Multiple platform build/check report for BioC 3.22 - CHECK results for singleCellTK on merida1

Hostname	OS	Arch (*)	R version	Installed pkgs
nebbiolo2	Linux (Ubuntu 24.04.3 LTS)	x86_64	4.5.2 (2025-10-31) -- "[Not] Part in a Rumble"	4876
merida1	macOS 12.7.6 Monterey	x86_64	4.5.2 Patched (2025-11-05 r88990) -- "[Not] Part in a Rumble"	4656
kjohnson1	macOS 13.7.5 Ventura	arm64	4.5.2 Patched (2025-11-04 r88984) -- "[Not] Part in a Rumble"	4602
taishan	Linux (openEuler 24.03 LTS)	aarch64	4.5.0 (2025-04-11) -- "How About a Twenty-Six"	4668
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 merida1

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: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.20.0.tar.gz

StartedAt: 2025-11-25 11:12:49 -0500 (Tue, 25 Nov 2025)

EndedAt: 2025-11-25 11:44:49 -0500 (Tue, 25 Nov 2025)

EllapsedTime: 1920.3 seconds

RetCode: 0

Status: OK

CheckDir: singleCellTK.Rcheck

Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.20.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.2 Patched (2025-11-05 r88990)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* 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  6.8Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    extdata   1.5Mb
    shiny     2.9Mb
* 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 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 92.429  0.927  95.955
plotDoubletFinderResults 54.794  0.282  58.832
plotScDblFinderResults   51.932  1.104  56.484
runDoubletFinder         47.610  0.181  48.108
runScDblFinder           34.054  0.461  34.844
importExampleData        21.659  2.204  25.267
plotBatchCorrCompare     19.444  0.172  19.807
plotScdsHybridResults    15.674  0.183  16.782
plotBcdsResults          14.226  0.227  14.667
plotDecontXResults       13.938  0.107  15.555
plotDEGViolin            13.322  0.164  14.066
plotDEGRegression        12.906  0.091  14.078
plotTSCANClusterDEG      12.015  0.121  12.403
plotCxdsResults          11.907  0.095  12.855
plotEmptyDropsResults    10.409  0.053  11.541
plotEmptyDropsScatter    10.219  0.038  11.277
plotFindMarkerHeatmap     9.827  0.049  10.888
convertSCEToSeurat        9.303  0.355   9.865
runDecontX                9.508  0.075   9.892
runEmptyDrops             9.464  0.028   9.561
runUMAP                   9.242  0.093   9.423
plotUMAP                  9.231  0.075   9.365
detectCellOutlier         8.563  0.170   8.991
plotRunPerCellQCResults   7.627  0.045   8.087
runSeuratSCTransform      7.226  0.098   7.367
plotDEGHeatmap            5.256  0.053   5.648
getEnrichRResult          0.669  0.062   9.874
runEnrichR                0.654  0.047   8.680
* 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 running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  ‘/Users/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.

Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/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 4.5.2 Patched (2025-11-05 r88990) -- "[Not] Part in a Rumble"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-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.

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
All Done!
> 
> proc.time()
   user  system elapsed 
  0.351   0.112   0.432

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.2 Patched (2025-11-05 r88990) -- "[Not] Part in a Rumble"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-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(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
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
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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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

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  |======================================================================| 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

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

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

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  |======================================================================| 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 
562.896   9.821 597.910

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.004	0.005	0.009
SEG	0.004	0.005	0.010
calcEffectSizes	0.463	0.024	0.488
combineSCE	1.711	0.027	1.748
computeZScore	0.421	0.020	0.446
convertSCEToSeurat	9.303	0.355	9.865
convertSeuratToSCE	0.757	0.014	0.792
dedupRowNames	0.119	0.006	0.126
detectCellOutlier	8.563	0.170	8.991
diffAbundanceFET	0.098	0.004	0.103
discreteColorPalette	0.010	0.001	0.011
distinctColors	0.004	0.001	0.005
downSampleCells	1.073	0.119	1.213
downSampleDepth	0.908	0.060	0.989
expData-ANY-character-method	0.287	0.011	0.304
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.377	0.012	0.393
expData-set	0.345	0.007	0.355
expData	0.268	0.006	0.276
expDataNames-ANY-method	0.269	0.015	0.286
expDataNames	0.277	0.033	0.316
expDeleteDataTag	0.059	0.004	0.064
expSetDataTag	0.044	0.003	0.047
expTaggedData	0.046	0.007	0.053
exportSCE	0.038	0.005	0.044
exportSCEtoAnnData	0.119	0.011	0.131
exportSCEtoFlatFile	0.122	0.009	0.131
featureIndex	0.071	0.011	0.082
generateSimulatedData	0.093	0.012	0.105
getBiomarker	0.118	0.015	0.133
getDEGTopTable	1.594	0.124	1.730
getDiffAbundanceResults	0.089	0.004	0.096
getEnrichRResult	0.669	0.062	9.874
getFindMarkerTopTable	3.439	0.131	3.636
getMSigDBTable	0.009	0.011	0.022
getPathwayResultNames	0.039	0.005	0.046
getSampleSummaryStatsTable	0.402	0.010	0.419
getSoupX	0.001	0.001	0.002
getTSCANResults	2.185	0.084	3.322
getTopHVG	1.640	0.036	1.699
importAnnData	0.003	0.001	0.004
importBUStools	0.308	0.009	1.040
importCellRanger	1.624	0.060	3.146
importCellRangerV2Sample	0.336	0.006	0.353
importCellRangerV3Sample	0.640	0.032	0.780
importDropEst	0.472	0.009	0.491
importExampleData	21.659	2.204	25.267
importGeneSetsFromCollection	2.921	0.160	3.100
importGeneSetsFromGMT	0.125	0.009	0.135
importGeneSetsFromList	0.289	0.009	0.304
importGeneSetsFromMSigDB	92.429	0.927	95.955
importMitoGeneSet	0.113	0.012	0.125
importOptimus	0.003	0.001	0.004
importSEQC	0.331	0.023	0.355
importSTARsolo	0.364	0.037	0.402
iterateSimulations	0.390	0.042	0.432
listSampleSummaryStatsTables	0.568	0.063	0.632
mergeSCEColData	0.867	0.052	0.922
mouseBrainSubsetSCE	0.064	0.007	0.072
msigdb_table	0.002	0.004	0.007
plotBarcodeRankDropsResults	2.054	0.046	2.105
plotBarcodeRankScatter	2.094	0.019	2.131
plotBatchCorrCompare	19.444	0.172	19.807
plotBatchVariance	1.104	0.024	1.160
plotBcdsResults	14.226	0.227	14.667
plotBubble	1.941	0.020	1.983
plotClusterAbundance	3.400	0.017	3.524
plotCxdsResults	11.907	0.095	12.855
plotDEGHeatmap	5.256	0.053	5.648
plotDEGRegression	12.906	0.091	14.078
plotDEGViolin	13.322	0.164	14.066
plotDEGVolcano	2.069	0.021	2.150
plotDecontXResults	13.938	0.107	15.555
plotDimRed	0.661	0.010	0.734
plotDoubletFinderResults	54.794	0.282	58.832
plotEmptyDropsResults	10.409	0.053	11.541
plotEmptyDropsScatter	10.219	0.038	11.277
plotFindMarkerHeatmap	9.827	0.049	10.888
plotMASTThresholdGenes	3.275	0.043	3.488
plotPCA	0.915	0.014	0.972
plotPathway	1.698	0.017	1.826
plotRunPerCellQCResults	7.627	0.045	8.087
plotSCEBarAssayData	0.735	0.012	0.784
plotSCEBarColData	0.560	0.014	0.614
plotSCEBatchFeatureMean	0.998	0.008	1.049
plotSCEDensity	0.771	0.010	0.801
plotSCEDensityAssayData	0.725	0.011	0.742
plotSCEDensityColData	0.725	0.009	0.738
plotSCEDimReduceColData	1.942	0.022	2.400
plotSCEDimReduceFeatures	0.904	0.014	0.982
plotSCEHeatmap	1.016	0.009	1.028
plotSCEScatter	0.870	0.012	0.883
plotSCEViolin	0.928	0.011	0.944
plotSCEViolinAssayData	0.907	0.012	0.927
plotSCEViolinColData	0.869	0.011	0.882
plotScDblFinderResults	51.932	1.104	56.484
plotScanpyDotPlot	0.040	0.005	0.061
plotScanpyEmbedding	0.040	0.006	0.053
plotScanpyHVG	0.042	0.005	0.059
plotScanpyHeatmap	0.042	0.006	0.057
plotScanpyMarkerGenes	0.043	0.005	0.057
plotScanpyMarkerGenesDotPlot	0.042	0.006	0.053
plotScanpyMarkerGenesHeatmap	0.041	0.006	0.051
plotScanpyMarkerGenesMatrixPlot	0.047	0.008	0.063
plotScanpyMarkerGenesViolin	0.043	0.006	0.056
plotScanpyMatrixPlot	0.051	0.005	0.065
plotScanpyPCA	0.044	0.006	0.059
plotScanpyPCAGeneRanking	0.041	0.007	0.050
plotScanpyPCAVariance	0.042	0.005	0.056
plotScanpyViolin	0.039	0.005	0.052
plotScdsHybridResults	15.674	0.183	16.782
plotScrubletResults	0.041	0.004	0.045
plotSeuratElbow	0.039	0.005	0.047
plotSeuratHVG	0.039	0.005	0.045
plotSeuratJackStraw	0.040	0.006	0.047
plotSeuratReduction	0.041	0.006	0.047
plotSoupXResults	0.000	0.000	0.001
plotTSCANClusterDEG	12.015	0.121	12.403
plotTSCANClusterPseudo	3.287	0.041	3.403
plotTSCANDimReduceFeatures	3.335	0.032	3.394
plotTSCANPseudotimeGenes	4.098	0.033	4.199
plotTSCANPseudotimeHeatmap	3.036	0.029	3.079
plotTSCANResults	2.980	0.028	3.045
plotTSNE	0.895	0.018	0.934
plotTopHVG	1.446	0.026	1.477
plotUMAP	9.231	0.075	9.365
readSingleCellMatrix	0.010	0.001	0.011
reportCellQC	0.171	0.006	0.180
reportDropletQC	0.040	0.004	0.043
reportQCTool	0.175	0.008	0.182
retrieveSCEIndex	0.053	0.003	0.056
runBBKNN	0.000	0.000	0.001
runBarcodeRankDrops	0.457	0.007	0.466
runBcds	3.349	0.045	3.481
runCellQC	0.171	0.008	0.178
runClusterSummaryMetrics	0.864	0.020	0.892
runComBatSeq	0.955	0.027	0.985
runCxds	0.655	0.016	0.672
runCxdsBcdsHybrid	3.350	0.108	3.465
runDEAnalysis	0.857	0.060	0.918
runDecontX	9.508	0.075	9.892
runDimReduce	0.631	0.007	0.645
runDoubletFinder	47.610	0.181	48.108
runDropletQC	0.040	0.007	0.047
runEmptyDrops	9.464	0.028	9.561
runEnrichR	0.654	0.047	8.680
runFastMNN	4.008	0.063	4.096
runFeatureSelection	0.456	0.007	0.464
runFindMarker	3.231	0.043	3.288
runGSVA	1.524	0.054	1.608
runHarmony	0.093	0.002	0.095
runKMeans	0.483	0.019	0.503
runLimmaBC	0.184	0.002	0.191
runMNNCorrect	0.895	0.006	0.909
runModelGeneVar	0.684	0.008	0.701
runNormalization	3.480	0.059	3.589
runPerCellQC	0.719	0.014	0.736
runSCANORAMA	0.000	0.001	0.001
runSCMerge	0.007	0.001	0.009
runScDblFinder	34.054	0.461	34.844
runScanpyFindClusters	0.042	0.007	0.048
runScanpyFindHVG	0.045	0.008	0.053
runScanpyFindMarkers	0.039	0.005	0.044
runScanpyNormalizeData	0.214	0.006	0.221
runScanpyPCA	0.040	0.004	0.044
runScanpyScaleData	0.041	0.007	0.048
runScanpyTSNE	0.044	0.005	0.051
runScanpyUMAP	0.044	0.005	0.051
runScranSNN	0.669	0.017	0.713
runScrublet	0.040	0.007	0.047
runSeuratFindClusters	0.044	0.003	0.049
runSeuratFindHVG	1.082	0.020	1.113
runSeuratHeatmap	0.039	0.006	0.045
runSeuratICA	0.039	0.005	0.045
runSeuratJackStraw	0.039	0.003	0.043
runSeuratNormalizeData	0.040	0.005	0.046
runSeuratPCA	0.040	0.007	0.047
runSeuratSCTransform	7.226	0.098	7.367
runSeuratScaleData	0.041	0.006	0.053
runSeuratUMAP	0.042	0.007	0.050
runSingleR	0.086	0.005	0.091
runSoupX	0.001	0.000	0.001
runTSCAN	1.513	0.020	1.591
runTSCANClusterDEAnalysis	1.686	0.026	1.740
runTSCANDEG	1.700	0.034	1.739
runTSNE	1.311	0.014	1.333
runUMAP	9.242	0.093	9.423
runVAM	0.697	0.008	0.708
runZINBWaVE	0.007	0.001	0.009
sampleSummaryStats	0.375	0.009	0.389
scaterCPM	0.215	0.007	0.227
scaterPCA	1.058	0.015	1.092
scaterlogNormCounts	0.436	0.010	0.446
sce	0.039	0.006	0.045
sctkListGeneSetCollections	0.177	0.008	0.186
sctkPythonInstallConda	0.000	0.001	0.001
sctkPythonInstallVirtualEnv	0.001	0.001	0.001
selectSCTKConda	0.000	0.001	0.001
selectSCTKVirtualEnvironment	0.000	0.001	0.001
setRowNames	0.186	0.006	0.193
setSCTKDisplayRow	0.985	0.023	1.013
singleCellTK	0.001	0.001	0.001
subDiffEx	0.683	0.039	0.738
subsetSCECols	0.182	0.011	0.207
subsetSCERows	0.573	0.023	0.605
summarizeSCE	0.136	0.011	0.149
trimCounts	0.321	0.015	0.340