Multiple platform build/check report for BioC 3.21 - CHECK results for singleCellTK on nebbiolo1

Hostname	OS	Arch (*)	R version	Installed pkgs
nebbiolo1	Linux (Ubuntu 24.04.3 LTS)	x86_64	4.5.1 (2025-06-13) -- "Great Square Root"	4824
merida1	macOS 12.7.5 Monterey	x86_64	4.5.1 RC (2025-06-05 r88288) -- "Great Square Root"	4604
kjohnson1	macOS 13.6.6 Ventura	arm64	4.5.1 Patched (2025-06-14 r88325) -- "Great Square Root"	4545
kunpeng2	Linux (openEuler 24.03 LTS)	aarch64	R Under development (unstable) (2025-02-19 r87757) -- "Unsuffered Consequences"	4579
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 nebbiolo1

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: 2.18.1

Command: /home/biocbuild/bbs-3.21-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.21-bioc/R/site-library --timings singleCellTK_2.18.1.tar.gz

StartedAt: 2025-08-21 02:53:26 -0400 (Thu, 21 Aug 2025)

EndedAt: 2025-08-21 03:09:59 -0400 (Thu, 21 Aug 2025)

EllapsedTime: 993.1 seconds

RetCode: 0

Status: OK

CheckDir: singleCellTK.Rcheck

Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.21-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.21-bioc/R/site-library --timings singleCellTK_2.18.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.21-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.1 (2025-06-13)
* 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 ‘2.18.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 79 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  5.6Mb
  sub-directories of 1Mb or more:
    shiny   2.3Mb
* 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
  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 45.625  0.769  46.397
plotDoubletFinderResults 39.548  0.362  39.996
runDoubletFinder         33.559  0.213  33.791
plotScDblFinderResults   29.654  0.702  30.436
runSeuratSCTransform     27.853  0.249  28.104
runScDblFinder           19.896  0.416  20.314
plotBatchCorrCompare     12.463  0.167  12.812
importExampleData        11.847  0.585  12.775
plotScdsHybridResults     9.733  0.069   9.108
plotBcdsResults           8.421  0.139   7.819
plotDecontXResults        7.944  0.076   8.020
plotUMAP                  7.537  0.071   7.690
runDecontX                7.233  0.037   7.272
runUMAP                   6.848  0.125   7.053
plotCxdsResults           6.629  0.056   6.763
plotEmptyDropsResults     6.513  0.012   6.525
plotEmptyDropsScatter     6.449  0.009   6.456
runEmptyDrops             6.247  0.012   6.259
detectCellOutlier         4.917  0.141   5.059
* 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-3.21-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.

Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.21-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.21-bioc/R/site-library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.18.1’
** 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 4.5.1 (2025-06-13) -- "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 
  0.143   0.042   0.173

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.1 (2025-06-13) -- "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: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

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

    rowMedians

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

    anyMissing, rowMedians

Loading required package: 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%

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

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]

[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]
> 
> proc.time()
   user  system elapsed 
304.917  10.295 319.649

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.003	0.001	0.004
SEG	0.001	0.001	0.002
calcEffectSizes	0.181	0.015	0.196
combineSCE	0.738	0.005	0.742
computeZScore	0.232	0.010	0.243
convertSCEToSeurat	4.106	0.125	4.232
convertSeuratToSCE	0.338	0.005	0.342
dedupRowNames	0.050	0.001	0.052
detectCellOutlier	4.917	0.141	5.059
diffAbundanceFET	0.055	0.002	0.057
discreteColorPalette	0.006	0.001	0.007
distinctColors	0.001	0.001	0.003
downSampleCells	0.495	0.044	0.539
downSampleDepth	0.391	0.005	0.396
expData-ANY-character-method	0.117	0.000	0.117
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.152	0.005	0.158
expData-set	0.141	0.001	0.142
expData	0.118	0.001	0.118
expDataNames-ANY-method	0.107	0.003	0.110
expDataNames	0.109	0.000	0.109
expDeleteDataTag	0.032	0.001	0.034
expSetDataTag	0.023	0.001	0.025
expTaggedData	0.024	0.001	0.026
exportSCE	0.022	0.000	0.022
exportSCEtoAnnData	0.092	0.006	0.098
exportSCEtoFlatFile	0.089	0.008	0.097
featureIndex	0.036	0.001	0.037
generateSimulatedData	0.052	0.002	0.053
getBiomarker	0.058	0.001	0.060
getDEGTopTable	0.698	0.077	0.776
getDiffAbundanceResults	0.049	0.001	0.050
getEnrichRResult	0.410	0.025	2.806
getFindMarkerTopTable	1.540	0.030	1.571
getMSigDBTable	0.002	0.002	0.004
getPathwayResultNames	0.023	0.000	0.024
getSampleSummaryStatsTable	0.223	0.000	0.223
getSoupX	0	0	0
getTSCANResults	1.057	0.025	1.082
getTopHVG	0.828	0.005	0.834
importAnnData	0.002	0.000	0.001
importBUStools	0.148	0.001	0.149
importCellRanger	0.706	0.015	0.723
importCellRangerV2Sample	0.136	0.002	0.138
importCellRangerV3Sample	0.285	0.004	0.289
importDropEst	0.191	0.001	0.193
importExampleData	11.847	0.585	12.775
importGeneSetsFromCollection	0.673	0.032	0.705
importGeneSetsFromGMT	0.061	0.003	0.064
importGeneSetsFromList	0.118	0.005	0.123
importGeneSetsFromMSigDB	45.625	0.769	46.397
importMitoGeneSet	0.050	0.005	0.055
importOptimus	0.002	0.000	0.002
importSEQC	0.146	0.009	0.155
importSTARsolo	0.146	0.026	0.172
iterateSimulations	0.182	0.023	0.205
listSampleSummaryStatsTables	0.260	0.030	0.289
mergeSCEColData	0.362	0.033	0.396
mouseBrainSubsetSCE	0.036	0.000	0.036
msigdb_table	0.000	0.001	0.001
plotBarcodeRankDropsResults	0.576	0.009	0.585
plotBarcodeRankScatter	0.641	0.011	0.652
plotBatchCorrCompare	12.463	0.167	12.812
plotBatchVariance	0.309	0.002	0.312
plotBcdsResults	8.421	0.139	7.819
plotBubble	0.714	0.005	0.719
plotClusterAbundance	0.808	0.000	0.807
plotCxdsResults	6.629	0.056	6.763
plotDEGHeatmap	2.106	0.060	2.166
plotDEGRegression	3.207	0.023	3.225
plotDEGViolin	4.133	0.080	4.208
plotDEGVolcano	0.801	0.002	0.804
plotDecontXResults	7.944	0.076	8.020
plotDimRed	0.194	0.003	0.196
plotDoubletFinderResults	39.548	0.362	39.996
plotEmptyDropsResults	6.513	0.012	6.525
plotEmptyDropsScatter	6.449	0.009	6.456
plotFindMarkerHeatmap	3.672	0.013	3.687
plotMASTThresholdGenes	1.232	0.011	1.242
plotPCA	0.296	0.003	0.299
plotPathway	0.560	0.002	0.562
plotRunPerCellQCResults	1.916	0.004	1.919
plotSCEBarAssayData	0.187	0.000	0.187
plotSCEBarColData	0.144	0.001	0.146
plotSCEBatchFeatureMean	0.218	0.001	0.220
plotSCEDensity	0.221	0.002	0.223
plotSCEDensityAssayData	0.208	0.001	0.210
plotSCEDensityColData	0.203	0.002	0.204
plotSCEDimReduceColData	0.495	0.002	0.496
plotSCEDimReduceFeatures	0.252	0.000	0.252
plotSCEHeatmap	0.449	0.000	0.449
plotSCEScatter	0.238	0.002	0.238
plotSCEViolin	0.261	0.001	0.262
plotSCEViolinAssayData	0.257	0.001	0.257
plotSCEViolinColData	0.248	0.002	0.251
plotScDblFinderResults	29.654	0.702	30.436
plotScanpyDotPlot	0.022	0.002	0.025
plotScanpyEmbedding	0.024	0.000	0.023
plotScanpyHVG	0.022	0.001	0.023
plotScanpyHeatmap	0.023	0.000	0.022
plotScanpyMarkerGenes	0.020	0.002	0.023
plotScanpyMarkerGenesDotPlot	0.021	0.002	0.024
plotScanpyMarkerGenesHeatmap	0.022	0.001	0.023
plotScanpyMarkerGenesMatrixPlot	0.023	0.000	0.022
plotScanpyMarkerGenesViolin	0.022	0.000	0.022
plotScanpyMatrixPlot	0.021	0.001	0.023
plotScanpyPCA	0.023	0.000	0.023
plotScanpyPCAGeneRanking	0.021	0.001	0.023
plotScanpyPCAVariance	0.023	0.001	0.025
plotScanpyViolin	0.020	0.003	0.024
plotScdsHybridResults	9.733	0.069	9.108
plotScrubletResults	0.022	0.001	0.023
plotSeuratElbow	0.023	0.001	0.025
plotSeuratHVG	0.021	0.001	0.022
plotSeuratJackStraw	0.022	0.001	0.023
plotSeuratReduction	0.023	0.000	0.022
plotSoupXResults	0	0	0
plotTSCANClusterDEG	3.675	0.020	3.695
plotTSCANClusterPseudo	1.236	0.011	1.247
plotTSCANDimReduceFeatures	1.222	0.007	1.229
plotTSCANPseudotimeGenes	1.373	0.005	1.379
plotTSCANPseudotimeHeatmap	1.288	0.003	1.291
plotTSCANResults	1.136	0.006	1.142
plotTSNE	0.290	0.001	0.292
plotTopHVG	0.497	0.005	0.503
plotUMAP	7.537	0.071	7.690
readSingleCellMatrix	0.005	0.001	0.006
reportCellQC	0.085	0.001	0.086
reportDropletQC	0.022	0.001	0.022
reportQCTool	0.083	0.002	0.086
retrieveSCEIndex	0.029	0.001	0.030
runBBKNN	0	0	0
runBarcodeRankDrops	0.232	0.002	0.234
runBcds	1.924	0.030	1.184
runCellQC	0.079	0.001	0.080
runClusterSummaryMetrics	0.377	0.004	0.381
runComBatSeq	0.432	0.006	0.438
runCxds	0.307	0.002	0.308
runCxdsBcdsHybrid	2.050	0.069	1.277
runDEAnalysis	0.427	0.011	0.438
runDecontX	7.233	0.037	7.272
runDimReduce	0.281	0.001	0.283
runDoubletFinder	33.559	0.213	33.791
runDropletQC	0.022	0.001	0.024
runEmptyDrops	6.247	0.012	6.259
runEnrichR	0.500	0.013	2.496
runFastMNN	1.629	0.008	1.638
runFeatureSelection	0.211	0.000	0.212
runFindMarker	1.360	0.016	1.376
runGSVA	0.661	0.015	0.676
runHarmony	0.237	0.000	0.236
runKMeans	0.171	0.002	0.173
runLimmaBC	0.071	0.000	0.072
runMNNCorrect	0.388	0.001	0.389
runModelGeneVar	0.296	0.001	0.298
runNormalization	2.354	0.113	2.466
runPerCellQC	0.326	0.002	0.329
runSCANORAMA	0.000	0.000	0.001
runSCMerge	0.004	0.000	0.005
runScDblFinder	19.896	0.416	20.314
runScanpyFindClusters	0.019	0.002	0.022
runScanpyFindHVG	0.021	0.001	0.022
runScanpyFindMarkers	0.020	0.001	0.021
runScanpyNormalizeData	0.092	0.001	0.092
runScanpyPCA	0.021	0.001	0.022
runScanpyScaleData	0.022	0.000	0.022
runScanpyTSNE	0.020	0.003	0.022
runScanpyUMAP	0.021	0.001	0.022
runScranSNN	0.267	0.001	0.267
runScrublet	0.022	0.000	0.022
runSeuratFindClusters	0.021	0.001	0.021
runSeuratFindHVG	0.431	0.009	0.440
runSeuratHeatmap	0.022	0.000	0.022
runSeuratICA	0.022	0.000	0.022
runSeuratJackStraw	0.021	0.000	0.021
runSeuratNormalizeData	0.022	0.000	0.022
runSeuratPCA	0.021	0.000	0.021
runSeuratSCTransform	27.853	0.249	28.104
runSeuratScaleData	0.024	0.000	0.024
runSeuratUMAP	0.022	0.001	0.023
runSingleR	0.036	0.000	0.036
runSoupX	0	0	0
runTSCAN	0.656	0.003	0.659
runTSCANClusterDEAnalysis	0.748	0.003	0.751
runTSCANDEG	0.725	0.003	0.729
runTSNE	0.699	0.001	0.700
runUMAP	6.848	0.125	7.053
runVAM	0.289	0.001	0.290
runZINBWaVE	0.004	0.000	0.005
sampleSummaryStats	0.153	0.001	0.155
scaterCPM	0.126	0.010	0.137
scaterPCA	0.435	0.006	0.441
scaterlogNormCounts	0.233	0.003	0.236
sce	0.023	0.000	0.022
sctkListGeneSetCollections	0.075	0.001	0.076
sctkPythonInstallConda	0.000	0.000	0.001
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.080	0.001	0.082
setSCTKDisplayRow	0.299	0.001	0.300
singleCellTK	0	0	0
subDiffEx	0.316	0.004	0.319
subsetSCECols	0.081	0.000	0.081
subsetSCERows	0.216	0.003	0.219
summarizeSCE	0.066	0.002	0.067
trimCounts	0.200	0.005	0.205