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: 2025-10-31 04:09:18 -0400 (Fri, 31 Oct 2025)

EndedAt: 2025-10-31 04:26:30 -0400 (Fri, 31 Oct 2025)

EllapsedTime: 1031.9 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 45.047  0.968  46.016
plotDoubletFinderResults 38.707  0.354  39.139
runDoubletFinder         33.803  0.154  33.958
plotScDblFinderResults   30.085  0.728  30.599
runSeuratSCTransform     27.968  0.584  28.556
runScDblFinder           19.844  1.097  20.604
importExampleData        12.477  1.120  14.025
plotBatchCorrCompare     13.374  0.134  13.692
plotScdsHybridResults     9.861  0.077   9.223
plotBcdsResults           9.511  0.128   8.930
plotDecontXResults        8.504  0.003   8.508
plotCxdsResults           7.533  0.074   7.683
runUMAP                   7.440  0.159   7.674
plotUMAP                  7.432  0.135   7.649
runDecontX                7.403  0.105   7.508
detectCellOutlier         6.731  0.141   6.873
plotDEGViolin             6.762  0.076   6.833
plotEmptyDropsResults     6.667  0.012   6.679
plotEmptyDropsScatter     6.538  0.009   6.547
runEmptyDrops             6.235  0.005   6.239
* 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 
  0.133   0.041   0.164

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%
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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...
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Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
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Performing log-normalization
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[ FAIL 0 | WARN 21 | SKIP 0 | PASS 225 ]

[ FAIL 0 | WARN 21 | SKIP 0 | PASS 225 ]
> 
> proc.time()
   user  system elapsed 
324.426   9.036 337.826

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.001	0.000	0.003
SEG	0.002	0.001	0.002
calcEffectSizes	0.191	0.004	0.195
combineSCE	0.716	0.001	0.717
computeZScore	0.235	0.009	0.244
convertSCEToSeurat	4.549	0.076	4.625
convertSeuratToSCE	0.329	0.005	0.334
dedupRowNames	0.052	0.001	0.052
detectCellOutlier	6.731	0.141	6.873
diffAbundanceFET	0.052	0.001	0.053
discreteColorPalette	0.005	0.000	0.005
distinctColors	0.002	0.000	0.002
downSampleCells	0.462	0.058	0.520
downSampleDepth	0.379	0.029	0.408
expData-ANY-character-method	0.114	0.003	0.117
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.143	0.014	0.157
expData-set	0.139	0.010	0.149
expData	0.117	0.000	0.117
expDataNames-ANY-method	0.118	0.000	0.119
expDataNames	0.108	0.003	0.111
expDeleteDataTag	0.032	0.001	0.033
expSetDataTag	0.023	0.001	0.024
expTaggedData	0.022	0.001	0.023
exportSCE	0.021	0.001	0.022
exportSCEtoAnnData	0.089	0.009	0.098
exportSCEtoFlatFile	0.092	0.005	0.098
featureIndex	0.036	0.000	0.036
generateSimulatedData	0.049	0.003	0.052
getBiomarker	0.056	0.004	0.060
getDEGTopTable	0.637	0.035	0.673
getDiffAbundanceResults	0.045	0.002	0.048
getEnrichRResult	0.562	0.077	3.474
getFindMarkerTopTable	1.471	0.097	1.568
getMSigDBTable	0.003	0.001	0.003
getPathwayResultNames	0.020	0.002	0.022
getSampleSummaryStatsTable	0.168	0.009	0.177
getSoupX	0	0	0
getTSCANResults	1.024	0.098	1.122
getTopHVG	0.815	0.064	0.879
importAnnData	0.001	0.001	0.002
importBUStools	0.143	0.001	0.145
importCellRanger	0.699	0.043	0.743
importCellRangerV2Sample	0.139	0.009	0.148
importCellRangerV3Sample	0.259	0.025	0.284
importDropEst	0.196	0.018	0.214
importExampleData	12.477	1.120	14.025
importGeneSetsFromCollection	2.067	0.119	2.186
importGeneSetsFromGMT	0.068	0.002	0.069
importGeneSetsFromList	0.129	0.002	0.131
importGeneSetsFromMSigDB	45.047	0.968	46.016
importMitoGeneSet	0.051	0.001	0.051
importOptimus	0.002	0.000	0.001
importSEQC	0.130	0.028	0.158
importSTARsolo	0.137	0.026	0.163
iterateSimulations	0.170	0.025	0.195
listSampleSummaryStatsTables	0.231	0.040	0.271
mergeSCEColData	0.349	0.009	0.359
mouseBrainSubsetSCE	0.033	0.001	0.034
msigdb_table	0.000	0.001	0.001
plotBarcodeRankDropsResults	0.844	0.005	0.849
plotBarcodeRankScatter	0.804	0.001	0.806
plotBatchCorrCompare	13.374	0.134	13.692
plotBatchVariance	0.513	0.002	0.517
plotBcdsResults	9.511	0.128	8.930
plotBubble	0.785	0.001	0.785
plotClusterAbundance	1.352	0.036	1.388
plotCxdsResults	7.533	0.074	7.683
plotDEGHeatmap	2.025	0.004	2.029
plotDEGRegression	4.308	0.060	4.362
plotDEGViolin	6.762	0.076	6.833
plotDEGVolcano	0.877	0.005	0.881
plotDecontXResults	8.504	0.003	8.508
plotDimRed	0.271	0.000	0.271
plotDoubletFinderResults	38.707	0.354	39.139
plotEmptyDropsResults	6.667	0.012	6.679
plotEmptyDropsScatter	6.538	0.009	6.547
plotFindMarkerHeatmap	3.651	0.002	3.653
plotMASTThresholdGenes	1.229	0.015	1.243
plotPCA	0.363	0.000	0.363
plotPathway	0.664	0.001	0.665
plotRunPerCellQCResults	2.962	0.001	2.964
plotSCEBarAssayData	0.314	0.000	0.315
plotSCEBarColData	0.216	0.002	0.218
plotSCEBatchFeatureMean	0.388	0.000	0.388
plotSCEDensity	0.312	0.000	0.312
plotSCEDensityAssayData	0.305	0.002	0.307
plotSCEDensityColData	0.302	0.000	0.302
plotSCEDimReduceColData	0.805	0.002	0.807
plotSCEDimReduceFeatures	0.381	0.000	0.381
plotSCEHeatmap	0.415	0.000	0.416
plotSCEScatter	0.392	0.001	0.394
plotSCEViolin	0.350	0.000	0.351
plotSCEViolinAssayData	0.369	0.000	0.370
plotSCEViolinColData	0.353	0.003	0.355
plotScDblFinderResults	30.085	0.728	30.599
plotScanpyDotPlot	0.022	0.000	0.022
plotScanpyEmbedding	0.021	0.001	0.022
plotScanpyHVG	0.019	0.001	0.020
plotScanpyHeatmap	0.021	0.000	0.021
plotScanpyMarkerGenes	0.020	0.001	0.021
plotScanpyMarkerGenesDotPlot	0.021	0.000	0.021
plotScanpyMarkerGenesHeatmap	0.020	0.001	0.021
plotScanpyMarkerGenesMatrixPlot	0.020	0.000	0.021
plotScanpyMarkerGenesViolin	0.021	0.000	0.021
plotScanpyMatrixPlot	0.021	0.000	0.021
plotScanpyPCA	0.021	0.000	0.021
plotScanpyPCAGeneRanking	0.021	0.000	0.021
plotScanpyPCAVariance	0.02	0.00	0.02
plotScanpyViolin	0.021	0.000	0.021
plotScdsHybridResults	9.861	0.077	9.223
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	0.020
plotSeuratReduction	0.021	0.000	0.020
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.715	0.006	4.721
plotTSCANClusterPseudo	1.311	0.008	1.319
plotTSCANDimReduceFeatures	1.321	0.004	1.324
plotTSCANPseudotimeGenes	1.586	0.001	1.586
plotTSCANPseudotimeHeatmap	1.239	0.004	1.243
plotTSCANResults	1.187	0.000	1.187
plotTSNE	0.367	0.001	0.369
plotTopHVG	0.609	0.003	0.612
plotUMAP	7.432	0.135	7.649
readSingleCellMatrix	0.005	0.000	0.005
reportCellQC	0.075	0.000	0.075
reportDropletQC	0.020	0.000	0.021
reportQCTool	0.077	0.000	0.077
retrieveSCEIndex	0.028	0.000	0.028
runBBKNN	0.000	0.000	0.001
runBarcodeRankDrops	0.211	0.000	0.212
runBcds	1.887	0.019	1.145
runCellQC	0.077	0.000	0.077
runClusterSummaryMetrics	0.360	0.002	0.362
runComBatSeq	0.419	0.002	0.420
runCxds	0.315	0.003	0.317
runCxdsBcdsHybrid	1.888	0.062	1.183
runDEAnalysis	0.397	0.004	0.401
runDecontX	7.403	0.105	7.508
runDimReduce	0.272	0.002	0.274
runDoubletFinder	33.803	0.154	33.958
runDropletQC	0.022	0.000	0.022
runEmptyDrops	6.235	0.005	6.239
runEnrichR	0.527	0.041	2.616
runFastMNN	1.656	0.064	1.720
runFeatureSelection	0.206	0.003	0.210
runFindMarker	1.392	0.105	1.499
runGSVA	0.695	0.077	0.772
runHarmony	0.043	0.001	0.044
runKMeans	0.265	0.029	0.294
runLimmaBC	0.088	0.006	0.094
runMNNCorrect	0.396	0.027	0.422
runModelGeneVar	0.293	0.011	0.304
runNormalization	2.503	0.444	2.946
runPerCellQC	0.313	0.005	0.317
runSCANORAMA	0	0	0
runSCMerge	0.004	0.000	0.004
runScDblFinder	19.844	1.097	20.604
runScanpyFindClusters	0.021	0.001	0.022
runScanpyFindHVG	0.020	0.001	0.021
runScanpyFindMarkers	0.021	0.000	0.021
runScanpyNormalizeData	0.090	0.001	0.091
runScanpyPCA	0.022	0.000	0.022
runScanpyScaleData	0.021	0.000	0.021
runScanpyTSNE	0.021	0.001	0.022
runScanpyUMAP	0.022	0.000	0.021
runScranSNN	0.275	0.009	0.284
runScrublet	0.021	0.000	0.022
runSeuratFindClusters	0.021	0.000	0.022
runSeuratFindHVG	0.429	0.004	0.434
runSeuratHeatmap	0.021	0.000	0.021
runSeuratICA	0.022	0.000	0.021
runSeuratJackStraw	0.021	0.000	0.021
runSeuratNormalizeData	0.021	0.000	0.021
runSeuratPCA	0.021	0.000	0.020
runSeuratSCTransform	27.968	0.584	28.556
runSeuratScaleData	0.023	0.000	0.023
runSeuratUMAP	0.022	0.000	0.022
runSingleR	0.037	0.002	0.039
runSoupX	0	0	0
runTSCAN	0.617	0.002	0.619
runTSCANClusterDEAnalysis	0.711	0.009	0.720
runTSCANDEG	0.737	0.023	0.760
runTSNE	0.729	0.001	0.730
runUMAP	7.440	0.159	7.674
runVAM	0.274	0.007	0.281
runZINBWaVE	0.004	0.000	0.004
sampleSummaryStats	0.150	0.001	0.152
scaterCPM	0.134	0.004	0.139
scaterPCA	0.419	0.003	0.422
scaterlogNormCounts	0.232	0.008	0.240
sce	0.020	0.001	0.021
sctkListGeneSetCollections	0.073	0.003	0.076
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.087	0.000	0.087
setSCTKDisplayRow	0.447	0.004	0.452
singleCellTK	0	0	0
subDiffEx	0.324	0.006	0.329
subsetSCECols	0.082	0.001	0.083
subsetSCERows	0.253	0.002	0.256
summarizeSCE	0.065	0.001	0.066
trimCounts	0.206	0.008	0.214