| Back to Multiple platform build/check report for BioC 3.20: simplified long |
|
This page was generated on 2024-12-23 12:07 -0500 (Mon, 23 Dec 2024).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo2 | Linux (Ubuntu 24.04.1 LTS) | x86_64 | 4.4.2 (2024-10-31) -- "Pile of Leaves" | 4744 |
| palomino8 | Windows Server 2022 Datacenter | x64 | 4.4.2 (2024-10-31 ucrt) -- "Pile of Leaves" | 4487 |
| merida1 | macOS 12.7.5 Monterey | x86_64 | 4.4.2 (2024-10-31) -- "Pile of Leaves" | 4515 |
| kjohnson1 | macOS 13.6.6 Ventura | arm64 | 4.4.2 (2024-10-31) -- "Pile of Leaves" | 4467 |
| 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 680/2289 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| evaluomeR 1.22.0 (landing page) José Antonio Bernabé-Díaz
| nebbiolo2 | Linux (Ubuntu 24.04.1 LTS) / x86_64 | OK | OK | WARNINGS |  | ||||||||
| palomino8 | Windows Server 2022 Datacenter / x64 | OK | OK | WARNINGS | OK | | ||||||||
| merida1 | macOS 12.7.5 Monterey / x86_64 | OK | OK | WARNINGS | OK | | ||||||||
| kjohnson1 | macOS 13.6.6 Ventura / arm64 | 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: 1.22.0 |
| Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:evaluomeR.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings evaluomeR_1.22.0.tar.gz |
| StartedAt: 2024-12-20 19:08:45 -0500 (Fri, 20 Dec 2024) |
| EndedAt: 2024-12-20 19:14:09 -0500 (Fri, 20 Dec 2024) |
| EllapsedTime: 323.3 seconds |
| RetCode: 0 |
| Status: WARNINGS |
| CheckDir: evaluomeR.Rcheck |
| Warnings: 3 |
##############################################################################
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###
### Running command:
###
### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:evaluomeR.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings evaluomeR_1.22.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/evaluomeR.Rcheck’
* using R version 4.4.2 (2024-10-31)
* using platform: aarch64-apple-darwin20
* R was compiled by
Apple clang version 14.0.0 (clang-1400.0.29.202)
GNU Fortran (GCC) 12.2.0
* running under: macOS Ventura 13.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘evaluomeR/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘evaluomeR’ version ‘1.22.0’
* 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 for sufficient/correct file permissions ... 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
‘/Users/biocbuild/bbs-3.20-bioc/meat/evaluomeR.Rcheck/00check.log’
for details.
evaluomeR.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL evaluomeR ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/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 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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, saveRDS, 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
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 4.7-1.2
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
12.734 0.554 13.320
evaluomeR.Rcheck/tests/testAnalysis.Rout
R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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, saveRDS, 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
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 4.7-1.2
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
13.367 0.591 13.958
evaluomeR.Rcheck/tests/testCBI.Rout
R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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, saveRDS, 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
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 4.7-1.2
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" "0.7935136399609"
[12,] "POnto" "0.660145923222329"
[13,] "PROnto" "0.960518110441289"
[14,] "RFCOnto" "0.76512748624409"
[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
11.787 0.501 12.276
evaluomeR.Rcheck/tests/testMetricsRelevancy.Rout
R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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, saveRDS, 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
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 4.7-1.2
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: 36267.83
trimmed cases: 1 5 13 34 41 68 73 79 2 21 59 67 71
#non-zero weights: 20
3 clusters of sizes 31, 26, 23
Cluster labels: 3 1 3 2 1 2 1 2 3 2 2 3 3 1 2 1 2 1 2 1 1 1 1 3 2 3 3 3 2 1 2 3 3 3 1 2 3 1 1 2 1 1 2 1 3 2 3 1 3 3 3 1 3 3 1 2 1 2 2 3 2 3 2 3 1 3 3 3 2 1 3 3 1 1 3 3 1 2 3 2
> # Trimmed cases from input (row indexes)
> metricsRelevancy$trimmed_cases
[1] 1 2 5 13 21 34 41 59 67 68 71 73 79
> # Metrics relevancy table
> metricsRelevancy$relevancy
metric weight
1 Description 9.999835e-01
7 DITOnto 3.782486e-03
19 WMCOnto 3.559593e-03
3 AROnto 2.391771e-03
9 LCOMOnto 4.148457e-04
15 RFCOnto 3.485766e-04
12 NOMOnto 2.586262e-04
11 NOCOnto 1.394339e-04
20 WMCOnto2 3.226109e-05
13 POnto 3.168475e-05
18 TMOnto2 1.159899e-05
14 PROnto 7.809770e-06
16 RROnto 7.809770e-06
2 ANOnto 7.428264e-06
4 CBOOnto 6.718581e-06
5 CBOOnto2 6.718581e-06
10 NACOnto 4.589905e-06
8 INROnto 2.615559e-06
17 TMOnto 1.667959e-06
6 CROnto 1.063741e-06
>
>
> 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
11.560 0.516 12.158
evaluomeR.Rcheck/tests/testQuality.Rout
R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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, saveRDS, 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
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 4.7-1.2
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" "0.6209566207527" "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" "0.6209566207527" "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
11.062 0.470 11.540
evaluomeR.Rcheck/tests/testStability.Rout
R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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, saveRDS, 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
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 4.7-1.2
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" "0.7935136399609"
[12,] "POnto" "0.660145923222329"
[13,] "PROnto" "0.960518110441289"
[14,] "RFCOnto" "0.76512748624409"
[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" "0.7935136399609" "0.779713596698101" "0.758789978458299"
[12,] "POnto" "0.660145923222329" "0.795675361207579" "0.646480707690646"
[13,] "PROnto" "0.960518110441289" "0.790969731730725" "0.782307410022412"
[14,] "RFCOnto" "0.76512748624409" "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
16.829 0.722 17.559
evaluomeR.Rcheck/evaluomeR-Ex.timings
| name | user | system | elapsed | |
| annotateClustersByMetric | 1.013 | 0.047 | 1.060 | |
| evaluomeRSupportedCBI | 0 | 0 | 0 | |
| getDataQualityRange | 0.272 | 0.021 | 0.294 | |
| getMetricsRelevancy | 2.197 | 0.036 | 2.233 | |
| getOptimalKValue | 0.258 | 0.008 | 0.266 | |
| globalMetric | 1.034 | 0.045 | 1.079 | |
| metricsCorrelations | 0.040 | 0.006 | 0.046 | |
| plotMetricsBoxplot | 0.551 | 0.014 | 0.566 | |
| plotMetricsCluster | 0.043 | 0.005 | 0.048 | |
| plotMetricsClusterComparison | 0.285 | 0.007 | 0.293 | |
| plotMetricsMinMax | 0.565 | 0.009 | 0.574 | |
| plotMetricsViolin | 0.813 | 0.029 | 0.842 | |
| quality | 0.086 | 0.009 | 0.094 | |
| qualityRange | 0.170 | 0.013 | 0.184 | |
| qualitySet | 0.044 | 0.004 | 0.049 | |
| stability | 1.943 | 0.094 | 2.037 | |
| stabilityRange | 1.965 | 0.055 | 2.020 | |
| stabilitySet | 0.279 | 0.005 | 0.284 | |