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This page was generated on 2024-07-16 11:40 -0400 (Tue, 16 Jul 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
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nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4677 |
palomino6 | Windows Server 2022 Datacenter | x64 | 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" | 4416 |
lconway | macOS 12.7.1 Monterey | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4444 |
kjohnson3 | macOS 13.6.5 Ventura | arm64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4393 |
palomino8 | Windows Server 2022 Datacenter | x64 | 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" | 4373 |
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 |
Package 665/2243 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
evaluomeR 1.21.6 (landing page) José Antonio Bernabé-Díaz
| nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) / x86_64 | OK | OK | WARNINGS | |||||||||
palomino6 | Windows Server 2022 Datacenter / x64 | OK | OK | WARNINGS | OK | |||||||||
lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | WARNINGS | OK | |||||||||
kjohnson3 | macOS 13.6.5 Ventura / arm64 | OK | OK | WARNINGS | OK | |||||||||
palomino8 | Windows Server 2022 Datacenter / x64 | OK | OK | WARNINGS | OK | |||||||||
To the developers/maintainers of the evaluomeR package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/evaluomeR.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
Package: evaluomeR |
Version: 1.21.6 |
Command: C:\Users\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:evaluomeR.install-out.txt --library=C:\Users\biocbuild\bbs-3.20-bioc\R\library --no-vignettes --timings evaluomeR_1.21.6.tar.gz |
StartedAt: 2024-07-15 23:56:08 -0400 (Mon, 15 Jul 2024) |
EndedAt: 2024-07-16 00:01:00 -0400 (Tue, 16 Jul 2024) |
EllapsedTime: 291.8 seconds |
RetCode: 0 |
Status: WARNINGS |
CheckDir: evaluomeR.Rcheck |
Warnings: 3 |
############################################################################## ############################################################################## ### ### Running command: ### ### C:\Users\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:evaluomeR.install-out.txt --library=C:\Users\biocbuild\bbs-3.20-bioc\R\library --no-vignettes --timings evaluomeR_1.21.6.tar.gz ### ############################################################################## ############################################################################## * using log directory 'C:/Users/biocbuild/bbs-3.20-bioc/meat/evaluomeR.Rcheck' * using R version 4.4.1 (2024-06-14 ucrt) * using platform: x86_64-w64-mingw32 * R was compiled by gcc.exe (GCC) 13.2.0 GNU Fortran (GCC) 13.2.0 * running under: Windows Server 2022 x64 (build 20348) * using session charset: UTF-8 * using option '--no-vignettes' * checking for file 'evaluomeR/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'evaluomeR' version '1.21.6' * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... NOTE Depends: includes the non-default packages: 'SummarizedExperiment', 'MultiAssayExperiment', 'cluster', 'fpc', 'randomForest', 'flexmix', 'RSKC', 'sparcl' Adding so many packages to the search path is excessive and importing selectively is preferable. * 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 'evaluomeR' 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 ... NOTE File LICENSE is not mentioned in the DESCRIPTION file. * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code 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 ... NOTE Namespace in Imports field not imported from: 'kableExtra' All declared Imports should be used. Packages in Depends field not imported from: 'RSKC' 'sparcl' These packages need to be imported from (in the NAMESPACE file) for when this namespace is loaded but not attached. * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... NOTE getMetricRangeByCluster: no visible global function definition for '%>%' getMetricRangeByCluster: no visible binding for global variable 'cluster' getMetricsRelevancy: no visible global function definition for 'RSKC' kmeansruns: no visible global function definition for 'pairs' kmeansruns: no visible global function definition for 'calinhara' kmeansruns: no visible global function definition for 'dudahart2' plotMetricsCluster: no visible global function definition for 'as.dendrogram' rskcCBI: no visible global function definition for 'RSKC' speccCBI: no visible global function definition for 'specc' Undefined global functions or variables: %>% RSKC as.dendrogram calinhara cluster dudahart2 pairs specc Consider adding importFrom("graphics", "pairs") importFrom("stats", "as.dendrogram") to your NAMESPACE file. * checking Rd files ... OK * checking Rd metadata ... WARNING Rd files with duplicated alias 'getMetricRangeByCluster': 'getMetricRangeByCluster.Rd' 'getMetricsRelevancy.Rd' * checking Rd cross-references ... OK * checking for missing documentation entries ... WARNING Undocumented code objects: 'clusterbootWrapper' 'standardizeQualityData' 'standardizeStabilityData' All user-level objects in a package should have documentation entries. See chapter 'Writing R documentation files' in the 'Writing R Extensions' manual. * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... WARNING Undocumented arguments in Rd file 'quality.Rd' '...' Undocumented arguments in Rd file 'qualityRange.Rd' '...' Undocumented arguments in Rd file 'qualitySet.Rd' '...' Undocumented arguments in Rd file 'stability.Rd' '...' Undocumented arguments in Rd file 'stabilityRange.Rd' '...' Undocumented arguments in Rd file 'stabilitySet.Rd' '...' Functions with \usage entries need to have the appropriate \alias entries, and all their arguments documented. The \usage entries must correspond to syntactically valid R code. See chapter 'Writing R documentation files' in the 'Writing R Extensions' manual. * 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 files in 'vignettes' ... OK * checking examples ... OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... Running 'testAll.R' Running 'testAnalysis.R' Running 'testCBI.R' Running 'testMetricsRelevancy.R' Running 'testQuality.R' Running 'testStability.R' OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 3 WARNINGs, 4 NOTEs See 'C:/Users/biocbuild/bbs-3.20-bioc/meat/evaluomeR.Rcheck/00check.log' for details.
evaluomeR.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### C:\Users\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD INSTALL evaluomeR ### ############################################################################## ############################################################################## * installing to library 'C:/Users/biocbuild/bbs-3.20-bioc/R/library' * installing *source* package 'evaluomeR' ... ** using staged installation ** R ** data *** moving datasets to lazyload DB ** inst ** byte-compile and prepare package for lazy loading ** help Loading required namespace: evaluomeR *** 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 (evaluomeR)
evaluomeR.Rcheck/tests/testAll.Rout
R version 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 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(evaluomeR) Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: 'MatrixGenerics' The following objects are masked from 'package:matrixStats': colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars Loading required package: GenomicRanges Loading required package: stats4 Loading required package: BiocGenerics Attaching package: 'BiocGenerics' The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply, union, unique, unsplit, which.max, which.min Loading required package: S4Vectors Attaching package: 'S4Vectors' The following object is masked from 'package:utils': findMatches The following objects are masked from 'package:base': I, expand.grid, unname Loading required package: IRanges Attaching package: 'IRanges' The following object is masked from 'package:grDevices': windows Loading required package: GenomeInfoDb Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Attaching package: 'Biobase' The following object is masked from 'package:MatrixGenerics': rowMedians The following objects are masked from 'package:matrixStats': anyMissing, rowMedians Loading required package: MultiAssayExperiment Loading required package: cluster Loading required package: fpc Loading required package: randomForest randomForest 4.7-1.1 Type rfNews() to see new features/changes/bug fixes. Attaching package: 'randomForest' The following object is masked from 'package:Biobase': combine The following object is masked from 'package:BiocGenerics': combine Loading required package: flexmix Loading required package: lattice Loading required package: RSKC Loading required package: flexclust Loading required package: grid Loading required package: modeltools Loading required package: sparcl > > data("rnaMetrics") > > dataFrame <- stability(data=rnaMetrics, k=4, bs=100, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 4 Processing metric: DegFact(2) Calculation of k = 4 > dataFrame <- stabilityRange(data=rnaMetrics, k.range=c(2,4), bs=20, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Calculation of k = 3 Calculation of k = 4 Processing metric: DegFact(2) Calculation of k = 2 Calculation of k = 3 Calculation of k = 4 > assay(dataFrame) Metric Mean_stability_k_2 Mean_stability_k_3 Mean_stability_k_4 [1,] "RIN" "0.825833333333333" "0.778412698412698" "0.69625" [2,] "DegFact" "0.955595238095238" "0.977777777777778" "0.820833333333333" > # Metric Mean_stability_k_2 Mean_stability_k_3 Mean_stability_k_4 > # [1,] "RIN" "0.825833333333333" "0.778412698412698" "0.69625" > # [2,] "DegFact" "0.955595238095238" "0.977777777777778" "0.820833333333333" > dataFrame <- stabilitySet(data=rnaMetrics, k.set=c(2,3,4), bs=20, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Calculation of k = 3 Calculation of k = 4 Processing metric: DegFact(2) Calculation of k = 2 Calculation of k = 3 Calculation of k = 4 > > dataFrame <- quality(data=rnaMetrics, cbi="kmeans", k=3, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 3 Processing metric: DegFact(2) Calculation of k = 3 > assay(dataFrame) Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore [1,] "RIN" "0.724044583696066" "0.68338517747747" "0.420502645502646" [2,] "DegFact" "0.876516605981734" "0.643613928123002" "0.521618857725795" Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size [1,] "0.627829396038413" "4" "8" "4" [2,] "0.737191191352892" "8" "5" "3" > # Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore > # [1,] "RIN" "0.420502645502646" "0.724044583696066" "0.68338517747747" > # [2,] "DegFact" "0.876516605981734" "0.643613928123002" "0.521618857725795" > # Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size > # [1,] "0.627829396038413" "4" "4" "8" > # [2,] "0.737191191352892" "8" "5" "3" > dataFrame <- qualityRange(data=rnaMetrics, k.range=c(2,4), seed = 20, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Calculation of k = 3 Calculation of k = 4 Processing metric: DegFact(2) Calculation of k = 2 Calculation of k = 3 Calculation of k = 4 > assay(getDataQualityRange(dataFrame, 2)) Metric Cluster_1_SilScore Cluster_2_SilScore Avg_Silhouette_Width 1 "RIN" "0.619872562681118" "0.583166775069983" "0.608402004052639" 2 "DegFact" "0.664573423022171" "0.675315791048653" "0.666587617027136" Cluster_1_Size Cluster_2_Size Cluster_position 1 "11" "5" "1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2" 2 "13" "3" "1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2" Cluster_labels 1 "" 2 "" > # Metric Cluster_1_SilScore Cluster_2_SilScore Avg_Silhouette_Width Cluster_1_Size > # 1 "RIN" "0.583166775069983" "0.619872562681118" "0.608402004052639" "5" > # 2 "DegFact" "0.664573423022171" "0.675315791048653" "0.666587617027136" "13" > # Cluster_2_Size > # 1 "11" > # 2 "3" > assay(getDataQualityRange(dataFrame, 4)) Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore 1 "RIN" "0.348714574898785" "0.420502645502646" "0.674226581940152" 2 "DegFact" "0.59496499852177" "0.521618857725795" "0.600198799385732" Cluster_4_SilScore Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size 1 "0.433333333333333" "0.463905611516569" "5" "4" 2 "0.759196481622952" "0.634170498361632" "3" "3" Cluster_3_Size Cluster_4_Size Cluster_position 1 "4" "3" "1,1,1,1,1,4,4,4,3,3,3,3,2,2,2,2" 2 "5" "5" "4,4,4,4,4,1,1,1,3,3,3,3,3,2,2,2" Cluster_labels 1 "" 2 "" > # Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore > # 1 "RIN" "0.420502645502646" "0.674226581940152" "0.433333333333333" > # 2 "DegFact" "0.759196481622952" "0.59496499852177" "0.600198799385732" > # Cluster_4_SilScore Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size > # 1 "0.348714574898785" "0.463905611516569" "4" "4" "3" > # 2 "0.521618857725795" "0.634170498361632" "5" "3" "5" > # Cluster_4_Size > # 1 "5" > # 2 "3" > dataFrame1 <- qualitySet(data=rnaMetrics, k.set=c(2,3,4), all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Calculation of k = 3 Calculation of k = 4 Processing metric: DegFact(2) Calculation of k = 2 Calculation of k = 3 Calculation of k = 4 > > > dataFrame <- metricsCorrelations(data=rnaMetrics, getImages = FALSE, margins = c(4,4,11,10)) Data loaded. Number of rows: 16 Number of columns: 3 > assay(dataFrame, 1) RIN DegFact RIN 1.0000000 -0.9744685 DegFact -0.9744685 1.0000000 > > > dataFrame <- stability(data=rnaMetrics, cbi="kmeans", k=2, bs=100, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Processing metric: DegFact(2) Calculation of k = 2 > dataFrame <- stability(data=rnaMetrics, cbi="clara", k=2, bs=100, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Processing metric: DegFact(2) Calculation of k = 2 > dataFrame <- stability(data=rnaMetrics, cbi="clara_pam", k=2, bs=100, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Processing metric: DegFact(2) Calculation of k = 2 > dataFrame <- stability(data=rnaMetrics, cbi="hclust", k=2, bs=100, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Processing metric: DegFact(2) Calculation of k = 2 > dataFrame <- stability(data=rnaMetrics, cbi="pamk", k=2, bs=100, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Processing metric: DegFact(2) Calculation of k = 2 > dataFrame <- stability(data=rnaMetrics, cbi="pamk_pam", k=2, bs=100, all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Processing metric: DegFact(2) Calculation of k = 2 > #dataFrame <- stability(data=rnaMetrics, cbi="rskc", k=2, bs=100, all_metrics = TRUE, L1 = 2, alpha=0, getImages = FALSE) > > # Supported CBIs: > evaluomeRSupportedCBI() [1] "kmeans" "clara" "clara_pam" "hclust" "pamk" "pamk_pam" [7] "rskc" > > dataFrame <- qualityRange(data=rnaMetrics, k.range=c(2,10), all_metrics = FALSE, getImages = FALSE) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Calculation of k = 6 Calculation of k = 7 Calculation of k = 8 Calculation of k = 9 Calculation of k = 10 Processing metric: DegFact(2) Calculation of k = 2 Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Calculation of k = 6 Calculation of k = 7 Calculation of k = 8 Calculation of k = 9 Calculation of k = 10 > dataFrame ExperimentList class object of length 9: [1] k_2: SummarizedExperiment with 2 rows and 8 columns [2] k_3: SummarizedExperiment with 2 rows and 10 columns [3] k_4: SummarizedExperiment with 2 rows and 12 columns [4] k_5: SummarizedExperiment with 2 rows and 14 columns [5] k_6: SummarizedExperiment with 2 rows and 16 columns [6] k_7: SummarizedExperiment with 2 rows and 18 columns [7] k_8: SummarizedExperiment with 2 rows and 20 columns [8] k_9: SummarizedExperiment with 2 rows and 22 columns [9] k_10: SummarizedExperiment with 2 rows and 24 columns > > #dataFrame <- stabilityRange(data=rnaMetrics, k.range=c(2,8), bs=20, getImages = FALSE) > #assay(dataFrame) > > > proc.time() user system elapsed 10.76 0.64 11.57
evaluomeR.Rcheck/tests/testAnalysis.Rout
R version 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 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(evaluomeR) Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: 'MatrixGenerics' The following objects are masked from 'package:matrixStats': colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars Loading required package: GenomicRanges Loading required package: stats4 Loading required package: BiocGenerics Attaching package: 'BiocGenerics' The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply, union, unique, unsplit, which.max, which.min Loading required package: S4Vectors Attaching package: 'S4Vectors' The following object is masked from 'package:utils': findMatches The following objects are masked from 'package:base': I, expand.grid, unname Loading required package: IRanges Attaching package: 'IRanges' The following object is masked from 'package:grDevices': windows Loading required package: GenomeInfoDb Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Attaching package: 'Biobase' The following object is masked from 'package:MatrixGenerics': rowMedians The following objects are masked from 'package:matrixStats': anyMissing, rowMedians Loading required package: MultiAssayExperiment Loading required package: cluster Loading required package: fpc Loading required package: randomForest randomForest 4.7-1.1 Type rfNews() to see new features/changes/bug fixes. Attaching package: 'randomForest' The following object is masked from 'package:Biobase': combine The following object is masked from 'package:BiocGenerics': combine Loading required package: flexmix Loading required package: lattice Loading required package: RSKC Loading required package: flexclust Loading required package: grid Loading required package: modeltools Loading required package: sparcl > > > data("rnaMetrics") > plotMetricsMinMax(rnaMetrics) There were 17 warnings (use warnings() to see them) > plotMetricsBoxplot(rnaMetrics) Warning messages: 1: Use of `data.melt$variable` is discouraged. ℹ Use `variable` instead. 2: Use of `data.melt$value` is discouraged. ℹ Use `value` instead. > cluster = plotMetricsCluster(ontMetrics, scale = TRUE) > plotMetricsViolin(rnaMetrics) Warning messages: 1: Use of `data.melt$variable` is discouraged. ℹ Use `variable` instead. 2: Use of `data.melt$value` is discouraged. ℹ Use `value` instead. 3: Use of `data.melt$variable` is discouraged. ℹ Use `variable` instead. 4: Use of `data.melt$value` is discouraged. ℹ Use `value` instead. > plotMetricsViolin(ontMetrics, 2) Warning messages: 1: Use of `data.melt$variable` is discouraged. ℹ Use `variable` instead. 2: Use of `data.melt$value` is discouraged. ℹ Use `value` instead. 3: Use of `data.melt$variable` is discouraged. ℹ Use `variable` instead. 4: Use of `data.melt$value` is discouraged. ℹ Use `value` instead. > > stabilityData <- stabilityRange(data=rnaMetrics, k.range=c(3,4), bs=20, getImages = FALSE, seed=100) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 3 Calculation of k = 4 Processing metric: DegFact(2) Calculation of k = 3 Calculation of k = 4 > qualityData <- qualityRange(data=rnaMetrics, k.range=c(3,4), getImages = FALSE, seed=100) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 3 Calculation of k = 4 Processing metric: DegFact(2) Calculation of k = 3 Calculation of k = 4 > > kOptTable <- getOptimalKValue(stabilityData, qualityData, k.range=c(3,4)) Processing metric: RIN Maximum stability and quality values matches the same K value: '3' Processing metric: DegFact Maximum stability and quality values matches the same K value: '3' > kOptTable Metric Stability_max_k Stability_max_k_stab Stability_max_k_qual 1 RIN 3 0.8901389 0.6278294 2 DegFact 3 1.0000000 0.7371912 Quality_max_k Quality_max_k_stab Quality_max_k_qual Global_optimal_k 1 3 0.8901389 0.6278294 3 2 3 1.0000000 0.7371912 3 > > > df = assay(rnaMetrics) > k.vector1=rep(5,length(colnames(df))-1) > k.vector2=rep(2,length(colnames(df))-1) > > plotMetricsClusterComparison(rnaMetrics, k.vector1=k.vector1, k.vector2=k.vector2) > plotMetricsClusterComparison(rnaMetrics, k.vector1=3, k.vector2=c(2,5)) > plotMetricsClusterComparison(rnaMetrics, k.vector1=3) > > x = as.data.frame(assay(rnaMetrics)) > > # Multi metric clustering > a = clusterbootWrapper(data=x[c("RIN", "DegFact")], B=100, + bootmethod="boot", + cbi="kmeans", + krange=2, seed=100) > a$bootmean # 0.8534346 for "RIN" [1] 0.8306667 0.9233683 > mean(a$bootmean) # 0.8534346 for "RIN" [1] 0.8770175 > stab = stability(data=x, k=2, bs=100, seed=100) Data loaded. Number of rows: 16 Number of columns: 3 Processing metric: RIN(1) Calculation of k = 2 Processing metric: DegFact(2) Calculation of k = 2 > assay(stab$stability_mean) # 0.8534346 for "RIN" Metric Mean_stability_k_2 [1,] "RIN" "0.853434523809524" [2,] "DegFact" "0.872830988455988" > > proc.time() user system elapsed 11.50 0.75 12.36
evaluomeR.Rcheck/tests/testCBI.Rout
R version 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 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(evaluomeR) Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: 'MatrixGenerics' The following objects are masked from 'package:matrixStats': colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars Loading required package: GenomicRanges Loading required package: stats4 Loading required package: BiocGenerics Attaching package: 'BiocGenerics' The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply, union, unique, unsplit, which.max, which.min Loading required package: S4Vectors Attaching package: 'S4Vectors' The following object is masked from 'package:utils': findMatches The following objects are masked from 'package:base': I, expand.grid, unname Loading required package: IRanges Attaching package: 'IRanges' The following object is masked from 'package:grDevices': windows Loading required package: GenomeInfoDb Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Attaching package: 'Biobase' The following object is masked from 'package:MatrixGenerics': rowMedians The following objects are masked from 'package:matrixStats': anyMissing, rowMedians Loading required package: MultiAssayExperiment Loading required package: cluster Loading required package: fpc Loading required package: randomForest randomForest 4.7-1.1 Type rfNews() to see new features/changes/bug fixes. Attaching package: 'randomForest' The following object is masked from 'package:Biobase': combine The following object is masked from 'package:BiocGenerics': combine Loading required package: flexmix Loading required package: lattice Loading required package: RSKC Loading required package: flexclust Loading required package: grid Loading required package: modeltools Loading required package: sparcl > > > evaluomeRSupportedCBI() [1] "kmeans" "clara" "clara_pam" "hclust" "pamk" "pamk_pam" [7] "rskc" > > > dataFrame <- stability(data=ontMetrics, cbi="kmeans", k=3, all_metrics=FALSE, bs=100) Data loaded. Number of rows: 80 Number of columns: 20 Processing metric: ANOnto(1) Calculation of k = 3 Processing metric: AROnto(2) Calculation of k = 3 Processing metric: CBOOnto(3) Calculation of k = 3 Processing metric: CBOOnto2(4) Calculation of k = 3 Processing metric: CROnto(5) Calculation of k = 3 Processing metric: DITOnto(6) Calculation of k = 3 Processing metric: INROnto(7) Calculation of k = 3 Processing metric: LCOMOnto(8) Calculation of k = 3 Processing metric: NACOnto(9) Calculation of k = 3 Processing metric: NOCOnto(10) Calculation of k = 3 Processing metric: NOMOnto(11) Calculation of k = 3 Processing metric: POnto(12) Calculation of k = 3 Processing metric: PROnto(13) Calculation of k = 3 Processing metric: RFCOnto(14) Calculation of k = 3 Processing metric: RROnto(15) Calculation of k = 3 Processing metric: TMOnto(16) Calculation of k = 3 Processing metric: TMOnto2(17) Calculation of k = 3 Processing metric: WMCOnto(18) Calculation of k = 3 Processing metric: WMCOnto2(19) Calculation of k = 3 > assay(dataFrame) Metric Mean_stability_k_3 [1,] "ANOnto" "0.711599421597794" [2,] "AROnto" "0.834242802235359" [3,] "CBOOnto" "0.836200447888132" [4,] "CBOOnto2" "0.836200447888132" [5,] "CROnto" "0.80871022609772" [6,] "DITOnto" "0.802620378293628" [7,] "INROnto" "0.813132039213596" [8,] "LCOMOnto" "0.995402775270891" [9,] "NACOnto" "0.705135779579475" [10,] "NOCOnto" "0.902528819875511" [11,] "NOMOnto" "0.793513639960901" [12,] "POnto" "0.660145923222329" [13,] "PROnto" "0.960518110441289" [14,] "RFCOnto" "0.765127486244089" [15,] "RROnto" "0.960518110441289" [16,] "TMOnto" "0.862955680341511" [17,] "TMOnto2" "0.953719590152899" [18,] "WMCOnto" "0.85715656831332" [19,] "WMCOnto2" "0.904134166028688" > > #dataFrame <- stabilityRange(data=ontMetrics, cbi="rskc", k.range=c(3,4), all_metrics=TRUE, bs=100, L1=2) > #assay(dataFrame) > > #dataFrame <- stabilitySet(data=ontMetrics, k.set=c(3,4), bs=100, cbi="rskc", all_metrics=TRUE, L1=2) > #assay(dataFrame) > > #dataFrame <- quality(data=ontMetrics, cbi="rskc", k=3, all_metrics=TRUE, L1=2) > #assay(dataFrame) > > #dataFrame <- qualityRange(data=ontMetrics, cbi="rskc", k.range=c(3,4), all_metrics=TRUE, L1=2) > #assay(dataFrame$k_3) > > #dataFrame <- qualitySet(data=ontMetrics, cbi="rskc", k.set=c(3,5), all_metrics=TRUE, L1=2) > #assay(dataFrame$k_3) > > > proc.time() user system elapsed 9.46 0.71 10.20
evaluomeR.Rcheck/tests/testMetricsRelevancy.Rout
R version 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 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(evaluomeR) Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: 'MatrixGenerics' The following objects are masked from 'package:matrixStats': colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars Loading required package: GenomicRanges Loading required package: stats4 Loading required package: BiocGenerics Attaching package: 'BiocGenerics' The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply, union, unique, unsplit, which.max, which.min Loading required package: S4Vectors Attaching package: 'S4Vectors' The following object is masked from 'package:utils': findMatches The following objects are masked from 'package:base': I, expand.grid, unname Loading required package: IRanges Attaching package: 'IRanges' The following object is masked from 'package:grDevices': windows Loading required package: GenomeInfoDb Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Attaching package: 'Biobase' The following object is masked from 'package:MatrixGenerics': rowMedians The following objects are masked from 'package:matrixStats': anyMissing, rowMedians Loading required package: MultiAssayExperiment Loading required package: cluster Loading required package: fpc Loading required package: randomForest randomForest 4.7-1.1 Type rfNews() to see new features/changes/bug fixes. Attaching package: 'randomForest' The following object is masked from 'package:Biobase': combine The following object is masked from 'package:BiocGenerics': combine Loading required package: flexmix Loading required package: lattice Loading required package: RSKC Loading required package: flexclust Loading required package: grid Loading required package: modeltools Loading required package: sparcl > > individuals_per_cluster = function(qualityResult) { + qual_df = as.data.frame(assay(qualityResult)) + + + cluster_pos_str = as.character(unlist(qual_df["Cluster_position"])) + cluster_labels_str = as.character(unlist(qual_df["Cluster_labels"])) + + cluster_pos = as.list(strsplit(cluster_pos_str, ",")[[1]]) + cluster_labels = as.list(strsplit(cluster_labels_str, ",")[[1]]) + + individuals_in_cluster = as.data.frame(cbind(cluster_labels, cluster_pos)) + colnames(individuals_in_cluster) = c("Individual", "InCluster") + + return(individuals_in_cluster) + } > > data("ontMetrics") > metricsRelevancy = getMetricsRelevancy(ontMetrics, k=3, alpha=0.1, seed=100) [1] "No L1 provided. Computing best L1 boundry with 'sparcl::KMeansSparseCluster.permute'" [1] "Alpha set as: 0.1" [1] "L1 set as: 2" > # RSKC output object > metricsRelevancy$rskc Input: #obs= 80 #feature= 20 L1= 2 alpha= 0.1 Result: wbss: 36493.8 trimmed cases: 5 13 26 37 41 68 73 75 2 21 59 67 71 #non-zero weights: 20 3 clusters of sizes 29, 28, 23 Cluster labels: 2 3 2 1 3 1 3 1 2 1 1 2 3 3 1 3 1 3 1 3 3 3 3 2 1 2 2 2 1 3 1 2 2 2 3 1 2 3 3 1 3 3 1 3 2 1 2 3 2 2 2 3 2 2 3 1 3 1 1 2 1 2 1 2 3 2 2 3 1 3 2 2 3 3 2 2 3 1 2 1 > # Trimmed cases from input (row indexes) > metricsRelevancy$trimmed_cases [1] 2 5 13 21 26 37 41 59 67 68 71 73 75 > # Metrics relevancy table > metricsRelevancy$relevancy metric weight 1 Description 9.999715e-01 19 WMCOnto 5.006773e-03 7 DITOnto 4.960088e-03 3 AROnto 2.628306e-03 15 RFCOnto 4.461140e-04 9 LCOMOnto 3.877396e-04 12 NOMOnto 3.426531e-04 11 NOCOnto 1.759248e-04 20 WMCOnto2 4.438819e-05 13 POnto 3.151949e-05 18 TMOnto2 1.370685e-05 14 PROnto 1.286771e-05 16 RROnto 1.286771e-05 2 ANOnto 1.009264e-05 4 CBOOnto 6.816740e-06 5 CBOOnto2 6.816740e-06 10 NACOnto 4.231373e-06 8 INROnto 2.598031e-06 17 TMOnto 1.619235e-06 6 CROnto 9.969769e-07 > > > test = qualityRange(data=ontMetrics, k.range=c(3,3), + seed=13007, + all_metrics=TRUE, + cbi="rskc", L1=2, alpha=0) Data loaded. Number of rows: 80 Number of columns: 20 Processing all metrics, 'merge', in dataframe (19) Calculation of k = 3 > > # Shows how clusters are partitioned according to the individuals > individuals_per_cluster(test$k_3) Individual InCluster 1 3 2 2 42 2 3 26 1 4 79 2 5 41 3 6 66 2 7 53 2 8 76 2 9 6 2 10 68 2 11 74 2 12 7 2 13 30 1 14 57 2 15 69 2 16 48 2 17 80 2 18 45 2 19 61 2 20 49 2 21 55 2 22 52 2 23 50 2 24 16 2 25 70 2 26 28 2 27 13 2 28 24 2 29 60 2 30 40 2 31 64 2 32 11 2 33 19 2 34 1 2 35 38 2 36 58 2 37 29 2 38 54 2 39 37 2 40 62 2 41 34 3 42 51 2 43 71 2 44 43 2 45 25 2 46 77 2 47 4 2 48 36 2 49 14 2 50 20 2 51 9 2 52 35 2 53 17 2 54 23 2 55 46 2 56 59 2 57 33 2 58 73 2 59 63 1 60 8 2 61 65 2 62 10 2 63 67 2 64 21 2 65 47 2 66 15 2 67 12 1 68 31 2 69 75 2 70 56 2 71 22 1 72 18 2 73 32 1 74 44 2 75 27 2 76 5 2 77 39 2 78 72 2 79 2 2 80 78 2 > > > proc.time() user system elapsed 9.56 0.81 10.43
evaluomeR.Rcheck/tests/testQuality.Rout
R version 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 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(evaluomeR) Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: 'MatrixGenerics' The following objects are masked from 'package:matrixStats': colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars Loading required package: GenomicRanges Loading required package: stats4 Loading required package: BiocGenerics Attaching package: 'BiocGenerics' The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply, union, unique, unsplit, which.max, which.min Loading required package: S4Vectors Attaching package: 'S4Vectors' The following object is masked from 'package:utils': findMatches The following objects are masked from 'package:base': I, expand.grid, unname Loading required package: IRanges Attaching package: 'IRanges' The following object is masked from 'package:grDevices': windows Loading required package: GenomeInfoDb Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Attaching package: 'Biobase' The following object is masked from 'package:MatrixGenerics': rowMedians The following objects are masked from 'package:matrixStats': anyMissing, rowMedians Loading required package: MultiAssayExperiment Loading required package: cluster Loading required package: fpc Loading required package: randomForest randomForest 4.7-1.1 Type rfNews() to see new features/changes/bug fixes. Attaching package: 'randomForest' The following object is masked from 'package:Biobase': combine The following object is masked from 'package:BiocGenerics': combine Loading required package: flexmix Loading required package: lattice Loading required package: RSKC Loading required package: flexclust Loading required package: grid Loading required package: modeltools Loading required package: sparcl > library(RSKC) > library(sparcl) > seed = 100 > dataFrame <- quality(data=ontMetrics, cbi="kmeans", k=3) Data loaded. Number of rows: 80 Number of columns: 20 Processing metric: ANOnto(1) Calculation of k = 3 Processing metric: AROnto(2) Calculation of k = 3 Processing metric: CBOOnto(3) Calculation of k = 3 Processing metric: CBOOnto2(4) Calculation of k = 3 Processing metric: CROnto(5) Calculation of k = 3 Processing metric: DITOnto(6) Calculation of k = 3 Processing metric: INROnto(7) Calculation of k = 3 Processing metric: LCOMOnto(8) Calculation of k = 3 Processing metric: NACOnto(9) Calculation of k = 3 Processing metric: NOCOnto(10) Calculation of k = 3 Processing metric: NOMOnto(11) Calculation of k = 3 Processing metric: POnto(12) Calculation of k = 3 Processing metric: PROnto(13) Calculation of k = 3 Processing metric: RFCOnto(14) Calculation of k = 3 Processing metric: RROnto(15) Calculation of k = 3 Processing metric: TMOnto(16) Calculation of k = 3 Processing metric: TMOnto2(17) Calculation of k = 3 Processing metric: WMCOnto(18) Calculation of k = 3 Processing metric: WMCOnto2(19) Calculation of k = 3 > assay(dataFrame) Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore [1,] "ANOnto" "0.754894925204277" "0.570241066303214" "0.775876285585267" [2,] "AROnto" "0.837074497995987" "0.509946991883709" "0.959264389073384" [3,] "CBOOnto" "0.470708665744913" "0.766630500367533" "0.574451527320666" [4,] "CBOOnto2" "0.470708665744913" "0.766630500367533" "0.574451527320666" [5,] "CROnto" "0" "0.636126752920544" "0.885055456924709" [6,] "DITOnto" "0.615581638093901" "0.441137593941046" "0.746848044839846" [7,] "INROnto" "0" "0.760945813444805" "0.506239463726949" [8,] "LCOMOnto" "0.657281417643165" "0.61764525421598" "0.722333227599342" [9,] "NACOnto" "0.445845264823784" "0.759522276872854" "0.254826579985626" [10,] "NOCOnto" "0.363472944618239" "0.898396530127955" "0.742673517080307" [11,] "NOMOnto" "0.708789049998754" "0" "0.605603643727872" [12,] "POnto" "0.755700546488043" "0.737169134813343" "0.651090644844594" [13,] "PROnto" "0.770018889790615" "0.636058646833202" "0.56606585120985" [14,] "RFCOnto" "0.672903800663584" "0" "0.571360647044581" [15,] "RROnto" "0.770018889790615" "0.636058646833202" "0.56606585120985" [16,] "TMOnto" "0.50860642260504" "0.782948726523096" "0.634534477835837" [17,] "TMOnto2" "0.73737171744016" "1" "0.462679160671249" [18,] "WMCOnto" "0.868556472442156" "0.369670756071292" "0.763547528087877" [19,] "WMCOnto2" "0.891854974826074" "0.598522433823083" "0.613618761016468" Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size [1,] "0.736742918153759" "12" "14" "54" [2,] "0.786971025529677" "65" "13" "2" [3,] "0.72319889705568" "2" "63" "15" [4,] "0.72319889705568" "2" "63" "15" [5,] "0.855322610912838" "1" "6" "73" [6,] "0.553468450386794" "41" "33" "6" [7,] "0.690941232718754" "1" "60" "19" [8,] "0.652913140794165" "21" "40" "19" [9,] "0.661322430756974" "17" "58" "5" [10,] "0.879183827500925" "2" "75" "3" [11,] "0.668973564992505" "55" "1" "24" [12,] "0.67661537075347" "8" "14" "58" [13,] "0.668644905329162" "32" "24" "24" [14,] "0.635298846489826" "56" "1" "23" [15,] "0.668644905329162" "32" "24" "24" [16,] "0.710090639489989" "18" "56" "6" [17,] "0.724657891719511" "45" "16" "19" [18,] "0.828514820105485" "72" "6" "2" [19,] "0.870232442430684" "74" "4" "2" > # Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size > # [1,] "ANOnto" "0.754894925204277" "0.570241066303214" "0.775876285585267" "0.736742918153759" "12" "14" "54" > # [2,] "AROnto" "0.837074497995987" "0.509946991883709" "0.959264389073384" "0.786971025529677" "65" "13" "2" > # [3,] "CBOOnto" "0.766630500367533" "0.574451527320666" "0.470708665744913" "0.72319889705568" "63" "15" "2" > # [4,] "CBOOnto2" "0.766630500367533" "0.574451527320666" "0.470708665744913" "0.72319889705568" "63" "15" "2" > # [5,] "CROnto" "0.885055456924709" "0.636126752920544" "0" "0.855322610912838" "73" "6" "1" > # [6,] "DITOnto" "0.615581638093901" "0.441137593941046" "0.746848044839846" "0.553468450386794" "41" "33" "6" > # [7,] "INROnto" "0.760945813444805" "0.506239463726949" "0" "0.690941232718754" "60" "19" "1" > # [8,] "LCOMOnto" "0.657281417643165" "0.61764525421598" "0.722333227599342" "0.652913140794165" "21" "40" "19" > # [9,] "NACOnto" "0.759522276872854" "0.445845264823784" "0.254826579985626" "0.661322430756974" "58" "17" "5" > # [10,] "NOCOnto" "0.898396530127955" "0.742673517080307" "0.363472944618239" "0.879183827500925" "75" "3" "2" > # [11,] "NOMOnto" "0.708789049998754" "0.605603643727872" "0" "0.668973564992505" "55" "24" "1" > # [12,] "POnto" "0.755700546488043" "0.737169134813343" "0.651090644844594" "0.67661537075347" "8" "14" "58" > # [13,] "PROnto" "0.770018889790615" "0.56606585120985" "0.636058646833202" "0.668644905329162" "32" "24" "24" > # [14,] "RFCOnto" "0.672903800663584" "0.571360647044581" "0" "0.635298846489826" "56" "23" "1" > # [15,] "RROnto" "0.636058646833202" "0.56606585120985" "0.770018889790615" "0.668644905329162" "24" "24" "32" > # [16,] "TMOnto" "0.782948726523096" "0.50860642260504" "0.634534477835837" "0.710090639489989" "56" "18" "6" > # [17,] "TMOnto2" "1" "0.73737171744016" "0.462679160671249" "0.724657891719511" "16" "45" "19" > # [18,] "WMCOnto" "0.868556472442156" "0.369670756071292" "0.763547528087877" "0.828514820105485" "72" "6" "2" > # [19,] "WMCOnto2" "0.891854974826074" "0.598522433823083" "0.613618761016468" "0.870232442430684" "74" "4" "2" > > dataFrame <- quality(data=ontMetrics, cbi="kmeans", k=4) Data loaded. Number of rows: 80 Number of columns: 20 Processing metric: ANOnto(1) Calculation of k = 4 Processing metric: AROnto(2) Calculation of k = 4 Processing metric: CBOOnto(3) Calculation of k = 4 Processing metric: CBOOnto2(4) Calculation of k = 4 Processing metric: CROnto(5) Calculation of k = 4 Processing metric: DITOnto(6) Calculation of k = 4 Processing metric: INROnto(7) Calculation of k = 4 Processing metric: LCOMOnto(8) Calculation of k = 4 Processing metric: NACOnto(9) Calculation of k = 4 Processing metric: NOCOnto(10) Calculation of k = 4 Processing metric: NOMOnto(11) Calculation of k = 4 Processing metric: POnto(12) Calculation of k = 4 Processing metric: PROnto(13) Calculation of k = 4 Processing metric: RFCOnto(14) Calculation of k = 4 Processing metric: RROnto(15) Calculation of k = 4 Processing metric: TMOnto(16) Calculation of k = 4 Processing metric: TMOnto2(17) Calculation of k = 4 Processing metric: WMCOnto(18) Calculation of k = 4 Processing metric: WMCOnto2(19) Calculation of k = 4 > assay(dataFrame) Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore [1,] "ANOnto" "0.569222510427433" "0.552363239306396" "0.584449669565973" [2,] "AROnto" "0.891757427020894" "0.498602630835942" "0.953766280221553" [3,] "CBOOnto" "0.682847685112873" "0.475694878561971" "0.418096612044278" [4,] "CBOOnto2" "0.682847685112873" "0.475694878561971" "0.418096612044278" [5,] "CROnto" "0.615016966742524" "0.931552645421743" "0.460688748724164" [6,] "DITOnto" "0.621392145232729" "0.589638237470761" "0.512852920317478" [7,] "INROnto" "0.679354776901229" "0.514845315378322" "0.552323396139528" [8,] "LCOMOnto" "0.563584714383498" "0.565734453969461" "0.526937877760086" [9,] "NACOnto" "0.507554700154524" "0.763008703189753" "0.0693863149967116" [10,] "NOCOnto" "0.363472944618239" "0.712806750183687" "0.368068489789737" [11,] "NOMOnto" "0.796568957921031" "0" "0.487448631370323" [12,] "POnto" "0.717551583859045" "0.702605079149018" "0.531828315626997" [13,] "PROnto" "0.808419016380534" "0.636912857924547" "0.406920889282586" [14,] "RFCOnto" "0.708660103503223" "0" "0.527891770926241" [15,] "RROnto" "0.808419016380534" "0.636912857924547" "0.406920889282586" [16,] "TMOnto" "0.527581279093128" "0.772548576303018" "0.756878515673905" [17,] "TMOnto2" "0.593309463294573" "1" "0.709314170957853" [18,] "WMCOnto" "0.811550829534933" "0.517887706724764" "0.751527957476758" [19,] "WMCOnto2" "0.48724511207104" "0.806794961402285" "0.613618761016468" Cluster_4_SilScore Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size [1,] "0.717030499002753" "0.600638738086962" "11" "4" [2,] "0.614385150712436" "0.813833608784603" "58" "7" [3,] "0.462053414220223" "0.5843870090796" "46" "18" [4,] "0.462053414220223" "0.5843870090796" "46" "18" [5,] "0" "0.84502648526675" "10" "63" [6,] "0.717462336796908" "0.582143307479606" "15" "35" [7,] "0" "0.609561353444975" "46" "19" [8,] "0.662861247621334" "0.57713748864992" "19" "19" [9,] "0.610806402578204" "0.627188990478616" "23" "42" [10,] "0.711626648649838" "0.600607673118847" "2" "51" [11,] "0.505810544669573" "0.620956620752701" "35" "1" [12,] "0.755700546488043" "0.676374911502771" "14" "42" [13,] "0.546429726628472" "0.623564355956028" "22" "23" [14,] "0.575667190561062" "0.613856368788046" "37" "1" [15,] "0.546429726628472" "0.623564355956028" "22" "23" [16,] "0.56435245544769" "0.694408411158545" "15" "48" [17,] "0.516092763511662" "0.725408613137789" "19" "16" [18,] "0.232935788267106" "0.737070037248562" "62" "12" [19,] "0.458575230569131" "0.72940235766569" "4" "61" Cluster_3_Size Cluster_4_Size [1,] "53" "12" [2,] "2" "13" [3,] "14" "2" [4,] "14" "2" [5,] "6" "1" [6,] "24" "6" [7,] "14" "1" [8,] "23" "19" [9,] "5" "10" [10,] "24" "3" [11,] "25" "19" [12,] "16" "8" [13,] "12" "23" [14,] "27" "15" [15,] "12" "23" [16,] "5" "12" [17,] "39" "6" [18,] "2" "4" [19,] "2" "13" > # Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore Cluster_4_SilScore Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size Cluster_4_Size > # [1,] "ANOnto" "0.717030499002753" "0.569222510427433" "0.552363239306396" "0.584449669565973" "0.600638738086962" "12" "11" "4" "53" > # [2,] "AROnto" "0.891757427020894" "0.614385150712436" "0.498602630835942" "0.953766280221553" "0.813833608784603" "58" "13" "7" "2" > # [3,] "CBOOnto" "0.682847685112873" "0.475694878561971" "0.418096612044278" "0.462053414220223" "0.5843870090796" "46" "18" "14" "2" > # [4,] "CBOOnto2" "0.682847685112873" "0.475694878561971" "0.418096612044278" "0.462053414220223" "0.5843870090796" "46" "18" "14" "2" > # [5,] "CROnto" "0.931552645421743" "0.615016966742524" "0.460688748724164" "0" "0.84502648526675" "63" "10" "6" "1" > # [6,] "DITOnto" "0.621392145232729" "0.589638237470761" "0.512852920317478" "0.717462336796908" "0.582143307479606" "15" "35" "24" "6" > # [7,] "INROnto" "0.679354776901229" "0.514845315378322" "0.552323396139528" "0" "0.609561353444975" "46" "19" "14" "1" > # [8,] "LCOMOnto" "0.563584714383498" "0.565734453969461" "0.526937877760086" "0.662861247621334" "0.57713748864992" "19" "19" "23" "19" > # [9,] "NACOnto" "0.763008703189753" "0.507554700154524" "0.610806402578204" "0.0693863149967116" "0.627188990478616" "42" "23" "10" "5" > # [10,] "NOCOnto" "0.712806750183687" "0.368068489789737" "0.711626648649838" "0.363472944618239" "0.600607673118847" "51" "24" "3" "2" > # [11,] "NOMOnto" "0.796568957921031" "0.487448631370323" "0.505810544669573" "0" "0.620956620752701" "35" "25" "19" "1" > # [12,] "POnto" "0.755700546488043" "0.717551583859045" "0.702605079149018" "0.531828315626997" "0.676374911502771" "8" "14" "42" "16" > # [13,] "PROnto" "0.808419016380534" "0.406920889282586" "0.546429726628472" "0.636912857924547" "0.623564355956028" "22" "12" "23" "23" > # [14,] "RFCOnto" "0.708660103503223" "0.527891770926241" "0.575667190561062" "0" "0.613856368788046" "37" "27" "15" "1" > # [15,] "RROnto" "0.636912857924547" "0.546429726628472" "0.406920889282586" "0.808419016380534" "0.623564355956028" "23" "23" "12" "22" > # [16,] "TMOnto" "0.772548576303018" "0.527581279093128" "0.56435245544769" "0.756878515673905" "0.694408411158545" "48" "15" "12" "5" > # [17,] "TMOnto2" "1" "0.709314170957853" "0.593309463294573" "0.516092763511662" "0.725408613137789" "16" "39" "19" "6" > # [18,] "WMCOnto" "0.811550829534933" "0.517887706724764" "0.232935788267106" "0.751527957476758" "0.737070037248562" "62" "12" "4" "2" > # [19,] "WMCOnto2" "0.806794961402285" "0.458575230569131" "0.48724511207104" "0.613618761016468" "0.72940235766569" "61" "13" "4" "2" > > dataFrame <- qualityRange(data=ontMetrics, cbi="kmeans", k.range = c(3,4)) Data loaded. Number of rows: 80 Number of columns: 20 Processing metric: ANOnto(1) Calculation of k = 3 Calculation of k = 4 Processing metric: AROnto(2) Calculation of k = 3 Calculation of k = 4 Processing metric: CBOOnto(3) Calculation of k = 3 Calculation of k = 4 Processing metric: CBOOnto2(4) Calculation of k = 3 Calculation of k = 4 Processing metric: CROnto(5) Calculation of k = 3 Calculation of k = 4 Processing metric: DITOnto(6) Calculation of k = 3 Calculation of k = 4 Processing metric: INROnto(7) Calculation of k = 3 Calculation of k = 4 Processing metric: LCOMOnto(8) Calculation of k = 3 Calculation of k = 4 Processing metric: NACOnto(9) Calculation of k = 3 Calculation of k = 4 Processing metric: NOCOnto(10) Calculation of k = 3 Calculation of k = 4 Processing metric: NOMOnto(11) Calculation of k = 3 Calculation of k = 4 Processing metric: POnto(12) Calculation of k = 3 Calculation of k = 4 Processing metric: PROnto(13) Calculation of k = 3 Calculation of k = 4 Processing metric: RFCOnto(14) Calculation of k = 3 Calculation of k = 4 Processing metric: RROnto(15) Calculation of k = 3 Calculation of k = 4 Processing metric: TMOnto(16) Calculation of k = 3 Calculation of k = 4 Processing metric: TMOnto2(17) Calculation of k = 3 Calculation of k = 4 Processing metric: WMCOnto(18) Calculation of k = 3 Calculation of k = 4 Processing metric: WMCOnto2(19) Calculation of k = 3 Calculation of k = 4 > assay(dataFrame$k_4) Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore 1 "ANOnto" "0.569222510427433" "0.552363239306396" "0.584449669565973" 2 "AROnto" "0.891757427020894" "0.498602630835942" "0.953766280221553" 3 "CBOOnto" "0.682847685112873" "0.475694878561971" "0.418096612044278" 4 "CBOOnto2" "0.682847685112873" "0.475694878561971" "0.418096612044278" 5 "CROnto" "0.615016966742524" "0.931552645421743" "0.460688748724164" 6 "DITOnto" "0.621392145232729" "0.589638237470761" "0.512852920317478" 7 "INROnto" "0.679354776901229" "0.514845315378322" "0.552323396139528" 8 "LCOMOnto" "0.563584714383498" "0.565734453969461" "0.526937877760086" 9 "NACOnto" "0.507554700154524" "0.763008703189753" "0.0693863149967116" 10 "NOCOnto" "0.363472944618239" "0.712806750183687" "0.368068489789737" 11 "NOMOnto" "0.796568957921031" "0" "0.487448631370323" 12 "POnto" "0.717551583859045" "0.702605079149018" "0.531828315626997" 13 "PROnto" "0.808419016380534" "0.636912857924547" "0.406920889282586" 14 "RFCOnto" "0.708660103503223" "0" "0.527891770926241" 15 "RROnto" "0.808419016380534" "0.636912857924547" "0.406920889282586" 16 "TMOnto" "0.527581279093128" "0.772548576303018" "0.756878515673905" 17 "TMOnto2" "0.593309463294573" "1" "0.709314170957853" 18 "WMCOnto" "0.811550829534933" "0.517887706724764" "0.751527957476758" 19 "WMCOnto2" "0.48724511207104" "0.806794961402285" "0.613618761016468" Cluster_4_SilScore Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size 1 "0.717030499002753" "0.600638738086962" "11" "4" 2 "0.614385150712436" "0.813833608784603" "58" "7" 3 "0.462053414220223" "0.5843870090796" "46" "18" 4 "0.462053414220223" "0.5843870090796" "46" "18" 5 "0" "0.84502648526675" "10" "63" 6 "0.717462336796908" "0.582143307479606" "15" "35" 7 "0" "0.609561353444975" "46" "19" 8 "0.662861247621334" "0.57713748864992" "19" "19" 9 "0.610806402578204" "0.627188990478616" "23" "42" 10 "0.711626648649838" "0.600607673118847" "2" "51" 11 "0.505810544669573" "0.620956620752701" "35" "1" 12 "0.755700546488043" "0.676374911502771" "14" "42" 13 "0.546429726628472" "0.623564355956028" "22" "23" 14 "0.575667190561062" "0.613856368788046" "37" "1" 15 "0.546429726628472" "0.623564355956028" "22" "23" 16 "0.56435245544769" "0.694408411158545" "15" "48" 17 "0.516092763511662" "0.725408613137789" "19" "16" 18 "0.232935788267106" "0.737070037248562" "62" "12" 19 "0.458575230569131" "0.72940235766569" "4" "61" Cluster_3_Size Cluster_4_Size 1 "53" "12" 2 "2" "13" 3 "14" "2" 4 "14" "2" 5 "6" "1" 6 "24" "6" 7 "14" "1" 8 "23" "19" 9 "5" "10" 10 "24" "3" 11 "25" "19" 12 "16" "8" 13 "12" "23" 14 "27" "15" 15 "12" "23" 16 "5" "12" 17 "39" "6" 18 "2" "4" 19 "2" "13" Cluster_position 1 "1,3,1,3,1,3,4,3,3,3,3,3,3,3,3,3,3,3,4,4,3,3,1,1,3,3,3,3,1,3,3,3,2,3,3,2,3,3,2,3,3,4,3,3,2,3,3,3,4,3,4,4,3,4,3,3,4,1,1,3,3,3,1,4,4,4,1,3,3,3,3,3,3,3,3,3,3,1,3,3" 2 "1,1,1,1,1,2,1,4,1,2,4,1,1,1,2,1,1,4,1,1,1,1,1,1,4,1,1,1,1,1,4,1,1,1,1,1,4,1,1,4,4,1,1,4,1,3,1,1,1,1,1,1,1,1,2,1,1,1,1,4,2,4,1,1,1,1,1,1,2,1,4,1,1,4,1,2,1,1,3,1" 3 "1,1,3,1,3,1,2,2,1,3,2,2,2,2,1,1,1,1,4,1,1,1,1,2,3,1,2,2,1,2,1,1,1,1,1,1,3,2,1,2,3,1,2,1,1,1,1,1,1,3,2,1,1,3,1,2,1,1,3,1,1,1,1,1,3,3,3,1,4,1,3,2,3,1,1,2,1,1,2,1" 4 "1,1,3,1,3,1,2,2,1,3,2,2,2,2,1,1,1,1,4,1,1,1,1,2,3,1,2,2,1,2,1,1,1,1,1,1,3,2,1,2,3,1,2,1,1,1,1,1,1,3,2,1,1,3,1,2,1,1,3,1,1,1,1,1,3,3,3,1,4,1,3,2,3,1,1,2,1,1,2,1" 5 "2,2,2,2,2,3,1,2,2,1,1,2,2,2,2,2,2,1,2,2,2,2,2,2,1,2,2,2,2,2,1,2,2,2,2,2,2,2,1,2,2,2,2,3,2,3,2,2,2,2,2,2,2,2,2,2,2,2,2,1,3,2,2,2,2,2,2,2,2,2,3,2,2,1,2,1,4,2,3,2" 6 "2,1,4,2,4,3,1,2,1,3,2,1,3,2,3,2,2,2,3,2,1,2,2,3,3,2,3,2,2,1,2,1,3,2,1,1,3,2,2,3,3,1,2,2,2,4,2,3,1,3,2,3,1,4,2,2,3,2,3,1,3,2,1,2,1,2,4,2,3,3,3,2,3,3,2,3,2,2,4,2" 7 "1,1,3,1,3,1,2,2,1,3,2,3,2,2,1,1,1,1,4,1,1,1,1,2,1,1,2,2,2,2,1,1,1,1,1,1,3,2,1,2,3,1,2,1,1,1,1,1,1,3,2,1,1,3,1,2,1,1,3,1,1,1,1,1,3,2,3,1,3,1,3,2,3,1,1,2,1,1,2,1" 8 "1,1,4,2,4,3,1,1,2,4,3,2,4,2,4,1,3,1,4,3,1,1,3,3,3,2,3,3,1,2,3,1,3,2,1,1,4,2,2,4,4,1,3,2,1,3,3,3,2,3,2,4,1,4,3,3,4,2,4,1,3,3,2,2,1,3,4,1,4,4,3,2,4,3,1,4,2,2,4,2" 9 "2,2,4,2,4,2,1,1,2,3,4,1,1,1,2,2,2,2,3,1,2,1,2,1,2,2,1,1,1,1,2,2,2,2,2,2,4,1,2,1,4,2,1,2,2,2,2,2,2,4,1,2,2,4,2,1,2,2,3,1,2,2,2,2,4,1,4,2,3,2,3,1,4,2,1,1,2,2,1,2" 10 "3,1,2,3,2,3,2,3,2,2,2,2,2,3,2,3,3,3,4,2,1,3,2,2,2,3,2,4,3,2,2,2,2,2,4,2,3,2,2,2,2,2,2,2,3,2,3,2,2,2,2,3,3,2,2,3,3,2,2,2,3,2,2,3,3,2,2,3,2,3,2,2,2,2,2,2,3,2,2,2" 11 "1,4,1,4,1,3,1,1,1,3,3,4,3,1,4,3,4,3,1,1,4,1,1,3,4,1,1,4,1,1,3,4,3,1,1,3,4,1,3,3,4,1,1,3,4,2,3,3,1,4,1,1,3,1,3,3,1,3,4,4,3,1,1,1,1,1,3,4,4,1,3,1,1,3,1,3,4,3,4,1" 12 "1,2,3,2,1,2,4,2,2,3,3,3,2,2,2,1,2,2,4,4,2,2,1,1,2,2,3,2,1,2,2,2,1,2,2,1,3,2,1,2,3,4,2,2,1,2,2,2,2,3,4,4,2,3,2,3,4,1,3,2,2,2,1,2,4,3,1,2,3,2,3,2,3,2,2,3,2,1,2,2" 13 "1,2,3,2,3,4,3,3,1,4,4,2,4,1,2,4,2,4,1,1,2,1,3,4,2,3,1,2,1,1,4,2,4,3,1,4,2,1,4,4,2,1,3,2,2,2,4,2,1,2,3,1,2,1,2,4,1,4,4,2,4,3,1,1,1,3,4,2,2,1,4,1,1,4,3,4,2,4,2,4" 14 "1,3,1,4,3,1,1,1,1,3,3,4,3,1,4,1,4,1,3,1,3,1,1,3,4,1,1,4,1,1,3,4,3,1,1,3,4,1,3,3,4,1,1,3,3,2,3,3,1,4,1,1,3,1,3,3,1,1,4,4,3,1,1,1,1,3,3,3,4,1,4,1,1,3,1,3,3,1,4,1" 15 "1,2,3,2,3,4,3,3,1,4,4,2,4,1,2,4,2,4,1,1,2,1,3,4,2,3,1,2,1,1,4,2,4,3,1,4,2,1,4,4,2,1,3,2,2,2,4,2,1,2,3,1,2,1,2,4,1,4,4,2,4,3,1,1,1,3,4,2,2,1,4,1,1,4,3,4,2,4,2,4" 16 "2,2,4,2,1,2,2,1,2,3,4,4,4,1,2,2,2,2,2,2,2,1,2,2,2,2,4,1,2,1,2,2,2,1,2,2,4,1,2,1,3,2,4,2,2,2,2,2,2,4,2,2,2,4,2,4,2,2,3,2,2,2,2,2,2,1,1,1,3,2,3,1,4,2,1,4,2,2,1,2" 17 "3,3,4,3,1,3,3,3,2,1,3,4,1,3,3,3,3,3,2,2,2,1,3,2,1,3,3,3,4,3,3,2,2,3,1,2,1,3,2,4,1,2,3,2,3,3,3,3,2,1,1,4,2,1,1,3,1,2,1,3,3,3,2,2,3,4,1,3,3,3,1,3,1,3,3,1,3,3,1,3" 18 "1,1,2,1,3,1,1,1,1,2,1,1,2,1,1,1,1,1,2,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,2,1,1,1,3,1,2,1,1,1,1,1,1,1,1,2,1,2,1,1,2,1,4,1,1,1,1,1,1,1,4,1,2,1,4,1,4,1,1,2,1,1,1,1" 19 "2,2,4,2,3,2,2,2,2,2,2,2,4,2,2,2,2,2,4,2,2,2,2,2,2,2,4,2,2,2,2,2,2,2,2,2,4,2,2,2,3,2,4,2,2,2,2,2,2,4,2,2,2,4,2,2,4,2,1,2,2,2,2,2,4,4,1,2,4,2,1,2,1,2,2,4,2,2,2,2" Cluster_labels 1 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 2 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 3 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 4 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 5 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 6 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 7 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 8 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 9 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 10 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 11 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 12 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 13 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 14 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 15 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 16 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 17 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 18 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" 19 "3,42,26,79,41,66,53,76,6,68,74,7,30,57,69,48,80,45,61,49,55,52,50,16,70,28,13,24,60,40,64,11,19,1,38,58,29,54,37,62,34,51,71,43,25,77,4,36,14,20,9,35,17,23,46,59,33,73,63,8,65,10,67,21,47,15,12,31,75,56,22,18,32,44,27,5,39,72,2,78" > # Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore Cluster_4_SilScore Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size Cluster_4_Size > # 1 "ANOnto" "0.569222510427433" "0.552363239306396" "0.584449669565973" "0.717030499002753" "0.600638738086962" "11" "4" "53" "12" > # 2 "AROnto" "0.891757427020894" "0.498602630835942" "0.953766280221553" "0.614385150712436" "0.813833608784603" "58" "7" "2" "13" > # 3 "CBOOnto" "0.682847685112873" "0.475694878561971" "0.418096612044278" "0.462053414220223" "0.5843870090796" "46" "18" "14" "2" > # 4 "CBOOnto2" "0.682847685112873" "0.475694878561971" "0.418096612044278" "0.462053414220223" "0.5843870090796" "46" "18" "14" "2" > # 5 "CROnto" "0.615016966742524" "0.931552645421743" "0.460688748724164" "0" "0.84502648526675" "10" "63" "6" "1" > # 6 "DITOnto" "0.621392145232729" "0.589638237470761" "0.512852920317478" "0.717462336796908" "0.582143307479606" "15" "35" "24" "6" > # 7 "INROnto" "0.679354776901229" "0.514845315378322" "0.552323396139528" "0" "0.609561353444975" "46" "19" "14" "1" > # 8 "LCOMOnto" "0.563584714383498" "0.565734453969461" "0.526937877760086" "0.662861247621334" "0.57713748864992" "19" "19" "23" "19" > # 9 "NACOnto" "0.507554700154524" "0.763008703189753" "0.0693863149967116" "0.610806402578204" "0.627188990478616" "23" "42" "5" "10" > # 10 "NOCOnto" "0.363472944618239" "0.712806750183687" "0.368068489789737" "0.711626648649838" "0.600607673118847" "2" "51" "24" "3" > # 11 "NOMOnto" "0.796568957921031" "0" "0.487448631370323" "0.505810544669573" "0.620956620752701" "35" "1" "25" "19" > # 12 "POnto" "0.717551583859045" "0.702605079149018" "0.531828315626997" "0.755700546488043" "0.676374911502771" "14" "42" "16" "8" > # 13 "PROnto" "0.808419016380534" "0.636912857924547" "0.406920889282586" "0.546429726628472" "0.623564355956028" "22" "23" "12" "23" > # 14 "RFCOnto" "0.708660103503223" "0" "0.527891770926241" "0.575667190561062" "0.613856368788046" "37" "1" "27" "15" > # 15 "RROnto" "0.808419016380534" "0.636912857924547" "0.406920889282586" "0.546429726628472" "0.623564355956028" "22" "23" "12" "23" > # 16 "TMOnto" "0.527581279093128" "0.772548576303018" "0.756878515673905" "0.56435245544769" "0.694408411158545" "15" "48" "5" "12" > # 17 "TMOnto2" "0.593309463294573" "1" "0.709314170957853" "0.516092763511662" "0.725408613137789" "19" "16" "39" "6" > # 18 "WMCOnto" "0.811550829534933" "0.517887706724764" "0.751527957476758" "0.232935788267106" "0.737070037248562" "62" "12" "2" "4" > # 19 "WMCOnto2" "0.48724511207104" "0.806794961402285" "0.613618761016468" "0.458575230569131" "0.72940235766569" "4" "61" "2" "13" > > #dataFrame <- qualityRange(data=ontMetrics, cbi="kmeans", k.range = c(3,4), all_metrics=TRUE, getImages = TRUE) > #assay(dataFrame$k_3) > # Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore Cluster_4_SilScore Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size Cluster_4_Size > # 1 "all_metrics" "0.560364615463509" "0.768006541644696" "0.761635263968552" "0.343459043619883" "0.730815149196402" "2" "70" "2" "6" > > #dataFrame <- quality(data=ontMetrics, cbi="kmeans", k=4, all_metrics=TRUE) > #assay(dataFrame) > # Metric Cluster_1_SilScore Cluster_2_SilScore Cluster_3_SilScore Cluster_4_SilScore Avg_Silhouette_Width > # [1,] "all_metrics" "0.560364615463509" "0.768006541644696" "0.761635263968552" "0.343459043619883" "0.730815149196402" > # Cluster_1_Size Cluster_2_Size Cluster_3_Size Cluster_4_Size > # [1,] "2" "70" "2" "6" > > proc.time() user system elapsed 8.34 0.67 9.07
evaluomeR.Rcheck/tests/testStability.Rout
R version 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 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(evaluomeR) Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: 'MatrixGenerics' The following objects are masked from 'package:matrixStats': colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars Loading required package: GenomicRanges Loading required package: stats4 Loading required package: BiocGenerics Attaching package: 'BiocGenerics' The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply, union, unique, unsplit, which.max, which.min Loading required package: S4Vectors Attaching package: 'S4Vectors' The following object is masked from 'package:utils': findMatches The following objects are masked from 'package:base': I, expand.grid, unname Loading required package: IRanges Attaching package: 'IRanges' The following object is masked from 'package:grDevices': windows Loading required package: GenomeInfoDb Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Attaching package: 'Biobase' The following object is masked from 'package:MatrixGenerics': rowMedians The following objects are masked from 'package:matrixStats': anyMissing, rowMedians Loading required package: MultiAssayExperiment Loading required package: cluster Loading required package: fpc Loading required package: randomForest randomForest 4.7-1.1 Type rfNews() to see new features/changes/bug fixes. Attaching package: 'randomForest' The following object is masked from 'package:Biobase': combine The following object is masked from 'package:BiocGenerics': combine Loading required package: flexmix Loading required package: lattice Loading required package: RSKC Loading required package: flexclust Loading required package: grid Loading required package: modeltools Loading required package: sparcl > library(RSKC) > library(sparcl) > > dataFrame <- stability(data=ontMetrics, cbi="kmeans", k=3, bs=100) Data loaded. Number of rows: 80 Number of columns: 20 Processing metric: ANOnto(1) Calculation of k = 3 Processing metric: AROnto(2) Calculation of k = 3 Processing metric: CBOOnto(3) Calculation of k = 3 Processing metric: CBOOnto2(4) Calculation of k = 3 Processing metric: CROnto(5) Calculation of k = 3 Processing metric: DITOnto(6) Calculation of k = 3 Processing metric: INROnto(7) Calculation of k = 3 Processing metric: LCOMOnto(8) Calculation of k = 3 Processing metric: NACOnto(9) Calculation of k = 3 Processing metric: NOCOnto(10) Calculation of k = 3 Processing metric: NOMOnto(11) Calculation of k = 3 Processing metric: POnto(12) Calculation of k = 3 Processing metric: PROnto(13) Calculation of k = 3 Processing metric: RFCOnto(14) Calculation of k = 3 Processing metric: RROnto(15) Calculation of k = 3 Processing metric: TMOnto(16) Calculation of k = 3 Processing metric: TMOnto2(17) Calculation of k = 3 Processing metric: WMCOnto(18) Calculation of k = 3 Processing metric: WMCOnto2(19) Calculation of k = 3 > assay(dataFrame) Metric Mean_stability_k_3 [1,] "ANOnto" "0.711599421597794" [2,] "AROnto" "0.834242802235359" [3,] "CBOOnto" "0.836200447888132" [4,] "CBOOnto2" "0.836200447888132" [5,] "CROnto" "0.80871022609772" [6,] "DITOnto" "0.802620378293628" [7,] "INROnto" "0.813132039213596" [8,] "LCOMOnto" "0.995402775270891" [9,] "NACOnto" "0.705135779579475" [10,] "NOCOnto" "0.902528819875511" [11,] "NOMOnto" "0.793513639960901" [12,] "POnto" "0.660145923222329" [13,] "PROnto" "0.960518110441289" [14,] "RFCOnto" "0.765127486244089" [15,] "RROnto" "0.960518110441289" [16,] "TMOnto" "0.862955680341511" [17,] "TMOnto2" "0.953719590152899" [18,] "WMCOnto" "0.85715656831332" [19,] "WMCOnto2" "0.904134166028688" > # Metric Mean_stability_k_3 > # [1,] "ANOnto" "0.711599421597794" > # [2,] "AROnto" "0.834242802235359" > # [3,] "CBOOnto" "0.836200447888132" > # [4,] "CBOOnto2" "0.836200447888132" > # [5,] "CROnto" "0.80871022609772" > # [6,] "DITOnto" "0.802620378293628" > # [7,] "INROnto" "0.813132039213596" > # [8,] "LCOMOnto" "0.995402775270891" > # [9,] "NACOnto" "0.705135779579475" > # [10,] "NOCOnto" "0.902528819875511" > # [11,] "NOMOnto" "0.793513639960901" > # [12,] "POnto" "0.660145923222329" > # [13,] "PROnto" "0.960518110441289" > # [14,] "RFCOnto" "0.765127486244089" > # [15,] "RROnto" "0.960518110441289" > # [16,] "TMOnto" "0.862955680341511" > # [17,] "TMOnto2" "0.953719590152899" > # [18,] "WMCOnto" "0.85715656831332" > # [19,] "WMCOnto2" "0.904134166028688" > > dataFrame <- stability(data=ontMetrics, cbi="kmeans", k=5, bs=100) Data loaded. Number of rows: 80 Number of columns: 20 Processing metric: ANOnto(1) Calculation of k = 5 Processing metric: AROnto(2) Calculation of k = 5 Processing metric: CBOOnto(3) Calculation of k = 5 Processing metric: CBOOnto2(4) Calculation of k = 5 Processing metric: CROnto(5) Calculation of k = 5 Processing metric: DITOnto(6) Calculation of k = 5 Processing metric: INROnto(7) Calculation of k = 5 Processing metric: LCOMOnto(8) Calculation of k = 5 Processing metric: NACOnto(9) Calculation of k = 5 Processing metric: NOCOnto(10) Calculation of k = 5 Processing metric: NOMOnto(11) Calculation of k = 5 Processing metric: POnto(12) Calculation of k = 5 Processing metric: PROnto(13) Calculation of k = 5 Processing metric: RFCOnto(14) Calculation of k = 5 Processing metric: RROnto(15) Calculation of k = 5 Processing metric: TMOnto(16) Calculation of k = 5 Processing metric: TMOnto2(17) Calculation of k = 5 Processing metric: WMCOnto(18) Calculation of k = 5 Processing metric: WMCOnto2(19) Calculation of k = 5 > assay(dataFrame) Metric Mean_stability_k_5 [1,] "ANOnto" "0.53661574785721" [2,] "AROnto" "0.808877375863211" [3,] "CBOOnto" "0.773161766854306" [4,] "CBOOnto2" "0.773161766854306" [5,] "CROnto" "0.747939612559589" [6,] "DITOnto" "0.738901091226716" [7,] "INROnto" "0.804579603939195" [8,] "LCOMOnto" "0.703629344931179" [9,] "NACOnto" "0.663958844840551" [10,] "NOCOnto" "0.899994756895055" [11,] "NOMOnto" "0.758789978458299" [12,] "POnto" "0.646480707690646" [13,] "PROnto" "0.782307410022412" [14,] "RFCOnto" "0.726761185593769" [15,] "RROnto" "0.782307410022412" [16,] "TMOnto" "0.88221333660635" [17,] "TMOnto2" "0.830282245373099" [18,] "WMCOnto" "0.747236615208537" [19,] "WMCOnto2" "0.752468990321845" > # Metric Mean_stability_k_5 > # [1,] "ANOnto" "0.53661574785721" > # [2,] "AROnto" "0.808877375863211" > # [3,] "CBOOnto" "0.773161766854306" > # [4,] "CBOOnto2" "0.773161766854306" > # [5,] "CROnto" "0.747939612559589" > # [6,] "DITOnto" "0.738901091226716" > # [7,] "INROnto" "0.804579603939195" > # [8,] "LCOMOnto" "0.703629344931179" > # [9,] "NACOnto" "0.663958844840551" > # [10,] "NOCOnto" "0.899994756895055" > # [11,] "NOMOnto" "0.758789978458299" > # [12,] "POnto" "0.646480707690646" > # [13,] "PROnto" "0.782307410022412" > # [14,] "RFCOnto" "0.726761185593769" > # [15,] "RROnto" "0.782307410022412" > # [16,] "TMOnto" "0.88221333660635" > # [17,] "TMOnto2" "0.830282245373099" > # [18,] "WMCOnto" "0.747236615208537" > # [19,] "WMCOnto2" "0.752468990321845" > > dataFrame <- stabilityRange(data=ontMetrics, cbi="kmeans", k.range = c(3,5), bs=100) Data loaded. Number of rows: 80 Number of columns: 20 Processing metric: ANOnto(1) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: AROnto(2) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: CBOOnto(3) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: CBOOnto2(4) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: CROnto(5) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: DITOnto(6) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: INROnto(7) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: LCOMOnto(8) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: NACOnto(9) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: NOCOnto(10) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: NOMOnto(11) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: POnto(12) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: PROnto(13) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: RFCOnto(14) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: RROnto(15) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: TMOnto(16) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: TMOnto2(17) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: WMCOnto(18) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 Processing metric: WMCOnto2(19) Calculation of k = 3 Calculation of k = 4 Calculation of k = 5 > assay(dataFrame) Metric Mean_stability_k_3 Mean_stability_k_4 Mean_stability_k_5 [1,] "ANOnto" "0.711599421597794" "0.661877018484356" "0.53661574785721" [2,] "AROnto" "0.834242802235359" "0.905679508527523" "0.808877375863211" [3,] "CBOOnto" "0.836200447888132" "0.809715382620901" "0.773161766854306" [4,] "CBOOnto2" "0.836200447888132" "0.809715382620901" "0.773161766854306" [5,] "CROnto" "0.80871022609772" "0.848428661689236" "0.747939612559589" [6,] "DITOnto" "0.802620378293628" "0.801976319968573" "0.738901091226716" [7,] "INROnto" "0.813132039213596" "0.833324929464065" "0.804579603939195" [8,] "LCOMOnto" "0.995402775270891" "0.758953924881616" "0.703629344931179" [9,] "NACOnto" "0.705135779579475" "0.679182045909186" "0.663958844840551" [10,] "NOCOnto" "0.902528819875511" "0.844518653163586" "0.899994756895055" [11,] "NOMOnto" "0.793513639960901" "0.779713596698101" "0.758789978458299" [12,] "POnto" "0.660145923222329" "0.795675361207579" "0.646480707690646" [13,] "PROnto" "0.960518110441289" "0.790969731730725" "0.782307410022412" [14,] "RFCOnto" "0.765127486244089" "0.790802265552443" "0.726761185593769" [15,] "RROnto" "0.960518110441289" "0.790969731730725" "0.782307410022412" [16,] "TMOnto" "0.862955680341511" "0.904973710968594" "0.88221333660635" [17,] "TMOnto2" "0.953719590152899" "0.868195348078741" "0.830282245373099" [18,] "WMCOnto" "0.85715656831332" "0.854182751568963" "0.747236615208537" [19,] "WMCOnto2" "0.904134166028688" "0.883417390847072" "0.752468990321845" > # Metric Mean_stability_k_3 Mean_stability_k_4 Mean_stability_k_5 > # [1,] "ANOnto" "0.711599421597794" "0.661877018484356" "0.53661574785721" > # [2,] "AROnto" "0.834242802235359" "0.905679508527523" "0.808877375863211" > # [3,] "CBOOnto" "0.836200447888132" "0.809715382620901" "0.773161766854306" > # [4,] "CBOOnto2" "0.836200447888132" "0.809715382620901" "0.773161766854306" > # [5,] "CROnto" "0.80871022609772" "0.848428661689236" "0.747939612559589" > # [6,] "DITOnto" "0.802620378293628" "0.801976319968573" "0.738901091226716" > # [7,] "INROnto" "0.813132039213596" "0.833324929464065" "0.804579603939195" > # [8,] "LCOMOnto" "0.995402775270891" "0.758953924881616" "0.703629344931179" > # [9,] "NACOnto" "0.705135779579475" "0.679182045909186" "0.663958844840551" > # [10,] "NOCOnto" "0.902528819875511" "0.844518653163586" "0.899994756895055" > # [11,] "NOMOnto" "0.793513639960901" "0.779713596698101" "0.758789978458299" > # [12,] "POnto" "0.660145923222329" "0.795675361207579" "0.646480707690646" > # [13,] "PROnto" "0.960518110441289" "0.790969731730725" "0.782307410022412" > # [14,] "RFCOnto" "0.765127486244089" "0.790802265552443" "0.726761185593769" > # [15,] "RROnto" "0.960518110441289" "0.790969731730725" "0.782307410022412" > # [16,] "TMOnto" "0.862955680341511" "0.904973710968594" "0.88221333660635" > # [17,] "TMOnto2" "0.953719590152899" "0.868195348078741" "0.830282245373099" > # [18,] "WMCOnto" "0.85715656831332" "0.854182751568963" "0.747236615208537" > # [19,] "WMCOnto2" "0.904134166028688" "0.883417390847072" "0.752468990321845" > > > #dataFrame <- stability(data=ontMetrics, cbi="kmeans", k=3, all_metrics = TRUE, bs=100) > #assay(dataFrame) > # Metric Mean_stability_k_3 > # [1,] "all_metrics" "0.846238406081907" > > #dataFrame <- stability(data=ontMetrics, cbi="kmeans", k=5, all_metrics = TRUE, bs=100) > #assay(dataFrame) > # Metric Mean_stability_k_3 > # [1,] "all_metrics" "0.803322946463351" > > #dataFrame <- stabilityRange(data=ontMetrics, cbi="kmeans", k.range = c(3,5), all_metrics = TRUE, bs=100) > #assay(dataFrame) > # Metric Mean_stability_k_3 Mean_stability_k_4 Mean_stability_k_5 > # [1,] "all_metrics" "0.846238406081907" "0.783588073668732" "0.803322946463351" > > proc.time() user system elapsed 15.14 0.92 16.14
evaluomeR.Rcheck/evaluomeR-Ex.timings
name | user | system | elapsed | |
annotateClustersByMetric | 0.91 | 0.08 | 1.00 | |
evaluomeRSupportedCBI | 0.00 | 0.02 | 0.02 | |
getDataQualityRange | 0.38 | 0.05 | 0.42 | |
getMetricsRelevancy | 1.14 | 0.03 | 1.17 | |
getOptimalKValue | 0.23 | 0.00 | 0.24 | |
globalMetric | 2.08 | 0.03 | 2.10 | |
metricsCorrelations | 0.06 | 0.00 | 0.07 | |
plotMetricsBoxplot | 1.17 | 0.03 | 1.20 | |
plotMetricsCluster | 0.05 | 0.00 | 0.05 | |
plotMetricsClusterComparison | 0.22 | 0.06 | 0.28 | |
plotMetricsMinMax | 0.51 | 0.03 | 0.55 | |
plotMetricsViolin | 1.65 | 0.03 | 1.31 | |
quality | 0.10 | 0.02 | 0.12 | |
qualityRange | 0.24 | 0.05 | 0.30 | |
qualitySet | 0.05 | 0.01 | 0.06 | |
stability | 1.64 | 0.03 | 1.69 | |
stabilityRange | 1.82 | 0.13 | 1.95 | |
stabilitySet | 0.27 | 0.02 | 0.28 | |