(motif_scores)),
+ function(i)
+ log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], ref_base[i]]) -
+ log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], snp_base[i]])
+ )
+
+ expect_equal(my_log_reduce_odds, motif_scores$log_reduce_odds)
+
+ ## log odds in enhancing binding affinity
+
+ pos_in_pwm <- snp_pos - motif_scores$snp_start + 1
+ neg_ids <- which(motif_scores$snp_strand == "-")
+ pos_in_pwm[neg_ids] <- motif_scores$snp_end[neg_ids]- snp_pos[neg_ids] + 1
+ snp_base <- sapply(substr(motif_scores$snp_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
+ ref_base <- sapply(substr(motif_scores$ref_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
+ snp_base[neg_ids] <- 5 - snp_base[neg_ids]
+ ref_base[neg_ids] <- 5 - ref_base[neg_ids]
+ my_log_enhance_odds <- sapply(seq(nrow(motif_scores)),
+ function(i)
+ log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], snp_base[i]]) -
+ log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], ref_base[i]])
+ )
+
+ expect_equal(my_log_enhance_odds, motif_scores$log_enhance_odds)
+
+
+ })
>
> test_that("Error: the maximum log likelihood computation is not correct.", {
+
+ snp_mat <- snpInfo$sequence_matrix
+ snp_mat[cbind(snp_pos, seq(ncol(snp_mat)))] <- snpInfo$snp_base
+
+ .findMaxLog <- function(seq_vec, pwm) {
+ snp_pos <- as.integer(length(seq_vec) / 2) + 1
+ start_pos <- snp_pos - nrow(pwm) + 1
+ end_pos <- snp_pos
+ rev_seq <- 5 - rev(seq_vec)
+
+ maxLogProb <- -Inf
+ for(i in start_pos : end_pos) {
+ LogProb <- log(prod(pwm[cbind(seq(nrow(pwm)),
+ seq_vec[i - 1 + seq(nrow(pwm))])]))
+ if(LogProb > maxLogProb)
+ maxLogProb <- LogProb
+ }
+ for(i in start_pos : end_pos) {
+ LogProb <- log(prod(pwm[cbind(seq(nrow(pwm)),
+ rev_seq[i - 1 + seq(nrow(pwm))])]))
+ if(LogProb > maxLogProb)
+ maxLogProb <- LogProb
+ }
+ return(maxLogProb)
+ }
+
+ ## find the maximum log likelihood on the reference sequence
+ my_log_lik_ref <- sapply(seq(nrow(motif_scores)),
+ function(x) {
+ colind<-which(snpInfo$snpids==motif_scores$snpid[x])
+ seq_vec<- snpInfo$sequence_matrix[, colind]
+ pwm <- motif_library[[motif_scores$motif[x]]]
+ return(.findMaxLog(seq_vec, pwm))
+ })
+
+ ## find the maximum log likelihood on the SNP sequence
+
+ my_log_lik_snp <- sapply(seq(nrow(motif_scores)),
+ function(x) {
+ colind<-which(snpInfo$snpids==motif_scores$snpid[x]) #ADDED
+ seq_vec<- snp_mat[, colind]
+ pwm <- motif_library[[motif_scores$motif[x]]]
+ return(.findMaxLog(seq_vec, pwm))
+ })
+
+ expect_equal(my_log_lik_ref, motif_scores$log_lik_ref)
+ expect_equal(my_log_lik_snp, motif_scores$log_lik_snp)
+
+ })
>
> proc.time()
user system elapsed
11.48 0.60 12.07
|
|
atSNP.Rcheck/tests_i386/test_change.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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.
> library(atSNP)
> library(BiocParallel)
> library(testthat)
> data(example)
>
> trans_mat <- matrix(rep(snpInfo$prior, each = 4), nrow = 4)
> test_pwm <- motif_library$SIX5_disc1
> scores <- as.matrix(motif_scores$motif.scores[3:4, 4:5])
> score_diff <- abs(scores[,2]-scores[,1])
>
> pval_a <- .Call("test_p_value", test_pwm, snpInfo$prior, snpInfo$transition, scores, 0.15, 100)
> pval_ratio <- abs(log(pval_a[seq(nrow(scores)),1]) - log(pval_a[seq(nrow(scores)) + nrow(scores), 1]))
>
> test_score <- test_pwm
> for(i in seq(nrow(test_score))) {
+ for(j in seq(ncol(test_score))) {
+ test_score[i, j] <- exp(mean(log(test_pwm[i, j] / test_pwm[i, -j])))
+ }
+ }
>
> adj_mat <- test_pwm + 0.25
> motif_len <- nrow(test_pwm)
>
> ## these are functions for this test only
> drawonesample <- function(theta) {
+ prob_start <- rev(rowSums(test_score ^ theta) / rowSums(adj_mat))
+ id <- sample(seq(motif_len), 1, prob = prob_start)
+ sample <- sample(1:4, 2 * motif_len - 1, replace = TRUE, prob = snpInfo$prior)
+ delta <- adj_mat
+ delta[motif_len - id + 1, ] <- test_score[motif_len - id + 1, ] ^ theta
+ sample[id - 1 + seq(motif_len)] <- apply(delta, 1, function(x) sample(seq(4), 1, prob = x))
+ ## compute weight
+ sc <- 0
+ for(s in seq(motif_len)) {
+ delta <- adj_mat
+ delta[motif_len + 1 - s, ] <- test_score[motif_len + 1 - s, ] ^ theta
+ sc <- sc + prod(delta[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)])]) /
+ prod(snpInfo$prior[sample[s - 1 + seq(motif_len)]])
+ }
+ sample <- c(sample, id, sc)
+ return(sample)
+ }
> jointprob <- function(x) prod(test_pwm[cbind(seq(motif_len), x)])
> maxjointprob <- function(x) {
+ maxp <- -Inf
+ p <- -Inf
+ for(i in 1:motif_len) {
+ p <- jointprob(x[i:(i+motif_len - 1)])
+ if(p > maxp)
+ maxp <- p
+ }
+ for(i in 1:motif_len) {
+ p <- jointprob(5 - x[(i+motif_len - 1):i])
+ if(p > maxp)
+ maxp <- p
+ }
+ return(maxp)
+ }
> get_freq <- function(sample) {
+ emp_freq <- matrix(0, nrow = 2 * motif_len - 1, ncol = 4)
+ for(i in seq(2 * motif_len - 1)) {
+ for(j in seq(4)) {
+ emp_freq[i, j] <- sum(sample[i, ] == j - 1)
+ }
+ }
+ emp_freq <- emp_freq / rowSums(emp_freq)
+ return(emp_freq)
+ }
>
> test_that("Error: quantile function computing are not equivalent.", {
+ for(p in c(0.01, 0.1, 0.5, 0.9, 0.99) ) {
+ delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")
+ delta.r <- as.double(sort(abs(scores[,2]-scores[,1]))[ceiling((1 - p) * (nrow(scores)))])
+ expect_equal(delta, delta.r)
+ }
+ })
>
> test_that("Error: the scores for samples are not equivalent.", {
+ p <- 0.1
+ delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_change", test_score, adj_mat, delta, package = "atSNP")
+ ## Use R code to generate a random sample
+ for(i in seq(10)) {
+ sample <- drawonesample(theta)
+ sample_score <- .Call("test_compute_sample_score_change", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)] - 1, snpInfo$prior, trans_mat, sample[2 * motif_len] - 1, theta, package = "atSNP")
+ expect_equal(sample[2 * motif_len + 1], sample_score[1])
+ sample1 <- sample2 <- sample3 <- sample
+ sample1[motif_len] <- seq(4)[-sample[motif_len]][1]
+ sample2[motif_len] <- seq(4)[-sample[motif_len]][2]
+ sample3[motif_len] <- seq(4)[-sample[motif_len]][3]
+ sample_score_r <- log(maxjointprob(sample[seq(2 * motif_len - 1)])) -
+ log(c(maxjointprob(sample1[seq(2 * motif_len - 1)]),
+ maxjointprob(sample2[seq(2 * motif_len - 1)]),
+ maxjointprob(sample3[seq(2 * motif_len - 1)])))
+ expect_equal(sample_score_r, sample_score[2:4])
+ }
+
+ ## Use C code to generate a random sample
+ for(i in seq(10)) {
+ sample <- .Call("test_importance_sample_change", test_score, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP")
+ start_pos <- sample[2 * motif_len] + 1
+ adj_score <- 0
+ for(s in seq_len(motif_len)) {
+ adj_s <- sum(log(adj_mat[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)] + 1)]) -
+ log(snpInfo$prior[sample[s - 1 + seq(motif_len)] + 1]))
+ adj_s <- adj_s + theta * log(test_score[motif_len + 1 - s, sample[motif_len] + 1]) -
+ log(adj_mat[motif_len + 1 - s, sample[motif_len] + 1])
+ adj_score <- adj_score + exp(adj_s)
+ }
+ sample_score <- .Call("test_compute_sample_score_change", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)], snpInfo$prior, trans_mat, sample[2 * motif_len], theta, package = "atSNP")
+ expect_equal(adj_score, sample_score[1])
+ }
+ })
>
> test_that("Error: compute the normalizing constant.", {
+ ## parameters
+ for(p in seq(9) / 10) {
+ delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_change", test_score, adj_mat, delta, package = "atSNP")
+ const <- .Call("test_func_delta_change", test_score, adj_mat, theta, package = "atSNP")
+ ## in R
+ adj_sum <- rowSums(adj_mat)
+ wei_sum <- rowSums(test_score ^ theta)
+ const.r <- prod(adj_sum) * sum(wei_sum / adj_sum)
+ expect_equal(const, const.r)
+ }
+ })
>
> test_that("Error: sample distributions are not expected.", {
+ ## parameters
+ p <- 0.1
+ delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_change", test_score, adj_mat, delta, package = "atSNP")
+ prob_start <- rev(rowSums(test_score ^ theta) / rowSums(adj_mat))
+ ## construct the delta matrix
+ delta <- matrix(1, nrow = 4 * motif_len, ncol = 2 * motif_len - 1)
+ for(pos in seq(motif_len)) {
+ delta[seq(4) + 4 * (pos - 1), ] <- snpInfo$prior
+ delta[seq(4) + 4 * (pos - 1), pos - 1 + seq(motif_len)] <- t(test_pwm)
+ delta[seq(4) + 4 * (pos - 1), motif_len] <- test_score[motif_len + 1 - pos, ] ^ theta
+ delta[seq(4) + 4 * (pos - 1), ] <- delta[seq(4) + 4 * (pos - 1),] / rep(colSums(delta[seq(4) + 4 * (pos - 1), ]), each = 4)
+ }
+ target_freq <- matrix(0, nrow = 4, ncol = 2 * motif_len - 1)
+ for(pos in seq(motif_len)) {
+ target_freq <- target_freq + delta[seq(4) + 4 * (pos - 1), ] * prob_start[pos]
+ }
+ target_freq <- t(target_freq)
+ target_freq <- target_freq / rowSums(target_freq)
+
+ results_i <- function(i) {
+ ## generate 100 samples
+ sample1 <- sapply(seq(100), function(x)
+ .Call("test_importance_sample_change",
+ adj_mat, snpInfo$prior, trans_mat, test_score, theta, package = "atSNP"))
+ emp_freq1 <- get_freq(sample1)
+ sample2 <- sapply(rep(theta, 100), drawonesample)
+ emp_freq2 <- get_freq(sample2 - 1)
+ ## print(rbind(emp_freq1[10, ], emp_freq2[10, ], target_freq[10, ]))
+ max(abs(emp_freq1 - target_freq)) > max(abs(emp_freq2 - target_freq))
+ }
+
+ if(Sys.info()[["sysname"]] == "Windows"){
+ snow <- SnowParam(workers = 1, type = "SOCK")
+ results<-bpmapply(results_i, seq(20), BPPARAM = snow,SIMPLIFY = FALSE)
+ }else{
+ results<-bpmapply(results_i, seq(20), BPPARAM = MulticoreParam(workers = 1),
+ SIMPLIFY = FALSE)
+ }
+
+ print(sum(unlist(results)))
+ print(pbinom(sum(unlist(results)), size = 20, prob = 0.5))
+ })
[1] 10
[1] 0.5880985
>
> test_that("Error: the chosen pvalues should have the smaller variance.", {
+ .structure_diff <- function(pval_mat) {
+ id <- apply(pval_mat[, c(2, 4)], 1, which.min)
+ return(cbind(pval_mat[, c(1, 3)][cbind(seq_along(id), id)],
+ pval_mat[, c(2, 4)][cbind(seq_along(id), id)]))
+ }
+ for(p in c(0.05, 0.1, 0.2, 0.5)) {
+ p_values <- .Call("test_p_value_change", test_pwm, test_score, adj_mat, snpInfo$prior, snpInfo$transition, score_diff, pval_ratio, quantile(score_diff, 1 - p), 100, package = "atSNP")$score
+ p_values_s <- .structure_diff(p_values)
+ expect_equal(p_values_s[, 2], apply(p_values[, c(2, 4)], 1, min))
+ }
+ })
>
> proc.time()
user system elapsed
10.15 1.03 11.15
|
atSNP.Rcheck/tests_x64/test_change.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 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.
> library(atSNP)
> library(BiocParallel)
> library(testthat)
> data(example)
>
> trans_mat <- matrix(rep(snpInfo$prior, each = 4), nrow = 4)
> test_pwm <- motif_library$SIX5_disc1
> scores <- as.matrix(motif_scores$motif.scores[3:4, 4:5])
> score_diff <- abs(scores[,2]-scores[,1])
>
> pval_a <- .Call("test_p_value", test_pwm, snpInfo$prior, snpInfo$transition, scores, 0.15, 100)
> pval_ratio <- abs(log(pval_a[seq(nrow(scores)),1]) - log(pval_a[seq(nrow(scores)) + nrow(scores), 1]))
>
> test_score <- test_pwm
> for(i in seq(nrow(test_score))) {
+ for(j in seq(ncol(test_score))) {
+ test_score[i, j] <- exp(mean(log(test_pwm[i, j] / test_pwm[i, -j])))
+ }
+ }
>
> adj_mat <- test_pwm + 0.25
> motif_len <- nrow(test_pwm)
>
> ## these are functions for this test only
> drawonesample <- function(theta) {
+ prob_start <- rev(rowSums(test_score ^ theta) / rowSums(adj_mat))
+ id <- sample(seq(motif_len), 1, prob = prob_start)
+ sample <- sample(1:4, 2 * motif_len - 1, replace = TRUE, prob = snpInfo$prior)
+ delta <- adj_mat
+ delta[motif_len - id + 1, ] <- test_score[motif_len - id + 1, ] ^ theta
+ sample[id - 1 + seq(motif_len)] <- apply(delta, 1, function(x) sample(seq(4), 1, prob = x))
+ ## compute weight
+ sc <- 0
+ for(s in seq(motif_len)) {
+ delta <- adj_mat
+ delta[motif_len + 1 - s, ] <- test_score[motif_len + 1 - s, ] ^ theta
+ sc <- sc + prod(delta[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)])]) /
+ prod(snpInfo$prior[sample[s - 1 + seq(motif_len)]])
+ }
+ sample <- c(sample, id, sc)
+ return(sample)
+ }
> jointprob <- function(x) prod(test_pwm[cbind(seq(motif_len), x)])
> maxjointprob <- function(x) {
+ maxp <- -Inf
+ p <- -Inf
+ for(i in 1:motif_len) {
+ p <- jointprob(x[i:(i+motif_len - 1)])
+ if(p > maxp)
+ maxp <- p
+ }
+ for(i in 1:motif_len) {
+ p <- jointprob(5 - x[(i+motif_len - 1):i])
+ if(p > maxp)
+ maxp <- p
+ }
+ return(maxp)
+ }
> get_freq <- function(sample) {
+ emp_freq <- matrix(0, nrow = 2 * motif_len - 1, ncol = 4)
+ for(i in seq(2 * motif_len - 1)) {
+ for(j in seq(4)) {
+ emp_freq[i, j] <- sum(sample[i, ] == j - 1)
+ }
+ }
+ emp_freq <- emp_freq / rowSums(emp_freq)
+ return(emp_freq)
+ }
>
> test_that("Error: quantile function computing are not equivalent.", {
+ for(p in c(0.01, 0.1, 0.5, 0.9, 0.99) ) {
+ delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")
+ delta.r <- as.double(sort(abs(scores[,2]-scores[,1]))[ceiling((1 - p) * (nrow(scores)))])
+ expect_equal(delta, delta.r)
+ }
+ })
>
> test_that("Error: the scores for samples are not equivalent.", {
+ p <- 0.1
+ delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_change", test_score, adj_mat, delta, package = "atSNP")
+ ## Use R code to generate a random sample
+ for(i in seq(10)) {
+ sample <- drawonesample(theta)
+ sample_score <- .Call("test_compute_sample_score_change", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)] - 1, snpInfo$prior, trans_mat, sample[2 * motif_len] - 1, theta, package = "atSNP")
+ expect_equal(sample[2 * motif_len + 1], sample_score[1])
+ sample1 <- sample2 <- sample3 <- sample
+ sample1[motif_len] <- seq(4)[-sample[motif_len]][1]
+ sample2[motif_len] <- seq(4)[-sample[motif_len]][2]
+ sample3[motif_len] <- seq(4)[-sample[motif_len]][3]
+ sample_score_r <- log(maxjointprob(sample[seq(2 * motif_len - 1)])) -
+ log(c(maxjointprob(sample1[seq(2 * motif_len - 1)]),
+ maxjointprob(sample2[seq(2 * motif_len - 1)]),
+ maxjointprob(sample3[seq(2 * motif_len - 1)])))
+ expect_equal(sample_score_r, sample_score[2:4])
+ }
+
+ ## Use C code to generate a random sample
+ for(i in seq(10)) {
+ sample <- .Call("test_importance_sample_change", test_score, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP")
+ start_pos <- sample[2 * motif_len] + 1
+ adj_score <- 0
+ for(s in seq_len(motif_len)) {
+ adj_s <- sum(log(adj_mat[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)] + 1)]) -
+ log(snpInfo$prior[sample[s - 1 + seq(motif_len)] + 1]))
+ adj_s <- adj_s + theta * log(test_score[motif_len + 1 - s, sample[motif_len] + 1]) -
+ log(adj_mat[motif_len + 1 - s, sample[motif_len] + 1])
+ adj_score <- adj_score + exp(adj_s)
+ }
+ sample_score <- .Call("test_compute_sample_score_change", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)], snpInfo$prior, trans_mat, sample[2 * motif_len], theta, package = "atSNP")
+ expect_equal(adj_score, sample_score[1])
+ }
+ })
>
> test_that("Error: compute the normalizing constant.", {
+ ## parameters
+ for(p in seq(9) / 10) {
+ delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_change", test_score, adj_mat, delta, package = "atSNP")
+ const <- .Call("test_func_delta_change", test_score, adj_mat, theta, package = "atSNP")
+ ## in R
+ adj_sum <- rowSums(adj_mat)
+ wei_sum <- rowSums(test_score ^ theta)
+ const.r <- prod(adj_sum) * sum(wei_sum / adj_sum)
+ expect_equal(const, const.r)
+ }
+ })
>
> test_that("Error: sample distributions are not expected.", {
+ ## parameters
+ p <- 0.1
+ delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_change", test_score, adj_mat, delta, package = "atSNP")
+ prob_start <- rev(rowSums(test_score ^ theta) / rowSums(adj_mat))
+ ## construct the delta matrix
+ delta <- matrix(1, nrow = 4 * motif_len, ncol = 2 * motif_len - 1)
+ for(pos in seq(motif_len)) {
+ delta[seq(4) + 4 * (pos - 1), ] <- snpInfo$prior
+ delta[seq(4) + 4 * (pos - 1), pos - 1 + seq(motif_len)] <- t(test_pwm)
+ delta[seq(4) + 4 * (pos - 1), motif_len] <- test_score[motif_len + 1 - pos, ] ^ theta
+ delta[seq(4) + 4 * (pos - 1), ] <- delta[seq(4) + 4 * (pos - 1),] / rep(colSums(delta[seq(4) + 4 * (pos - 1), ]), each = 4)
+ }
+ target_freq <- matrix(0, nrow = 4, ncol = 2 * motif_len - 1)
+ for(pos in seq(motif_len)) {
+ target_freq <- target_freq + delta[seq(4) + 4 * (pos - 1), ] * prob_start[pos]
+ }
+ target_freq <- t(target_freq)
+ target_freq <- target_freq / rowSums(target_freq)
+
+ results_i <- function(i) {
+ ## generate 100 samples
+ sample1 <- sapply(seq(100), function(x)
+ .Call("test_importance_sample_change",
+ adj_mat, snpInfo$prior, trans_mat, test_score, theta, package = "atSNP"))
+ emp_freq1 <- get_freq(sample1)
+ sample2 <- sapply(rep(theta, 100), drawonesample)
+ emp_freq2 <- get_freq(sample2 - 1)
+ ## print(rbind(emp_freq1[10, ], emp_freq2[10, ], target_freq[10, ]))
+ max(abs(emp_freq1 - target_freq)) > max(abs(emp_freq2 - target_freq))
+ }
+
+ if(Sys.info()[["sysname"]] == "Windows"){
+ snow <- SnowParam(workers = 1, type = "SOCK")
+ results<-bpmapply(results_i, seq(20), BPPARAM = snow,SIMPLIFY = FALSE)
+ }else{
+ results<-bpmapply(results_i, seq(20), BPPARAM = MulticoreParam(workers = 1),
+ SIMPLIFY = FALSE)
+ }
+
+ print(sum(unlist(results)))
+ print(pbinom(sum(unlist(results)), size = 20, prob = 0.5))
+ })
[1] 8
[1] 0.2517223
>
> test_that("Error: the chosen pvalues should have the smaller variance.", {
+ .structure_diff <- function(pval_mat) {
+ id <- apply(pval_mat[, c(2, 4)], 1, which.min)
+ return(cbind(pval_mat[, c(1, 3)][cbind(seq_along(id), id)],
+ pval_mat[, c(2, 4)][cbind(seq_along(id), id)]))
+ }
+ for(p in c(0.05, 0.1, 0.2, 0.5)) {
+ p_values <- .Call("test_p_value_change", test_pwm, test_score, adj_mat, snpInfo$prior, snpInfo$transition, score_diff, pval_ratio, quantile(score_diff, 1 - p), 100, package = "atSNP")$score
+ p_values_s <- .structure_diff(p_values)
+ expect_equal(p_values_s[, 2], apply(p_values[, c(2, 4)], 1, min))
+ }
+ })
>
> proc.time()
user system elapsed
12.65 0.60 13.25
|
|
atSNP.Rcheck/tests_i386/test_diff.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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.
> library(atSNP)
> library(BiocParallel)
> library(testthat)
> data(example)
>
> trans_mat <- matrix(rep(snpInfo$prior, each = 4), nrow = 4)
> test_pwm <- motif_library$SIX5_disc1
> scores <- as.matrix(motif_scores$motif.scores[3:4, 4:5])
> score_diff <- abs(scores[,2]-scores[,1])
>
> test_score <- test_pwm
> for(i in seq(nrow(test_score))) {
+ for(j in seq(ncol(test_score))) {
+ test_score[i, j] <- exp(mean(log(test_pwm[i, j] / test_pwm[i, -j])))
+ }
+ }
>
> adj_mat <- test_pwm + rowMeans(test_pwm)
> motif_len <- nrow(test_pwm)
>
> ## these are functions for this test only
> drawonesample <- function(theta) {
+ prob_start <- sapply(seq(motif_len),
+ function(j)
+ sum(snpInfo$prior * test_score[motif_len + 1 - j, ] ^ theta *
+ adj_mat[motif_len + 1 - j, ]) /
+ sum(snpInfo$prior * adj_mat[motif_len + 1 - j, ])
+ )
+ id <- sample(seq(motif_len), 1, prob = prob_start)
+ sample <- sample(1:4, 2 * motif_len - 1, replace = TRUE, prob = snpInfo$prior)
+ delta <- adj_mat
+ delta[motif_len + 1 - id, ] <- delta[motif_len + 1 - id, ] * test_score[motif_len + 1 - id, ] ^ theta
+ sample[id - 1 + seq(motif_len)] <- apply(delta, 1, function(x)
+ sample(seq(4), 1, prob = x * snpInfo$prior))
+ sc <- 0
+ for(s in seq(motif_len)) {
+ delta <- adj_mat
+ delta[motif_len + 1 - s, ] <- delta[motif_len + 1 - s, ] * test_score[motif_len + 1 - s, ] ^ theta
+ sc <- sc + prod(delta[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)])])
+ }
+ sample <- c(sample, id, sc)
+ return(sample)
+ }
> jointprob <- function(x) prod(test_pwm[cbind(seq(motif_len), x)])
> maxjointprob <- function(x) {
+ maxp <- -Inf
+ p <- -Inf
+ for(i in 1:motif_len) {
+ p <- jointprob(x[i:(i+motif_len - 1)])
+ if(p > maxp)
+ maxp <- p
+ }
+ for(i in 1:motif_len) {
+ p <- jointprob(5 - x[(i+motif_len - 1):i])
+ if(p > maxp)
+ maxp <- p
+ }
+ return(maxp)
+ }
> get_freq <- function(sample) {
+ emp_freq <- matrix(0, nrow = 2 * motif_len - 1, ncol = 4)
+ for(i in seq(2 * motif_len - 1)) {
+ for(j in seq(4)) {
+ emp_freq[i, j] <- sum(sample[i, ] == j - 1)
+ }
+ }
+ emp_freq <- emp_freq / rowSums(emp_freq)
+ return(emp_freq)
+ }
>
> test_that("Error: quantile function computing are not equivalent.", {
+ for(p in c(0.01, 0.1, 0.5, 0.9, 0.99)) {
+ delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")
+ delta.r <- as.double(sort(abs(scores[,2]-scores[,1]))[ceiling((1 - p) * (nrow(scores)))])
+ expect_equal(delta, delta.r)
+ }
+ })
>
> test_that("Error: the scores for samples are not equivalent.", {
+ p <- 0.1
+ delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_diff", test_score, adj_mat, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")
+ ## Use R code to generate a random sample
+ for(i in seq(10)) {
+ sample <- drawonesample(theta)
+ sample_score <- .Call("test_compute_sample_score_diff", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)] - 1, sample[2 * motif_len] - 1, theta, package = "atSNP")
+ expect_equal(sample[2 * motif_len + 1], sample_score[1])
+ sample1 <- sample2 <- sample3 <- sample
+ sample1[motif_len] <- seq(4)[-sample[motif_len]][1]
+ sample2[motif_len] <- seq(4)[-sample[motif_len]][2]
+ sample3[motif_len] <- seq(4)[-sample[motif_len]][3]
+ sample_score_r <- log(maxjointprob(sample[seq(2 * motif_len - 1)])) -
+ log(c(maxjointprob(sample1[seq(2 * motif_len - 1)]),
+ maxjointprob(sample2[seq(2 * motif_len - 1)]),
+ maxjointprob(sample3[seq(2 * motif_len - 1)])))
+ expect_equal(sample_score_r, sample_score[-1])
+ }
+
+ ## Use C code to generate a random sample
+ delta <- matrix(1, nrow = 4 * motif_len, ncol = 2 * motif_len - 1)
+ for(pos in seq(motif_len)) {
+ for(j in (pos + motif_len - 1) : 1) {
+ if(j < pos + motif_len - 1) {
+ delta[4 * (pos - 1) + seq(4), j] <- sum(snpInfo$prior * delta[4 * (pos - 1) + seq(4), j + 1])
+ }
+ if(j >= pos) {
+ delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * adj_mat[j - pos + 1, ]
+ }
+ if(j == motif_len) {
+ delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * test_score[j - pos + 1, ] ^ theta
+ }
+ }
+ }
+ for(i in seq(10)) {
+ sample <- .Call("test_importance_sample_diff", delta, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP")
+ start_pos <- sample[2 * motif_len] + 1
+ adj_score <- 0
+ for(s in seq_len(motif_len)) {
+ adj_s <- sum(log(adj_mat[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)] + 1)]))
+ adj_s <- adj_s + theta * log(test_score[motif_len + 1 - s, sample[motif_len] + 1])
+ adj_score <- adj_score + exp(adj_s)
+ }
+ sample_score <- .Call("test_compute_sample_score_diff", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)], sample[2 * motif_len], theta, package = "atSNP")
+ expect_equal(adj_score, sample_score[1])
+ }
+ })
>
> test_that("Error: compute the normalizing constant.", {
+
+ ## parameters
+ p <- 0.1
+ delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_diff", test_score, adj_mat, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")
+
+ ##
+ const <- .Call("test_func_delta_diff", test_score, adj_mat, snpInfo$prior, trans_mat, theta, package = "atSNP")
+
+ prob_start <- sapply(seq(motif_len),
+ function(j)
+ sum(snpInfo$prior * test_score[motif_len + 1 - j, ] ^ theta *
+ adj_mat[motif_len + 1 - j, ]) /
+ sum(snpInfo$prior * adj_mat[motif_len + 1 - j, ])
+ )
+
+ const.r <- prod(colSums(snpInfo$prior * t(adj_mat))) * sum(prob_start)
+ expect_equal(const, const.r)
+ })
>
> test_that("Error: sample distributions are not expected.", {
+
+ ## parameters
+ p <- 0.1
+ delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_diff", test_score, adj_mat, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")
+
+ ## construct the delta matrix
+ delta <- matrix(1, nrow = 4 * motif_len, ncol = 2 * motif_len - 1)
+ for(pos in seq(motif_len)) {
+ for(j in (pos + motif_len - 1) : 1) {
+ if(j < pos + motif_len - 1) {
+ delta[4 * (pos - 1) + seq(4), j] <- sum(snpInfo$prior * delta[4 * (pos - 1) + seq(4), j + 1])
+ }
+ if(j >= pos) {
+ delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * adj_mat[j - pos + 1, ]
+ }
+ if(j == motif_len) {
+ delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * test_score[j - pos + 1, ] ^ theta
+ }
+ }
+ }
+
+ target_freq <- matrix(0, nrow = 4, ncol = 2 * motif_len - 1)
+
+ mat <- snpInfo$prior * matrix(delta[, 1], nrow = 4)
+ wei <- colSums(mat)
+ for(j in seq(2 * motif_len - 1)) {
+ for(pos in seq(motif_len)) {
+ tmp <- delta[seq(4) + 4 * (pos - 1), j] * snpInfo$prior
+ target_freq[, j] <- target_freq[, j] + tmp / sum(tmp) * wei[pos]
+ }
+ }
+ target_freq <- t(target_freq)
+ target_freq <- target_freq / rowSums(target_freq)
+
+ results_i <- function(i) {
+ ## generate 100 samples
+ sample1 <- sapply(seq(100), function(x)
+ .Call("test_importance_sample_diff",
+ delta, snpInfo$prior, trans_mat, test_score, theta, package = "atSNP"))
+ emp_freq1 <- get_freq(sample1)
+
+ sample2 <- sapply(rep(theta, 100), drawonesample)
+ emp_freq2 <- get_freq(sample2 - 1)
+
+ ## print(rbind(emp_freq1[10, ], emp_freq2[10, ], target_freq[10, ]))
+ max(abs(emp_freq1 - target_freq)) > max(abs(emp_freq2 - target_freq))
+ }
+
+ if(Sys.info()[["sysname"]] == "Windows"){
+ snow <- SnowParam(workers = 1, type = "SOCK")
+ results<-bpmapply(results_i, seq(20), BPPARAM = snow,SIMPLIFY = FALSE)
+ }else{
+ results<-bpmapply(results_i, seq(20), BPPARAM = MulticoreParam(workers = 1),
+ SIMPLIFY = FALSE)
+ }
+
+ print(sum(unlist(results)))
+
+ print(pbinom(sum(unlist(results)), size = 20, prob = 0.5))
+
+ })
[1] 10
[1] 0.5880985
>
> test_that("Error: the chosen pvalues should have the smaller variance.", {
+
+ .structure_diff <- function(pval_mat) {
+ id <- apply(pval_mat[, c(2, 4)], 1, which.min)
+ return(cbind(pval_mat[, c(1, 3)][cbind(seq_along(id), id)],
+ pval_mat[, c(2, 4)][cbind(seq_along(id), id)]))
+ }
+
+ for(p in c(0.05, 0.1, 0.2, 0.5)) {
+ p_values <- .Call("test_p_value_diff", test_pwm, test_score, adj_mat, snpInfo$prior, snpInfo$transition, score_diff, quantile(score_diff, 1 - p), 100, package = "atSNP")
+ p_values_s <- .structure_diff(p_values)
+ expect_equal(p_values_s[, 2], apply(p_values[, c(2, 4)], 1, min))
+ }
+ })
>
> proc.time()
user system elapsed
12.20 0.92 13.10
|
atSNP.Rcheck/tests_x64/test_diff.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(atSNP)
> library(BiocParallel)
> library(testthat)
> data(example)
>
> trans_mat <- matrix(rep(snpInfo$prior, each = 4), nrow = 4)
> test_pwm <- motif_library$SIX5_disc1
> scores <- as.matrix(motif_scores$motif.scores[3:4, 4:5])
> score_diff <- abs(scores[,2]-scores[,1])
>
> test_score <- test_pwm
> for(i in seq(nrow(test_score))) {
+ for(j in seq(ncol(test_score))) {
+ test_score[i, j] <- exp(mean(log(test_pwm[i, j] / test_pwm[i, -j])))
+ }
+ }
>
> adj_mat <- test_pwm + rowMeans(test_pwm)
> motif_len <- nrow(test_pwm)
>
> ## these are functions for this test only
> drawonesample <- function(theta) {
+ prob_start <- sapply(seq(motif_len),
+ function(j)
+ sum(snpInfo$prior * test_score[motif_len + 1 - j, ] ^ theta *
+ adj_mat[motif_len + 1 - j, ]) /
+ sum(snpInfo$prior * adj_mat[motif_len + 1 - j, ])
+ )
+ id <- sample(seq(motif_len), 1, prob = prob_start)
+ sample <- sample(1:4, 2 * motif_len - 1, replace = TRUE, prob = snpInfo$prior)
+ delta <- adj_mat
+ delta[motif_len + 1 - id, ] <- delta[motif_len + 1 - id, ] * test_score[motif_len + 1 - id, ] ^ theta
+ sample[id - 1 + seq(motif_len)] <- apply(delta, 1, function(x)
+ sample(seq(4), 1, prob = x * snpInfo$prior))
+ sc <- 0
+ for(s in seq(motif_len)) {
+ delta <- adj_mat
+ delta[motif_len + 1 - s, ] <- delta[motif_len + 1 - s, ] * test_score[motif_len + 1 - s, ] ^ theta
+ sc <- sc + prod(delta[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)])])
+ }
+ sample <- c(sample, id, sc)
+ return(sample)
+ }
> jointprob <- function(x) prod(test_pwm[cbind(seq(motif_len), x)])
> maxjointprob <- function(x) {
+ maxp <- -Inf
+ p <- -Inf
+ for(i in 1:motif_len) {
+ p <- jointprob(x[i:(i+motif_len - 1)])
+ if(p > maxp)
+ maxp <- p
+ }
+ for(i in 1:motif_len) {
+ p <- jointprob(5 - x[(i+motif_len - 1):i])
+ if(p > maxp)
+ maxp <- p
+ }
+ return(maxp)
+ }
> get_freq <- function(sample) {
+ emp_freq <- matrix(0, nrow = 2 * motif_len - 1, ncol = 4)
+ for(i in seq(2 * motif_len - 1)) {
+ for(j in seq(4)) {
+ emp_freq[i, j] <- sum(sample[i, ] == j - 1)
+ }
+ }
+ emp_freq <- emp_freq / rowSums(emp_freq)
+ return(emp_freq)
+ }
>
> test_that("Error: quantile function computing are not equivalent.", {
+ for(p in c(0.01, 0.1, 0.5, 0.9, 0.99)) {
+ delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")
+ delta.r <- as.double(sort(abs(scores[,2]-scores[,1]))[ceiling((1 - p) * (nrow(scores)))])
+ expect_equal(delta, delta.r)
+ }
+ })
>
> test_that("Error: the scores for samples are not equivalent.", {
+ p <- 0.1
+ delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_diff", test_score, adj_mat, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")
+ ## Use R code to generate a random sample
+ for(i in seq(10)) {
+ sample <- drawonesample(theta)
+ sample_score <- .Call("test_compute_sample_score_diff", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)] - 1, sample[2 * motif_len] - 1, theta, package = "atSNP")
+ expect_equal(sample[2 * motif_len + 1], sample_score[1])
+ sample1 <- sample2 <- sample3 <- sample
+ sample1[motif_len] <- seq(4)[-sample[motif_len]][1]
+ sample2[motif_len] <- seq(4)[-sample[motif_len]][2]
+ sample3[motif_len] <- seq(4)[-sample[motif_len]][3]
+ sample_score_r <- log(maxjointprob(sample[seq(2 * motif_len - 1)])) -
+ log(c(maxjointprob(sample1[seq(2 * motif_len - 1)]),
+ maxjointprob(sample2[seq(2 * motif_len - 1)]),
+ maxjointprob(sample3[seq(2 * motif_len - 1)])))
+ expect_equal(sample_score_r, sample_score[-1])
+ }
+
+ ## Use C code to generate a random sample
+ delta <- matrix(1, nrow = 4 * motif_len, ncol = 2 * motif_len - 1)
+ for(pos in seq(motif_len)) {
+ for(j in (pos + motif_len - 1) : 1) {
+ if(j < pos + motif_len - 1) {
+ delta[4 * (pos - 1) + seq(4), j] <- sum(snpInfo$prior * delta[4 * (pos - 1) + seq(4), j + 1])
+ }
+ if(j >= pos) {
+ delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * adj_mat[j - pos + 1, ]
+ }
+ if(j == motif_len) {
+ delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * test_score[j - pos + 1, ] ^ theta
+ }
+ }
+ }
+ for(i in seq(10)) {
+ sample <- .Call("test_importance_sample_diff", delta, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP")
+ start_pos <- sample[2 * motif_len] + 1
+ adj_score <- 0
+ for(s in seq_len(motif_len)) {
+ adj_s <- sum(log(adj_mat[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)] + 1)]))
+ adj_s <- adj_s + theta * log(test_score[motif_len + 1 - s, sample[motif_len] + 1])
+ adj_score <- adj_score + exp(adj_s)
+ }
+ sample_score <- .Call("test_compute_sample_score_diff", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)], sample[2 * motif_len], theta, package = "atSNP")
+ expect_equal(adj_score, sample_score[1])
+ }
+ })
>
> test_that("Error: compute the normalizing constant.", {
+
+ ## parameters
+ p <- 0.1
+ delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_diff", test_score, adj_mat, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")
+
+ ##
+ const <- .Call("test_func_delta_diff", test_score, adj_mat, snpInfo$prior, trans_mat, theta, package = "atSNP")
+
+ prob_start <- sapply(seq(motif_len),
+ function(j)
+ sum(snpInfo$prior * test_score[motif_len + 1 - j, ] ^ theta *
+ adj_mat[motif_len + 1 - j, ]) /
+ sum(snpInfo$prior * adj_mat[motif_len + 1 - j, ])
+ )
+
+ const.r <- prod(colSums(snpInfo$prior * t(adj_mat))) * sum(prob_start)
+ expect_equal(const, const.r)
+ })
>
> test_that("Error: sample distributions are not expected.", {
+
+ ## parameters
+ p <- 0.1
+ delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")
+ theta <- .Call("test_find_theta_diff", test_score, adj_mat, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")
+
+ ## construct the delta matrix
+ delta <- matrix(1, nrow = 4 * motif_len, ncol = 2 * motif_len - 1)
+ for(pos in seq(motif_len)) {
+ for(j in (pos + motif_len - 1) : 1) {
+ if(j < pos + motif_len - 1) {
+ delta[4 * (pos - 1) + seq(4), j] <- sum(snpInfo$prior * delta[4 * (pos - 1) + seq(4), j + 1])
+ }
+ if(j >= pos) {
+ delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * adj_mat[j - pos + 1, ]
+ }
+ if(j == motif_len) {
+ delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * test_score[j - pos + 1, ] ^ theta
+ }
+ }
+ }
+
+ target_freq <- matrix(0, nrow = 4, ncol = 2 * motif_len - 1)
+
+ mat <- snpInfo$prior * matrix(delta[, 1], nrow = 4)
+ wei <- colSums(mat)
+ for(j in seq(2 * motif_len - 1)) {
+ for(pos in seq(motif_len)) {
+ tmp <- delta[seq(4) + 4 * (pos - 1), j] * snpInfo$prior
+ target_freq[, j] <- target_freq[, j] + tmp / sum(tmp) * wei[pos]
+ }
+ }
+ target_freq <- t(target_freq)
+ target_freq <- target_freq / rowSums(target_freq)
+
+ results_i <- function(i) {
+ ## generate 100 samples
+ sample1 <- sapply(seq(100), function(x)
+ .Call("test_importance_sample_diff",
+ delta, snpInfo$prior, trans_mat, test_score, theta, package = "atSNP"))
+ emp_freq1 <- get_freq(sample1)
+
+ sample2 <- sapply(rep(theta, 100), drawonesample)
+ emp_freq2 <- get_freq(sample2 - 1)
+
+ ## print(rbind(emp_freq1[10, ], emp_freq2[10, ], target_freq[10, ]))
+ max(abs(emp_freq1 - target_freq)) > max(abs(emp_freq2 - target_freq))
+ }
+
+ if(Sys.info()[["sysname"]] == "Windows"){
+ snow <- SnowParam(workers = 1, type = "SOCK")
+ results<-bpmapply(results_i, seq(20), BPPARAM = snow,SIMPLIFY = FALSE)
+ }else{
+ results<-bpmapply(results_i, seq(20), BPPARAM = MulticoreParam(workers = 1),
+ SIMPLIFY = FALSE)
+ }
+
+ print(sum(unlist(results)))
+
+ print(pbinom(sum(unlist(results)), size = 20, prob = 0.5))
+
+ })
[1] 8
[1] 0.2517223
>
> test_that("Error: the chosen pvalues should have the smaller variance.", {
+
+ .structure_diff <- function(pval_mat) {
+ id <- apply(pval_mat[, c(2, 4)], 1, which.min)
+ return(cbind(pval_mat[, c(1, 3)][cbind(seq_along(id), id)],
+ pval_mat[, c(2, 4)][cbind(seq_along(id), id)]))
+ }
+
+ for(p in c(0.05, 0.1, 0.2, 0.5)) {
+ p_values <- .Call("test_p_value_diff", test_pwm, test_score, adj_mat, snpInfo$prior, snpInfo$transition, score_diff, quantile(score_diff, 1 - p), 100, package = "atSNP")
+ p_values_s <- .structure_diff(p_values)
+ expect_equal(p_values_s[, 2], apply(p_values[, c(2, 4)], 1, min))
+ }
+ })
>
> proc.time()
user system elapsed
16.90 0.46 17.35
|
|
atSNP.Rcheck/tests_i386/test_is.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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.
> library(atSNP)
> library(BiocParallel)
> library(testthat)
> data(example)
>
> trans_mat <- matrix(rep(snpInfo$prior, each = 4), nrow = 4)
> test_pwm <- motif_library$SIX5_disc1
> scores <- as.matrix(motif_scores$motif.scores[3:4, 4:5])
>
> motif_len <- nrow(test_pwm)
>
> ## these are functions for this test only
> drawonesample <- function(theta) {
+ delta <- snpInfo$prior * t(test_pwm ^ theta)
+ delta <- delta / rep(colSums(delta), each = 4)
+ sample <- sample(1:4, 2 * motif_len - 1, replace = TRUE, prob = snpInfo$prior)
+ id <- sample(seq(motif_len), 1)
+ sample[id : (id + motif_len - 1)] <- apply(delta, 2, function(x) sample(1:4, 1, prob = x))
+ sc <- s_cond <- 0
+ for(s in seq(motif_len)) {
+ sc <- sc + prod(test_pwm[cbind(seq(motif_len),
+ sample[s : (s + motif_len - 1)])]) ^ theta
+ }
+ s_cond <- prod(test_pwm[cbind(seq(motif_len),
+ sample[id : (id + motif_len - 1)])]) ^ theta
+ sample <- c(sample, id, sc, s_cond)
+ return(sample)
+ }
> jointprob <- function(x) prod(test_pwm[cbind(seq(motif_len), x)])
> maxjointprob <- function(x) {
+ maxp <- -Inf
+ p <- -Inf
+ for(i in 1:motif_len) {
+ p <- jointprob(x[i:(i+motif_len - 1)])
+ if(p > maxp)
+ maxp <- p
+ }
+ for(i in 1:motif_len) {
+ p <- jointprob(5 - x[(i+motif_len - 1):i])
+ if(p > maxp)
+ maxp <- p
+ }
+ return(maxp)
+ }
> get_freq <- function(sample) {
+ ids <- cbind(
+ rep(sample[motif_len * 2, ], each = motif_len) + seq(motif_len),
+ rep(seq(100), each = motif_len))
+ sample_motif <- matrix(sample[ids], nrow = motif_len) + 1
+ emp_freq <- matrix(0, nrow = motif_len, ncol = 4)
+ for(i in seq(motif_len)) {
+ for(j in seq(4)) {
+ emp_freq[i, j] <- sum(sample_motif[i, ] == j)
+ }
+ }
+ emp_freq <- emp_freq / rowSums(emp_freq)
+ return(emp_freq)
+ }
>
> test_that("Error: quantile function computing are not equivalent.", {
+ for(p in c(0.01, 0.1, 0.5, 0.9, 0.99)) {
+ delta <- .Call("test_find_percentile", c(scores), p, package = "atSNP")
+ delta.r <- -sort(-c(scores))[as.integer(p * length(scores)) + 1]
+ delta==delta.r
+ expect_equal(delta, delta.r)
+ }
+ })
>
> test_that("Error: the scores for samples are not equivalent.", {
+ p <- 0.01
+ delta <- .Call("test_find_percentile", scores, p, package = "atSNP")
+ theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")
+ ## Use R code to generate a random sample
+ for(i in seq(10)) {
+ sample <- drawonesample(theta)
+ sample_score <- .Call("test_compute_sample_score", test_pwm, sample[seq(2 * motif_len - 1)] - 1, sample[motif_len * 2] - 1, theta, package = "atSNP")
+ expect_equal(sample[2 * motif_len + 1], sample_score[2])
+ expect_equal(sample[2 * motif_len + 2], sample_score[3])
+ }
+ ## Use C code to generate a random sample
+ for(i in seq(10)) {
+ delta <- t(test_pwm ^ theta)
+ delta <- cbind(matrix(
+ sum(snpInfo$prior * delta[, 1]),
+ nrow = 4, ncol = motif_len - 1), delta)
+ sample <- .Call("test_importance_sample", delta, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP")
+ start_pos <- sample[motif_len * 2]
+ adj_score <- 0
+ for(s in seq(motif_len) - 1) {
+ adj_score <- adj_score + prod(test_pwm[cbind(seq(motif_len),
+ sample[s + seq(motif_len)] + 1)]) ^ theta
+ }
+ adj_score_cond <- prod(test_pwm[cbind(seq(motif_len), sample[start_pos + seq(motif_len)] + 1)]) ^ theta
+ sample_score <- .Call("test_compute_sample_score", test_pwm, sample[seq(2 * motif_len - 1)], sample[motif_len * 2], theta, package = "atSNP")
+ expect_equal(adj_score, sample_score[2])
+ expect_equal(adj_score_cond, sample_score[3])
+ }
+ })
>
> test_that("Error: compute the normalizing constant.", {
+ ## parameters
+ p <- 0.01
+ delta <- .Call("test_find_percentile", scores, p, package = "atSNP")
+ theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")
+ ##
+ const <- .Call("test_func_delta", test_pwm, snpInfo$prior, trans_mat, theta, package = "atSNP")
+ const.r <- prod(colSums(snpInfo$prior * t(test_pwm) ^ theta)) * motif_len
+ expect_equal(abs(const - const.r) / const < 1e-5, TRUE)
+ })
>
> test_that("Error: sample distributions are not expected.", {
+ ## parameters
+ p <- 0.1
+ delta <- .Call("test_find_percentile", scores, p, package = "atSNP")
+ theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, trans_mat, delta, package = "atSNP")
+ delta <- t(test_pwm ^ theta)
+ delta <- cbind(matrix(
+ sum(snpInfo$prior * delta[, 1]),
+ nrow = 4, ncol = motif_len - 1), delta)
+
+ results_i <- function(i) {
+ ## generate 100 samples
+ sample <- sapply(seq(100), function(x)
+ .Call("test_importance_sample",
+ delta, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP"))
+ emp_freq1 <- get_freq(sample)
+ target_freq <- test_pwm ^ theta * snpInfo$prior
+ target_freq <- target_freq / rowSums(target_freq)
+ ## generate samples in R
+ sample <- sapply(rep(theta, 100), drawonesample)
+ emp_freq2 <- get_freq(sample[seq(2 * motif_len), ] - 1)
+ max(abs(emp_freq1 - target_freq)) > max(abs(emp_freq2 - target_freq))
+ }
+
+ if(Sys.info()[["sysname"]] == "Windows"){
+ snow <- SnowParam(workers = 1, type = "SOCK")
+ results<-bpmapply(results_i, seq(20), BPPARAM = snow,SIMPLIFY = FALSE)
+ }else{
+ results<-bpmapply(results_i, seq(20), BPPARAM = MulticoreParam(workers = 1),
+ SIMPLIFY = FALSE)
+ }
+
+ print(sum(unlist(results)))
+ print(pbinom(sum(unlist(results)), size = 20, prob = 0.5))
+ })
[1] 13
[1] 0.9423409
>
> test_that("Error: the chosen pvalues should have the smaller variance.", {
+ .structure <- function(pval_mat) {
+ id1 <- apply(pval_mat[, c(2, 4)], 1, which.min)
+ return(cbind(
+ pval_mat[, c(1, 3)][cbind(seq_along(id1), id1)],
+ pval_mat[, c(2, 4)][cbind(seq_along(id1), id1)])
+ )
+ }
+ for(p in c(0.01, 0.05, 0.1)) {
+ theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, trans_mat, quantile(c(scores), 1 - p), package = "atSNP")
+ p_values <- .Call("test_p_value", test_pwm, snpInfo$prior, snpInfo$transition, c(scores), theta, 100, package = "atSNP")
+ p_values_s <- .structure(p_values)
+ expect_equal(p_values_s[, 2], apply(p_values[, c(2, 4)], 1, min))
+ }
+ })
>
> proc.time()
user system elapsed
10.68 0.84 11.51
|
atSNP.Rcheck/tests_x64/test_is.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 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.
> library(atSNP)
> library(BiocParallel)
> library(testthat)
> data(example)
>
> trans_mat <- matrix(rep(snpInfo$prior, each = 4), nrow = 4)
> test_pwm <- motif_library$SIX5_disc1
> scores <- as.matrix(motif_scores$motif.scores[3:4, 4:5])
>
> motif_len <- nrow(test_pwm)
>
> ## these are functions for this test only
> drawonesample <- function(theta) {
+ delta <- snpInfo$prior * t(test_pwm ^ theta)
+ delta <- delta / rep(colSums(delta), each = 4)
+ sample <- sample(1:4, 2 * motif_len - 1, replace = TRUE, prob = snpInfo$prior)
+ id <- sample(seq(motif_len), 1)
+ sample[id : (id + motif_len - 1)] <- apply(delta, 2, function(x) sample(1:4, 1, prob = x))
+ sc <- s_cond <- 0
+ for(s in seq(motif_len)) {
+ sc <- sc + prod(test_pwm[cbind(seq(motif_len),
+ sample[s : (s + motif_len - 1)])]) ^ theta
+ }
+ s_cond <- prod(test_pwm[cbind(seq(motif_len),
+ sample[id : (id + motif_len - 1)])]) ^ theta
+ sample <- c(sample, id, sc, s_cond)
+ return(sample)
+ }
> jointprob <- function(x) prod(test_pwm[cbind(seq(motif_len), x)])
> maxjointprob <- function(x) {
+ maxp <- -Inf
+ p <- -Inf
+ for(i in 1:motif_len) {
+ p <- jointprob(x[i:(i+motif_len - 1)])
+ if(p > maxp)
+ maxp <- p
+ }
+ for(i in 1:motif_len) {
+ p <- jointprob(5 - x[(i+motif_len - 1):i])
+ if(p > maxp)
+ maxp <- p
+ }
+ return(maxp)
+ }
> get_freq <- function(sample) {
+ ids <- cbind(
+ rep(sample[motif_len * 2, ], each = motif_len) + seq(motif_len),
+ rep(seq(100), each = motif_len))
+ sample_motif <- matrix(sample[ids], nrow = motif_len) + 1
+ emp_freq <- matrix(0, nrow = motif_len, ncol = 4)
+ for(i in seq(motif_len)) {
+ for(j in seq(4)) {
+ emp_freq[i, j] <- sum(sample_motif[i, ] == j)
+ }
+ }
+ emp_freq <- emp_freq / rowSums(emp_freq)
+ return(emp_freq)
+ }
>
> test_that("Error: quantile function computing are not equivalent.", {
+ for(p in c(0.01, 0.1, 0.5, 0.9, 0.99)) {
+ delta <- .Call("test_find_percentile", c(scores), p, package = "atSNP")
+ delta.r <- -sort(-c(scores))[as.integer(p * length(scores)) + 1]
+ delta==delta.r
+ expect_equal(delta, delta.r)
+ }
+ })
>
> test_that("Error: the scores for samples are not equivalent.", {
+ p <- 0.01
+ delta <- .Call("test_find_percentile", scores, p, package = "atSNP")
+ theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")
+ ## Use R code to generate a random sample
+ for(i in seq(10)) {
+ sample <- drawonesample(theta)
+ sample_score <- .Call("test_compute_sample_score", test_pwm, sample[seq(2 * motif_len - 1)] - 1, sample[motif_len * 2] - 1, theta, package = "atSNP")
+ expect_equal(sample[2 * motif_len + 1], sample_score[2])
+ expect_equal(sample[2 * motif_len + 2], sample_score[3])
+ }
+ ## Use C code to generate a random sample
+ for(i in seq(10)) {
+ delta <- t(test_pwm ^ theta)
+ delta <- cbind(matrix(
+ sum(snpInfo$prior * delta[, 1]),
+ nrow = 4, ncol = motif_len - 1), delta)
+ sample <- .Call("test_importance_sample", delta, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP")
+ start_pos <- sample[motif_len * 2]
+ adj_score <- 0
+ for(s in seq(motif_len) - 1) {
+ adj_score <- adj_score + prod(test_pwm[cbind(seq(motif_len),
+ sample[s + seq(motif_len)] + 1)]) ^ theta
+ }
+ adj_score_cond <- prod(test_pwm[cbind(seq(motif_len), sample[start_pos + seq(motif_len)] + 1)]) ^ theta
+ sample_score <- .Call("test_compute_sample_score", test_pwm, sample[seq(2 * motif_len - 1)], sample[motif_len * 2], theta, package = "atSNP")
+ expect_equal(adj_score, sample_score[2])
+ expect_equal(adj_score_cond, sample_score[3])
+ }
+ })
>
> test_that("Error: compute the normalizing constant.", {
+ ## parameters
+ p <- 0.01
+ delta <- .Call("test_find_percentile", scores, p, package = "atSNP")
+ theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")
+ ##
+ const <- .Call("test_func_delta", test_pwm, snpInfo$prior, trans_mat, theta, package = "atSNP")
+ const.r <- prod(colSums(snpInfo$prior * t(test_pwm) ^ theta)) * motif_len
+ expect_equal(abs(const - const.r) / const < 1e-5, TRUE)
+ })
>
> test_that("Error: sample distributions are not expected.", {
+ ## parameters
+ p <- 0.1
+ delta <- .Call("test_find_percentile", scores, p, package = "atSNP")
+ theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, trans_mat, delta, package = "atSNP")
+ delta <- t(test_pwm ^ theta)
+ delta <- cbind(matrix(
+ sum(snpInfo$prior * delta[, 1]),
+ nrow = 4, ncol = motif_len - 1), delta)
+
+ results_i <- function(i) {
+ ## generate 100 samples
+ sample <- sapply(seq(100), function(x)
+ .Call("test_importance_sample",
+ delta, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP"))
+ emp_freq1 <- get_freq(sample)
+ target_freq <- test_pwm ^ theta * snpInfo$prior
+ target_freq <- target_freq / rowSums(target_freq)
+ ## generate samples in R
+ sample <- sapply(rep(theta, 100), drawonesample)
+ emp_freq2 <- get_freq(sample[seq(2 * motif_len), ] - 1)
+ max(abs(emp_freq1 - target_freq)) > max(abs(emp_freq2 - target_freq))
+ }
+
+ if(Sys.info()[["sysname"]] == "Windows"){
+ snow <- SnowParam(workers = 1, type = "SOCK")
+ results<-bpmapply(results_i, seq(20), BPPARAM = snow,SIMPLIFY = FALSE)
+ }else{
+ results<-bpmapply(results_i, seq(20), BPPARAM = MulticoreParam(workers = 1),
+ SIMPLIFY = FALSE)
+ }
+
+ print(sum(unlist(results)))
+ print(pbinom(sum(unlist(results)), size = 20, prob = 0.5))
+ })
[1] 8
[1] 0.2517223
>
> test_that("Error: the chosen pvalues should have the smaller variance.", {
+ .structure <- function(pval_mat) {
+ id1 <- apply(pval_mat[, c(2, 4)], 1, which.min)
+ return(cbind(
+ pval_mat[, c(1, 3)][cbind(seq_along(id1), id1)],
+ pval_mat[, c(2, 4)][cbind(seq_along(id1), id1)])
+ )
+ }
+ for(p in c(0.01, 0.05, 0.1)) {
+ theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, trans_mat, quantile(c(scores), 1 - p), package = "atSNP")
+ p_values <- .Call("test_p_value", test_pwm, snpInfo$prior, snpInfo$transition, c(scores), theta, 100, package = "atSNP")
+ p_values_s <- .structure(p_values)
+ expect_equal(p_values_s[, 2], apply(p_values[, c(2, 4)], 1, min))
+ }
+ })
>
> proc.time()
user system elapsed
14.37 0.34 14.70
|
|
atSNP.Rcheck/examples_i386/atSNP-Ex.timings
|
atSNP.Rcheck/examples_x64/atSNP-Ex.timings
|