R Under development (unstable) (2024-02-16 r85931 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 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("ACNE") Loading required package: aroma.affymetrix Loading required package: R.utils Loading required package: R.oo Loading required package: R.methodsS3 R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help. R.oo v1.26.0 (2024-01-24 05:12:50 UTC) successfully loaded. See ?R.oo for help. Attaching package: 'R.oo' The following object is masked from 'package:R.methodsS3': throw The following objects are masked from 'package:methods': getClasses, getMethods The following objects are masked from 'package:base': attach, detach, load, save R.utils v2.12.3 (2023-11-18 01:00:02 UTC) successfully loaded. See ?R.utils for help. Attaching package: 'R.utils' The following object is masked from 'package:utils': timestamp The following objects are masked from 'package:base': cat, commandArgs, getOption, isOpen, nullfile, parse, use, warnings Loading required package: aroma.core Loading required package: R.filesets R.filesets v2.15.1 (2024-01-24 17:22:49 UTC) successfully loaded. See ?R.filesets for help. Attaching package: 'R.filesets' The following object is masked from 'package:R.utils': validate The following objects are masked from 'package:base': append, readLines Loading required package: R.devices R.devices v2.17.2 (2024-01-29 13:30:11 UTC) successfully loaded. See ?R.devices for help. aroma.core v3.3.0 (2022-11-15 18:30:13 UTC) successfully loaded. See ?aroma.core for help. Attaching package: 'aroma.core' The following objects are masked from 'package:base': .Machine, colMeans, colSums, library, require, write Loading required package: aroma.light aroma.light v3.32.0 (2023-10-24) successfully loaded. See ?aroma.light for help. Attaching package: 'aroma.light' The following objects are masked from 'package:aroma.affymetrix': averageQuantile, normalizeQuantile, plotDensity, plotMvsA, plotXYCurve The following objects are masked from 'package:aroma.core': callNaiveGenotypes, normalizeTumorBoost Loading required package: affxparser aroma.affymetrix v3.2.1 (2022-07-18 10:50:02 UTC) successfully loaded. See ?aroma.affymetrix for help. Attaching package: 'aroma.affymetrix' The following objects are masked _by_ 'package:aroma.light': averageQuantile, normalizeQuantile, plotDensity, plotMvsA, plotXYCurve ACNE v0.9.1 successfully loaded. See ?ACNE for help. Warning message: In .requireBiocPackage("affxparser", neededBy = getName(pkg)) : Package 'affxparser' could not be loaded. Without it aroma.affymetrix will not work. Please install it from Bioconductor, cf. https://www.bioconductor.org/ > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # DATA: Lx2xI allele-specific signals for six different SNPs > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > filenames <- sprintf("V%d.Rbin", 1:6) > pathnames <- system.file("extData", filenames, package="ACNE") > Ys <- lapply(pathnames, FUN=function(p) snpMatrixToArray(loadToEnv(p)$V)) > names(Ys) <- sprintf("SNP #%d", seq_along(Ys)) > > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # ACNE fitting of NMF to the six SNPs > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > for (name in names(Ys)) { + Y <- Ys[[name]] + fit <- fitSnpNmfArray(Y) + str(fit) + plot(fit, lim=c(0,2^14), main=name) + } List of 8 $ W : num [1:20, 1:2] 1285 1561 1882 2472 1939 ... $ H : num [1:2, 1:64] 0.3743 1.5268 0.1655 1.9309 0.0767 ... $ hasConverged : logi TRUE $ nbrOfIterations: int 7 $ V : num [1:20, 1:64] 1346 1986 1788 1077 1181 ... $ Y : num [1:10, 1:2, 1:64] 1346 1986 1788 1077 1181 ... $ W2 : num [1:10, 1:2, 1:2] 1285 1561 1882 2472 1939 ... $ args : list() - attr(*, "class")= chr [1:2] "list" "SnpNmfFit" num [1:10, 1:2, 1:64] 1346 1986 1788 1077 1181 ... num [1:10, 1:2, 1:64] 1209 1619 1717 1411 1284 ... List of 8 $ W : num [1:20, 1:2] 2377 2018 1943 1835 1514 ... $ H : num [1:2, 1:64] 0.11449 1.14975 0.00001 2.26512 0.04569 ... $ hasConverged : logi TRUE $ nbrOfIterations: int 3 $ V : num [1:20, 1:64] 846 992 1368 732 884 ... $ Y : num [1:10, 1:2, 1:64] 846 992 1368 732 884 ... $ W2 : num [1:10, 1:2, 1:2] 2377 2018 1943 1835 1514 ... $ args : list() - attr(*, "class")= chr [1:2] "list" "SnpNmfFit" num [1:10, 1:2, 1:64] 846 992 1368 732 884 ... num [1:10, 1:2, 1:64] 851 791 747 657 687 ... List of 8 $ W : num [1:20, 1:2] 1981 1905 1837 1949 1678 ... $ H : num [1:2, 1:64] 0.0852 1.7681 0.9981 1.1326 0.1584 ... $ hasConverged : logi TRUE $ nbrOfIterations: int 6 $ V : num [1:20, 1:64] 1109 787 874 766 881 ... $ Y : num [1:10, 1:2, 1:64] 1109 787 874 766 881 ... $ W2 : num [1:10, 1:2, 1:2] 1981 1905 1837 1949 1678 ... $ args : list() - attr(*, "class")= chr [1:2] "list" "SnpNmfFit" num [1:10, 1:2, 1:64] 1109 787 874 766 881 ... num [1:10, 1:2, 1:64] 928 697 681 691 951 ... List of 8 $ W : num [1:20, 1:2] 5800 2374 3126 2872 4921 ... $ H : num [1:2, 1:64] 1.30 9.21e-02 2.34 1.07e-05 1.07 ... $ hasConverged : logi TRUE $ nbrOfIterations: int 5 $ V : num [1:20, 1:64] 6708 3451 4694 3849 5334 ... $ Y : num [1:10, 1:2, 1:64] 6708 3451 4694 3849 5334 ... $ W2 : num [1:10, 1:2, 1:2] 5800 2374 3126 2872 4921 ... $ args : list() - attr(*, "class")= chr [1:2] "list" "SnpNmfFit" num [1:10, 1:2, 1:64] 6708 3451 4694 3849 5334 ... num [1:10, 1:2, 1:64] 7575 3132 4138 3791 6486 ... List of 8 $ W : num [1:20, 1:2] 3853 3389 4416 3080 2968 ... $ H : num [1:2, 1:64] 0.8694 1.2451 0.8925 1.1219 0.0453 ... $ hasConverged : logi TRUE $ nbrOfIterations: int 7 $ V : num [1:20, 1:64] 5181 4257 4253 3400 3720 ... $ Y : num [1:10, 1:2, 1:64] 5181 4257 4253 3400 3720 ... $ W2 : num [1:10, 1:2, 1:2] 3853 3389 4416 3080 2968 ... $ args : list() - attr(*, "class")= chr [1:2] "list" "SnpNmfFit" num [1:10, 1:2, 1:64] 5181 4257 4253 3400 3720 ... num [1:10, 1:2, 1:64] 4666 3783 4914 3562 3462 ... List of 8 $ W : num [1:20, 1:2] 3608 3315 3971 3648 4223 ... $ H : num [1:2, 1:64] 0.16 2.8993 0.1054 2.0966 0.0629 ... $ hasConverged : logi TRUE $ nbrOfIterations: int 6 $ V : num [1:20, 1:64] 5870 3369 5017 2700 4315 ... $ Y : num [1:10, 1:2, 1:64] 5870 3369 5017 2700 4315 ... $ W2 : num [1:10, 1:2, 1:2] 3608 3315 3971 3648 4223 ... $ args : list() - attr(*, "class")= chr [1:2] "list" "SnpNmfFit" num [1:10, 1:2, 1:64] 5870 3369 5017 2700 4315 ... num [1:10, 1:2, 1:64] 4534 2753 4395 3402 4907 ... > > proc.time() user system elapsed 1.17 0.10 1.23