Back to Build/check report for BioC 3.24:   simplified   long
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This page was generated on 2026-04-22 11:58 -0400 (Wed, 22 Apr 2026).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 24.04.4 LTS)x86_644.6.0 RC (2026-04-17 r89917) -- "Because it was There" 4796
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 2021/2351HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.21.1  (landing page)
Joshua David Campbell
Snapshot Date: 2026-04-21 14:59 -0400 (Tue, 21 Apr 2026)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: 15d4a13
git_last_commit_date: 2026-01-11 08:42:53 -0400 (Sun, 11 Jan 2026)
nebbiolo2Linux (Ubuntu 24.04.4 LTS) / x86_64  OK    OK    WARNINGS  NO, package depends on 'GSVA' which is not available
See other builds for singleCellTK in R Universe.


CHECK results for singleCellTK on nebbiolo2

To the developers/maintainers of the singleCellTK package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/singleCellTK.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.

raw results


Summary

Package: singleCellTK
Version: 2.21.1
Command: /home/biocbuild/bbs-3.24-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.24-bioc/R/site-library --timings singleCellTK_2.21.1.tar.gz
StartedAt: 2026-04-22 04:57:21 -0400 (Wed, 22 Apr 2026)
EndedAt: 2026-04-22 05:13:23 -0400 (Wed, 22 Apr 2026)
EllapsedTime: 962.9 seconds
RetCode: 0
Status:   WARNINGS  
CheckDir: singleCellTK.Rcheck
Warnings: 1

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.24-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.24-bioc/R/site-library --timings singleCellTK_2.21.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.24-bioc/meat/singleCellTK.Rcheck’
* using R version 4.6.0 RC (2026-04-17 r89917)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
    GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
* running under: Ubuntu 24.04.4 LTS
* using session charset: UTF-8
* current time: 2026-04-22 08:57:21 UTC
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.21.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 80 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable.  Move as many as possible to Suggests and
use conditionally.
* 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 ‘singleCellTK’ can be installed ... OK
* checking installed package size ... INFO
  installed size is  7.0Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    extdata   1.6Mb
    shiny     3.0Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking 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 loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
  dedupRowNames.Rd: SingleCellExperiment-class
  detectCellOutlier.Rd: colData
  diffAbundanceFET.Rd: colData
  downSampleCells.Rd: SingleCellExperiment-class
  downSampleDepth.Rd: SingleCellExperiment-class
  featureIndex.Rd: SummarizedExperiment-class,
    SingleCellExperiment-class
  getBiomarker.Rd: SingleCellExperiment-class
  getDEGTopTable.Rd: SingleCellExperiment-class
  getEnrichRResult.Rd: SingleCellExperiment-class
  getFindMarkerTopTable.Rd: SingleCellExperiment-class
  getGenesetNamesFromCollection.Rd: SingleCellExperiment-class
  getPathwayResultNames.Rd: SingleCellExperiment-class
  getSampleSummaryStatsTable.Rd: SingleCellExperiment-class, assay,
    colData
  getSoupX.Rd: SingleCellExperiment-class
  getTSCANResults.Rd: SingleCellExperiment-class
  getTopHVG.Rd: SingleCellExperiment-class
  importAlevin.Rd: DelayedArray, readMM
  importAnnData.Rd: DelayedArray, readMM
  importBUStools.Rd: readMM
  importCellRanger.Rd: readMM, DelayedArray
  importCellRangerV2Sample.Rd: readMM, DelayedArray
  importCellRangerV3Sample.Rd: readMM, DelayedArray
  importDropEst.Rd: DelayedArray, readMM
  importExampleData.Rd: scRNAseq, Matrix, DelayedArray,
    ReprocessedFluidigmData, ReprocessedAllenData, NestorowaHSCData
  importFromFiles.Rd: readMM, DelayedArray, SingleCellExperiment-class
  importGeneSetsFromCollection.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GeneSetCollection, GSEABase, metadata
  importGeneSetsFromGMT.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, getGmt, GSEABase, metadata
  importGeneSetsFromList.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GSEABase, metadata
  importGeneSetsFromMSigDB.Rd: SingleCellExperiment-class, msigdbr,
    GeneSetCollection-class, GSEABase, metadata
  importMitoGeneSet.Rd: SingleCellExperiment-class,
    GeneSetCollection-class, GSEABase, metadata
  importMultipleSources.Rd: DelayedArray
  importOptimus.Rd: readMM, DelayedArray
  importSEQC.Rd: readMM, DelayedArray
  importSTARsolo.Rd: readMM, DelayedArray
  iterateSimulations.Rd: SingleCellExperiment-class
  listSampleSummaryStatsTables.Rd: SingleCellExperiment-class, metadata
  plotBarcodeRankDropsResults.Rd: SingleCellExperiment-class
  plotBarcodeRankScatter.Rd: SingleCellExperiment-class
  plotBatchCorrCompare.Rd: SingleCellExperiment-class
  plotBatchVariance.Rd: SingleCellExperiment-class
  plotBcdsResults.Rd: SingleCellExperiment-class
  plotClusterAbundance.Rd: colData
  plotCxdsResults.Rd: SingleCellExperiment-class
  plotDEGHeatmap.Rd: SingleCellExperiment-class
  plotDEGRegression.Rd: SingleCellExperiment-class
  plotDEGViolin.Rd: SingleCellExperiment-class
  plotDEGVolcano.Rd: SingleCellExperiment-class
  plotDecontXResults.Rd: SingleCellExperiment-class
  plotDoubletFinderResults.Rd: SingleCellExperiment-class
  plotEmptyDropsResults.Rd: SingleCellExperiment-class
  plotEmptyDropsScatter.Rd: SingleCellExperiment-class
  plotEnrichR.Rd: SingleCellExperiment-class
  plotFindMarkerHeatmap.Rd: SingleCellExperiment-class
  plotPCA.Rd: SingleCellExperiment-class
  plotPathway.Rd: SingleCellExperiment-class
  plotRunPerCellQCResults.Rd: SingleCellExperiment-class
  plotSCEBarAssayData.Rd: SingleCellExperiment-class
  plotSCEBarColData.Rd: SingleCellExperiment-class
  plotSCEBatchFeatureMean.Rd: SingleCellExperiment-class
  plotSCEDensity.Rd: SingleCellExperiment-class
  plotSCEDensityAssayData.Rd: SingleCellExperiment-class
  plotSCEDensityColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceFeatures.Rd: SingleCellExperiment-class
  plotSCEHeatmap.Rd: SingleCellExperiment-class
  plotSCEScatter.Rd: SingleCellExperiment-class
  plotSCEViolin.Rd: SingleCellExperiment-class
  plotSCEViolinAssayData.Rd: SingleCellExperiment-class
  plotSCEViolinColData.Rd: SingleCellExperiment-class
  plotScDblFinderResults.Rd: SingleCellExperiment-class
  plotScdsHybridResults.Rd: SingleCellExperiment-class
  plotScrubletResults.Rd: SingleCellExperiment-class
  plotSoupXResults.Rd: SingleCellExperiment-class
  plotTSCANClusterDEG.Rd: SingleCellExperiment-class
  plotTSCANClusterPseudo.Rd: SingleCellExperiment-class
  plotTSCANDimReduceFeatures.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeGenes.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeHeatmap.Rd: SingleCellExperiment-class
  plotTSCANResults.Rd: SingleCellExperiment-class
  plotTSNE.Rd: SingleCellExperiment-class
  plotUMAP.Rd: SingleCellExperiment-class
  readSingleCellMatrix.Rd: DelayedArray
  reportCellQC.Rd: SingleCellExperiment-class
  reportClusterAbundance.Rd: colData
  reportDiffAbundanceFET.Rd: colData
  retrieveSCEIndex.Rd: SingleCellExperiment-class
  runBBKNN.Rd: SingleCellExperiment-class
  runBarcodeRankDrops.Rd: SingleCellExperiment-class, colData
  runBcds.Rd: SingleCellExperiment-class, colData
  runCellQC.Rd: colData
  runComBatSeq.Rd: SingleCellExperiment-class
  runCxds.Rd: SingleCellExperiment-class, colData
  runCxdsBcdsHybrid.Rd: colData
  runDEAnalysis.Rd: SingleCellExperiment-class
  runDecontX.Rd: colData
  runDimReduce.Rd: SingleCellExperiment-class
  runDoubletFinder.Rd: SingleCellExperiment-class
  runDropletQC.Rd: colData
  runEmptyDrops.Rd: SingleCellExperiment-class, colData
  runEnrichR.Rd: SingleCellExperiment-class
  runFastMNN.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runFeatureSelection.Rd: SingleCellExperiment-class
  runFindMarker.Rd: SingleCellExperiment-class
  runGSVA.Rd: SingleCellExperiment-class
  runHarmony.Rd: SingleCellExperiment-class
  runKMeans.Rd: SingleCellExperiment-class, colData
  runLimmaBC.Rd: SingleCellExperiment-class, assay
  runMNNCorrect.Rd: SingleCellExperiment-class, assay,
    BiocParallelParam-class
  runModelGeneVar.Rd: SingleCellExperiment-class
  runPerCellQC.Rd: SingleCellExperiment-class, BiocParallelParam,
    colData
  runSCANORAMA.Rd: SingleCellExperiment-class, assay
  runSCMerge.Rd: SingleCellExperiment-class, colData, assay,
    BiocParallelParam-class
  runScDblFinder.Rd: SingleCellExperiment-class, colData
  runScranSNN.Rd: SingleCellExperiment-class, reducedDim, assay,
    altExp, colData, igraph
  runScrublet.Rd: SingleCellExperiment-class, colData
  runSingleR.Rd: SingleCellExperiment-class
  runSoupX.Rd: SingleCellExperiment-class
  runTSCAN.Rd: SingleCellExperiment-class
  runTSCANClusterDEAnalysis.Rd: SingleCellExperiment-class
  runTSCANDEG.Rd: SingleCellExperiment-class
  runTSNE.Rd: SingleCellExperiment-class
  runUMAP.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runVAM.Rd: SingleCellExperiment-class
  runZINBWaVE.Rd: SingleCellExperiment-class, colData,
    BiocParallelParam-class
  sampleSummaryStats.Rd: SingleCellExperiment-class, assay, colData
  scaterPCA.Rd: SingleCellExperiment-class, BiocParallelParam-class
  scaterlogNormCounts.Rd: logNormCounts
  sctkListGeneSetCollections.Rd: GeneSetCollection-class
  sctkPythonInstallConda.Rd: conda_install, reticulate, conda_create
  sctkPythonInstallVirtualEnv.Rd: virtualenv_install, reticulate,
    virtualenv_create
  selectSCTKConda.Rd: reticulate
  selectSCTKVirtualEnvironment.Rd: reticulate
  setRowNames.Rd: SingleCellExperiment-class
  setSCTKDisplayRow.Rd: SingleCellExperiment-class
  singleCellTK.Rd: SingleCellExperiment-class
  subsetSCECols.Rd: SingleCellExperiment-class
  subsetSCERows.Rd: SingleCellExperiment-class, altExp
  summarizeSCE.Rd: SingleCellExperiment-class
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... WARNING
Found the following significant warnings:

  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'librarySizeFactors' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in fitTrendVar(fm, fv, ...) : 'fitTrendVar' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in fitTrendVar(fm, fv, ...) : 'fitTrendVar' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'pairwiseWilcox' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'sumCountsAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'summarizeAssayByGroup' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in fitTrendVar(fm, fv, ...) : 'fitTrendVar' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'librarySizeFactors' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'aggregateAcrossCells' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
  Warning in fitTrendVar(fm, fv, ...) : 'fitTrendVar' is deprecated.
  Warning in .local(x, ...) : 'normalizeCounts' is deprecated.
Deprecated functions may be defunct as soon as of the next release of
R.
See ?Deprecated.
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
plotDoubletFinderResults 36.503  0.169  36.672
runSeuratSCTransform     29.996  0.429  30.429
runDoubletFinder         30.048  0.329  30.381
plotScDblFinderResults   28.957  0.466  26.427
runScDblFinder           15.939  0.502  13.498
plotBatchCorrCompare     12.850  0.415  13.266
plotScdsHybridResults    11.244  0.460  11.101
importExampleData         9.461  1.124  11.040
plotBcdsResults           8.197  0.303   7.876
plotDecontXResults        8.033  0.205   8.238
plotEmptyDropsScatter     7.446  0.016   7.902
runUMAP                   7.210  0.094   7.304
plotCxdsResults           7.110  0.076   7.186
plotEmptyDropsResults     7.109  0.021   7.132
plotUMAP                  6.280  0.293   6.574
runEmptyDrops             6.355  0.011   6.367
detectCellOutlier         5.785  0.119   5.906
runDecontX                5.895  0.009   5.904
plotDEGViolin             5.112  0.056   5.162
convertSCEToSeurat        4.774  0.252   5.028
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 1 WARNING, 1 NOTE
See
  ‘/home/biocbuild/bbs-3.24-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.24-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.24-bioc/R/site-library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.21.1’
** using staged installation
** R
** data
** exec
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (singleCellTK)

Tests output

singleCellTK.Rcheck/tests/spelling.Rout


R version 4.6.0 RC (2026-04-17 r89917) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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.

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
All Done!
> 
> proc.time()
   user  system elapsed 
  0.145   0.042   0.175 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.6.0 RC (2026-04-17 r89917) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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(testthat)
> library(singleCellTK)
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
Loading required package: generics

Attaching package: 'generics'

The following objects are masked from 'package:base':

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


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, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, 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: Seqinfo
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: SingleCellExperiment
Loading required package: DelayedArray
Loading required package: Matrix

Attaching package: 'Matrix'

The following object is masked from 'package:S4Vectors':

    expand

Loading required package: S4Arrays
Loading required package: abind

Attaching package: 'S4Arrays'

The following object is masked from 'package:abind':

    abind

The following object is masked from 'package:base':

    rowsum

Loading required package: SparseArray

Attaching package: 'DelayedArray'

The following objects are masked from 'package:base':

    apply, scale, sweep


Attaching package: 'singleCellTK'

The following object is masked from 'package:BiocGenerics':

    plotPCA

> 
> test_check("singleCellTK")
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 0 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 1 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
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  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Uploading data to Enrichr... Done.
  Querying HDSigDB_Human_2021... Done.
Parsing results... Done.
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
[05:11:10] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

[05:11:11] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 390
Number of edges: 9849

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 84 | SKIP 0 | PASS 225 ]

[ FAIL 0 | WARN 84 | SKIP 0 | PASS 225 ]
> 
> proc.time()
   user  system elapsed 
274.974  11.857 284.692 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0020.0010.002
SEG0.0020.0000.003
calcEffectSizes0.1510.0040.155
combineSCE0.7390.0990.838
computeZScore0.2930.0110.305
convertSCEToSeurat4.7740.2525.028
convertSeuratToSCE0.3370.0030.340
dedupRowNames0.0620.0000.063
detectCellOutlier5.7850.1195.906
diffAbundanceFET0.0510.0040.055
discreteColorPalette0.0050.0000.006
distinctColors0.0020.0000.002
downSampleCells0.4890.0130.501
downSampleDepth0.4110.0260.437
expData-ANY-character-method0.1190.0040.122
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.1580.0030.160
expData-set0.1480.0040.153
expData0.1170.0010.118
expDataNames-ANY-method0.1140.0000.113
expDataNames0.1180.0010.118
expDeleteDataTag0.0330.0000.033
expSetDataTag0.0240.0000.023
expTaggedData0.0240.0000.024
exportSCE0.0220.0000.022
exportSCEtoAnnData0.0940.0030.097
exportSCEtoFlatFile0.0880.0070.095
featureIndex0.0340.0010.034
generateSimulatedData0.0470.0020.050
getBiomarker0.0570.0000.057
getDEGTopTable0.6730.0550.728
getDiffAbundanceResults0.0460.0000.046
getEnrichRResult0.5340.0663.347
getFindMarkerTopTable1.7110.2301.942
getMSigDBTable0.0030.0000.003
getPathwayResultNames0.0210.0010.022
getSampleSummaryStatsTable0.2710.0180.289
getSoupX000
getTSCANResults1.1520.1881.340
getTopHVG0.7320.1030.836
importAnnData0.0010.0000.002
importBUStools0.1460.0160.162
importCellRanger0.7500.0440.795
importCellRangerV2Sample0.1530.0140.167
importCellRangerV3Sample0.2930.0200.314
importDropEst0.1960.0020.198
importExampleData 9.461 1.12411.040
importGeneSetsFromCollection0.0710.0000.072
importGeneSetsFromGMT0.0630.0010.064
importGeneSetsFromList0.1220.0110.132
importGeneSetsFromMSigDB2.3340.1832.517
importMitoGeneSet0.0500.0020.052
importOptimus0.0020.0000.001
importSEQC0.110.000.11
importSTARsolo0.1390.0020.140
iterateSimulations0.1620.0040.166
listSampleSummaryStatsTables0.2950.0020.297
mergeSCEColData0.3590.0050.366
mouseBrainSubsetSCE0.0360.0000.037
msigdb_table0.0010.0000.001
plotBarcodeRankDropsResults0.8730.0150.889
plotBarcodeRankScatter0.7940.0080.803
plotBatchCorrCompare12.850 0.41513.266
plotBatchVariance0.4480.0650.512
plotBcdsResults8.1970.3037.876
plotBubble0.7770.0020.779
plotClusterAbundance1.3040.0011.305
plotCxdsResults7.1100.0767.186
plotDEGHeatmap2.0230.0142.037
plotDEGRegression4.0220.0344.051
plotDEGViolin5.1120.0565.162
plotDEGVolcano0.9140.0040.918
plotDecontXResults8.0330.2058.238
plotDimRed0.2780.0010.279
plotDoubletFinderResults36.503 0.16936.672
plotEmptyDropsResults7.1090.0217.132
plotEmptyDropsScatter7.4460.0167.902
plotFindMarkerHeatmap3.7830.0263.810
plotMASTThresholdGenes1.2320.0061.238
plotPCA0.3590.0040.363
plotPathway0.6660.0430.709
plotRunPerCellQCResults3.0480.0153.063
plotSCEBarAssayData0.2940.0010.295
plotSCEBarColData0.2390.0000.240
plotSCEBatchFeatureMean0.3950.0010.396
plotSCEDensity0.3080.0010.309
plotSCEDensityAssayData0.2680.0040.272
plotSCEDensityColData0.3170.0010.317
plotSCEDimReduceColData0.7260.0040.730
plotSCEDimReduceFeatures0.3730.0010.374
plotSCEHeatmap0.4530.0010.453
plotSCEScatter0.3430.0020.346
plotSCEViolin0.3450.0010.346
plotSCEViolinAssayData0.3820.0010.383
plotSCEViolinColData0.3550.0000.355
plotScDblFinderResults28.957 0.46626.427
plotScanpyDotPlot0.0220.0000.021
plotScanpyEmbedding0.0220.0000.022
plotScanpyHVG0.0210.0010.022
plotScanpyHeatmap0.0200.0010.021
plotScanpyMarkerGenes0.0210.0000.021
plotScanpyMarkerGenesDotPlot0.0210.0000.021
plotScanpyMarkerGenesHeatmap0.0220.0000.022
plotScanpyMarkerGenesMatrixPlot0.0220.0000.022
plotScanpyMarkerGenesViolin0.0220.0000.022
plotScanpyMatrixPlot0.0220.0000.022
plotScanpyPCA0.0210.0010.022
plotScanpyPCAGeneRanking0.0210.0000.021
plotScanpyPCAVariance0.0200.0010.021
plotScanpyViolin0.0220.0000.022
plotScdsHybridResults11.244 0.46011.101
plotScrubletResults0.0200.0020.021
plotSeuratElbow0.0200.0010.020
plotSeuratHVG0.0200.0000.021
plotSeuratJackStraw0.0200.0010.021
plotSeuratReduction0.0210.0000.021
plotSoupXResults0.0010.0000.000
plotTSCANClusterDEG4.7210.1434.865
plotTSCANClusterPseudo1.3850.0741.459
plotTSCANDimReduceFeatures1.3290.0531.382
plotTSCANPseudotimeGenes1.5880.0231.611
plotTSCANPseudotimeHeatmap1.3080.0151.323
plotTSCANResults1.2420.0111.252
plotTSNE0.3550.0010.355
plotTopHVG0.6370.0260.663
plotUMAP6.2800.2936.574
readSingleCellMatrix0.0050.0010.006
reportCellQC0.0810.0070.088
reportDropletQC0.0230.0010.023
reportQCTool0.0810.0060.087
retrieveSCEIndex0.0290.0000.029
runBBKNN000
runBarcodeRankDrops0.2210.0070.229
runBcds1.5040.1001.009
runCellQC0.0820.0010.083
runClusterSummaryMetrics0.3890.0110.400
runComBatSeq0.4560.0200.476
runCxds0.3050.0010.307
runCxdsBcdsHybrid1.5090.0460.980
runDEAnalysis0.3540.0030.357
runDecontX5.8950.0095.904
runDimReduce0.270.000.27
runDoubletFinder30.048 0.32930.381
runDropletQC0.0230.0000.023
runEmptyDrops6.3550.0116.367
runEnrichR0.5580.0862.928
runFastMNN1.6340.0381.672
runFeatureSelection0.2060.0090.216
runFindMarker1.4280.1101.539
runGSVA0.6120.0880.700
runHarmony0.0380.0020.041
runKMeans0.2040.0190.222
runLimmaBC0.0810.0050.086
runMNNCorrect0.3790.0210.400
runModelGeneVar0.2810.0120.293
runNormalization2.2050.3942.599
runPerCellQC0.3310.0030.334
runSCANORAMA000
runSCMerge0.0040.0000.004
runScDblFinder15.939 0.50213.498
runScanpyFindClusters0.0220.0000.021
runScanpyFindHVG0.0200.0010.021
runScanpyFindMarkers0.0210.0000.021
runScanpyNormalizeData0.0940.0000.094
runScanpyPCA0.0220.0000.021
runScanpyScaleData0.0210.0000.021
runScanpyTSNE0.0210.0000.021
runScanpyUMAP0.0200.0010.021
runScranSNN0.2830.0020.286
runScrublet0.0230.0000.023
runSeuratFindClusters0.0220.0000.022
runSeuratFindHVG0.4840.0000.484
runSeuratHeatmap0.0220.0010.023
runSeuratICA0.0220.0000.022
runSeuratJackStraw0.0220.0000.022
runSeuratNormalizeData0.0210.0000.021
runSeuratPCA0.0210.0000.022
runSeuratSCTransform29.996 0.42930.429
runSeuratScaleData0.0220.0010.023
runSeuratUMAP0.0200.0010.022
runSingleR0.0370.0000.037
runSoupX000
runTSCAN0.6460.0030.648
runTSCANClusterDEAnalysis0.7940.0090.803
runTSCANDEG0.7150.0260.741
runTSNE0.7250.0040.728
runUMAP7.2100.0947.304
runVAM0.2980.0010.300
runZINBWaVE0.0030.0010.005
sampleSummaryStats0.1530.0010.155
scaterCPM0.1490.0050.154
scaterPCA0.4640.0030.466
scaterlogNormCounts0.2460.0130.260
sce0.0220.0000.023
sctkListGeneSetCollections0.0760.0060.083
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda0.0000.0000.001
selectSCTKVirtualEnvironment000
setRowNames0.0920.0010.093
setSCTKDisplayRow0.4760.0080.483
singleCellTK0.0010.0000.000
subDiffEx0.3310.0070.338
subsetSCECols0.0920.0000.093
subsetSCERows0.2190.0060.225
summarizeSCE0.0650.0020.067
trimCounts0.2080.0090.217