Multiple platform build/check report for BioC 3.22 - CHECK results for singleCellTK on nebbiolo2

Hostname	OS	Arch (*)	R version	Installed pkgs
nebbiolo2	Linux (Ubuntu 24.04.3 LTS)	x86_64	4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root"	4901
lconway	macOS 12.7.6 Monterey	x86_64	4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root"	4691
kjohnson3	macOS 13.7.7 Ventura	arm64	4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root"	4637
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*

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: 2.20.0

Command: /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings singleCellTK_2.20.0.tar.gz

StartedAt: 2025-11-01 04:18:28 -0400 (Sat, 01 Nov 2025)

EndedAt: 2025-11-01 04:35:55 -0400 (Sat, 01 Nov 2025)

EllapsedTime: 1046.9 seconds

RetCode: 0

Status: OK

CheckDir: singleCellTK.Rcheck

Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings singleCellTK_2.20.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.1 Patched (2025-08-23 r88802)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
    GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
* running under: Ubuntu 24.04.3 LTS
* using session charset: UTF-8
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.20.0’
* 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 ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
importGeneSetsFromMSigDB 47.060  0.710  47.872
plotDoubletFinderResults 39.705  0.273  40.060
runDoubletFinder         34.073  0.206  34.283
plotScDblFinderResults   31.243  0.755  31.761
runSeuratSCTransform     28.085  0.208  28.297
runScDblFinder           20.113  0.500  20.295
plotBatchCorrCompare     13.052  0.211  13.442
importExampleData        11.817  0.431  12.700
plotScdsHybridResults     9.903  0.065   9.306
plotDecontXResults        9.303  0.013   9.316
plotBcdsResults           8.987  0.117   8.407
plotCxdsResults           8.057  0.068   8.208
runUMAP                   7.665  0.124   7.867
plotUMAP                  6.957  0.066   7.104
runDecontX                6.959  0.033   6.992
plotDEGViolin             6.759  0.059   6.813
plotEmptyDropsResults     6.732  0.036   6.768
plotEmptyDropsScatter     6.684  0.018   6.702
detectCellOutlier         6.517  0.158   6.676
runEmptyDrops             6.253  0.008   6.261
* 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 NOTE
See
  ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.

Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.22-bioc/R/site-library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.20.0’
** 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 4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root"
Copyright (C) 2025 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 
  0.146   0.040   0.174

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root"
Copyright (C) 2025 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   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%
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%
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 21 | SKIP 0 | PASS 225 ]

[ FAIL 0 | WARN 21 | SKIP 0 | PASS 225 ]
> 
> proc.time()
   user  system elapsed 
336.633   5.795 350.600

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.002	0.001	0.002
SEG	0.002	0.000	0.002
calcEffectSizes	0.188	0.011	0.199
combineSCE	0.733	0.003	0.736
computeZScore	0.240	0.007	0.247
convertSCEToSeurat	4.466	0.078	4.545
convertSeuratToSCE	0.338	0.002	0.340
dedupRowNames	0.056	0.000	0.056
detectCellOutlier	6.517	0.158	6.676
diffAbundanceFET	0.054	0.001	0.055
discreteColorPalette	0.005	0.000	0.006
distinctColors	0.003	0.001	0.002
downSampleCells	0.500	0.068	0.568
downSampleDepth	0.400	0.035	0.436
expData-ANY-character-method	0.119	0.000	0.119
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.159	0.004	0.163
expData-set	0.145	0.012	0.156
expData	0.12	0.00	0.12
expDataNames-ANY-method	0.117	0.000	0.118
expDataNames	0.110	0.004	0.113
expDeleteDataTag	0.028	0.004	0.033
expSetDataTag	0.022	0.003	0.025
expTaggedData	0.024	0.000	0.025
exportSCE	0.022	0.000	0.023
exportSCEtoAnnData	0.088	0.008	0.097
exportSCEtoFlatFile	0.091	0.008	0.099
featureIndex	0.031	0.005	0.037
generateSimulatedData	0.049	0.003	0.054
getBiomarker	0.055	0.004	0.060
getDEGTopTable	0.641	0.044	0.685
getDiffAbundanceResults	0.045	0.000	0.046
getEnrichRResult	0.571	0.059	3.584
getFindMarkerTopTable	1.600	0.053	1.653
getMSigDBTable	0.003	0.000	0.003
getPathwayResultNames	0.024	0.000	0.024
getSampleSummaryStatsTable	0.191	0.003	0.194
getSoupX	0	0	0
getTSCANResults	1.049	0.012	1.061
getTopHVG	0.832	0.036	0.868
importAnnData	0.001	0.000	0.001
importBUStools	0.151	0.001	0.153
importCellRanger	0.749	0.007	0.757
importCellRangerV2Sample	0.147	0.000	0.147
importCellRangerV3Sample	0.269	0.004	0.273
importDropEst	0.197	0.000	0.197
importExampleData	11.817	0.431	12.700
importGeneSetsFromCollection	1.930	0.019	1.949
importGeneSetsFromGMT	0.058	0.002	0.060
importGeneSetsFromList	0.118	0.000	0.119
importGeneSetsFromMSigDB	47.060	0.710	47.872
importMitoGeneSet	0.051	0.004	0.055
importOptimus	0.002	0.000	0.002
importSEQC	0.134	0.022	0.156
importSTARsolo	0.144	0.024	0.169
iterateSimulations	0.173	0.027	0.200
listSampleSummaryStatsTables	0.245	0.042	0.286
mergeSCEColData	0.377	0.006	0.382
mouseBrainSubsetSCE	0.036	0.000	0.036
msigdb_table	0.001	0.000	0.001
plotBarcodeRankDropsResults	0.854	0.005	0.859
plotBarcodeRankScatter	0.961	0.001	0.964
plotBatchCorrCompare	13.052	0.211	13.442
plotBatchVariance	0.468	0.000	0.468
plotBcdsResults	8.987	0.117	8.407
plotBubble	0.768	0.002	0.770
plotClusterAbundance	1.342	0.001	1.343
plotCxdsResults	8.057	0.068	8.208
plotDEGHeatmap	2.119	0.053	2.172
plotDEGRegression	4.386	0.013	4.392
plotDEGViolin	6.759	0.059	6.813
plotDEGVolcano	0.905	0.005	0.910
plotDecontXResults	9.303	0.013	9.316
plotDimRed	0.291	0.001	0.293
plotDoubletFinderResults	39.705	0.273	40.060
plotEmptyDropsResults	6.732	0.036	6.768
plotEmptyDropsScatter	6.684	0.018	6.702
plotFindMarkerHeatmap	3.773	0.017	3.790
plotMASTThresholdGenes	1.250	0.012	1.262
plotPCA	0.366	0.001	0.367
plotPathway	0.676	0.002	0.678
plotRunPerCellQCResults	3.022	0.007	3.029
plotSCEBarAssayData	0.323	0.002	0.325
plotSCEBarColData	0.229	0.000	0.229
plotSCEBatchFeatureMean	0.384	0.001	0.386
plotSCEDensity	0.314	0.001	0.315
plotSCEDensityAssayData	0.322	0.001	0.322
plotSCEDensityColData	0.315	0.000	0.314
plotSCEDimReduceColData	0.835	0.002	0.838
plotSCEDimReduceFeatures	0.371	0.002	0.374
plotSCEHeatmap	0.438	0.002	0.441
plotSCEScatter	0.411	0.002	0.413
plotSCEViolin	0.370	0.001	0.371
plotSCEViolinAssayData	0.371	0.000	0.372
plotSCEViolinColData	0.366	0.002	0.368
plotScDblFinderResults	31.243	0.755	31.761
plotScanpyDotPlot	0.023	0.000	0.022
plotScanpyEmbedding	0.021	0.001	0.023
plotScanpyHVG	0.022	0.000	0.022
plotScanpyHeatmap	0.022	0.000	0.022
plotScanpyMarkerGenes	0.022	0.000	0.022
plotScanpyMarkerGenesDotPlot	0.021	0.001	0.023
plotScanpyMarkerGenesHeatmap	0.020	0.002	0.023
plotScanpyMarkerGenesMatrixPlot	0.021	0.000	0.022
plotScanpyMarkerGenesViolin	0.021	0.000	0.022
plotScanpyMatrixPlot	0.021	0.000	0.022
plotScanpyPCA	0.021	0.000	0.021
plotScanpyPCAGeneRanking	0.021	0.000	0.022
plotScanpyPCAVariance	0.020	0.001	0.021
plotScanpyViolin	0.022	0.000	0.021
plotScdsHybridResults	9.903	0.065	9.306
plotScrubletResults	0.022	0.000	0.022
plotSeuratElbow	0.021	0.000	0.021
plotSeuratHVG	0.021	0.000	0.021
plotSeuratJackStraw	0.021	0.000	0.021
plotSeuratReduction	0.022	0.000	0.021
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.793	0.012	4.806
plotTSCANClusterPseudo	1.313	0.002	1.315
plotTSCANDimReduceFeatures	1.338	0.004	1.342
plotTSCANPseudotimeGenes	1.632	0.004	1.636
plotTSCANPseudotimeHeatmap	1.325	0.006	1.330
plotTSCANResults	1.244	0.010	1.254
plotTSNE	0.372	0.002	0.373
plotTopHVG	0.621	0.003	0.623
plotUMAP	6.957	0.066	7.104
readSingleCellMatrix	0.004	0.001	0.005
reportCellQC	0.075	0.000	0.075
reportDropletQC	0.021	0.000	0.022
reportQCTool	0.074	0.001	0.076
retrieveSCEIndex	0.026	0.001	0.027
runBBKNN	0	0	0
runBarcodeRankDrops	0.206	0.001	0.206
runBcds	1.849	0.030	1.128
runCellQC	0.077	0.003	0.080
runClusterSummaryMetrics	0.373	0.003	0.376
runComBatSeq	0.410	0.006	0.416
runCxds	0.296	0.001	0.297
runCxdsBcdsHybrid	1.886	0.067	1.180
runDEAnalysis	0.408	0.011	0.420
runDecontX	6.959	0.033	6.992
runDimReduce	0.268	0.001	0.269
runDoubletFinder	34.073	0.206	34.283
runDropletQC	0.023	0.000	0.023
runEmptyDrops	6.253	0.008	6.261
runEnrichR	0.550	0.031	2.728
runFastMNN	1.640	0.035	1.674
runFeatureSelection	0.202	0.004	0.205
runFindMarker	1.331	0.003	1.335
runGSVA	0.644	0.009	0.653
runHarmony	0.038	0.000	0.038
runKMeans	0.249	0.000	0.250
runLimmaBC	0.073	0.000	0.074
runMNNCorrect	0.381	0.012	0.393
runModelGeneVar	0.289	0.013	0.303
runNormalization	2.407	0.098	2.506
runPerCellQC	0.312	0.000	0.312
runSCANORAMA	0.000	0.000	0.001
runSCMerge	0.004	0.000	0.004
runScDblFinder	20.113	0.500	20.295
runScanpyFindClusters	0.022	0.001	0.023
runScanpyFindHVG	0.020	0.003	0.022
runScanpyFindMarkers	0.022	0.000	0.023
runScanpyNormalizeData	0.096	0.002	0.098
runScanpyPCA	0.023	0.000	0.023
runScanpyScaleData	0.022	0.001	0.023
runScanpyTSNE	0.022	0.000	0.022
runScanpyUMAP	0.022	0.000	0.022
runScranSNN	0.295	0.003	0.298
runScrublet	0.021	0.002	0.023
runSeuratFindClusters	0.022	0.000	0.022
runSeuratFindHVG	0.442	0.010	0.452
runSeuratHeatmap	0.021	0.001	0.022
runSeuratICA	0.020	0.001	0.021
runSeuratJackStraw	0.021	0.000	0.021
runSeuratNormalizeData	0.020	0.001	0.021
runSeuratPCA	0.020	0.001	0.021
runSeuratSCTransform	28.085	0.208	28.297
runSeuratScaleData	0.023	0.001	0.023
runSeuratUMAP	0.022	0.000	0.021
runSingleR	0.036	0.000	0.037
runSoupX	0	0	0
runTSCAN	0.633	0.002	0.635
runTSCANClusterDEAnalysis	0.731	0.003	0.734
runTSCANDEG	0.742	0.009	0.752
runTSNE	0.721	0.004	0.726
runUMAP	7.665	0.124	7.867
runVAM	0.292	0.002	0.294
runZINBWaVE	0.004	0.000	0.005
sampleSummaryStats	0.169	0.001	0.171
scaterCPM	0.141	0.002	0.143
scaterPCA	0.436	0.000	0.435
scaterlogNormCounts	0.231	0.001	0.232
sce	0.022	0.000	0.022
sctkListGeneSetCollections	0.081	0.001	0.082
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0.000	0.000	0.001
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.086	0.001	0.087
setSCTKDisplayRow	0.439	0.001	0.440
singleCellTK	0.001	0.000	0.000
subDiffEx	0.320	0.002	0.322
subsetSCECols	0.081	0.001	0.082
subsetSCERows	0.261	0.000	0.262
summarizeSCE	0.064	0.001	0.066
trimCounts	0.198	0.000	0.198