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"	4903
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 nebbiolo2

To the developers/maintainers of the singleCellTK package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/singleCellTK.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results

Summary

Package: singleCellTK

Version: 2.20.0

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

StartedAt: 2025-11-15 04:28:16 -0500 (Sat, 15 Nov 2025)

EndedAt: 2025-11-15 04:45:29 -0500 (Sat, 15 Nov 2025)

EllapsedTime: 1032.9 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 45.116  0.711  45.830
plotDoubletFinderResults 38.655  0.298  39.030
runDoubletFinder         34.529  0.355  34.886
plotScDblFinderResults   30.266  0.521  30.510
runSeuratSCTransform     28.318  0.206  28.526
runScDblFinder           20.345  0.364  20.403
plotBatchCorrCompare     12.587  0.079  12.853
importExampleData        11.043  0.351  11.880
plotScdsHybridResults    10.192  0.010   9.483
plotBcdsResults           9.036  0.109   8.399
plotDecontXResults        8.685  0.058   8.743
runUMAP                   7.687  0.129   7.894
plotUMAP                  7.604  0.115   7.800
plotCxdsResults           7.480  0.077   7.637
runDecontX                7.470  0.013   7.485
plotEmptyDropsResults     6.875  0.054   6.929
plotDEGViolin             6.758  0.133   6.885
plotEmptyDropsScatter     6.594  0.027   6.620
detectCellOutlier         6.327  0.126   6.453
runEmptyDrops             6.252  0.026   6.278
* 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 
  0.156   0.034   0.177

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

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

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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 
325.102  11.455 338.098

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.001	0.001	0.003
SEG	0.002	0.001	0.003
calcEffectSizes	0.181	0.007	0.188
combineSCE	0.706	0.003	0.709
computeZScore	0.231	0.008	0.239
convertSCEToSeurat	4.396	0.070	4.467
convertSeuratToSCE	0.317	0.006	0.323
dedupRowNames	0.055	0.000	0.055
detectCellOutlier	6.327	0.126	6.453
diffAbundanceFET	0.055	0.000	0.054
discreteColorPalette	0.006	0.000	0.005
distinctColors	0.002	0.000	0.002
downSampleCells	0.461	0.073	0.534
downSampleDepth	0.387	0.006	0.394
expData-ANY-character-method	0.121	0.000	0.121
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.159	0.000	0.159
expData-set	0.156	0.017	0.172
expData	0.128	0.001	0.130
expDataNames-ANY-method	0.113	0.002	0.115
expDataNames	0.118	0.001	0.119
expDeleteDataTag	0.035	0.001	0.036
expSetDataTag	0.022	0.003	0.025
expTaggedData	0.025	0.000	0.026
exportSCE	0.023	0.000	0.023
exportSCEtoAnnData	0.090	0.011	0.101
exportSCEtoFlatFile	0.088	0.009	0.098
featureIndex	0.033	0.004	0.038
generateSimulatedData	0.052	0.003	0.055
getBiomarker	0.054	0.005	0.059
getDEGTopTable	0.659	0.037	0.697
getDiffAbundanceResults	0.047	0.000	0.047
getEnrichRResult	0.437	0.032	2.965
getFindMarkerTopTable	1.450	0.051	1.501
getMSigDBTable	0.002	0.001	0.003
getPathwayResultNames	0.021	0.002	0.023
getSampleSummaryStatsTable	0.178	0.008	0.186
getSoupX	0	0	0
getTSCANResults	1.040	0.009	1.048
getTopHVG	0.861	0.015	0.876
importAnnData	0.001	0.000	0.001
importBUStools	0.139	0.002	0.142
importCellRanger	0.703	0.019	0.723
importCellRangerV2Sample	0.132	0.000	0.132
importCellRangerV3Sample	0.255	0.004	0.258
importDropEst	0.189	0.002	0.190
importExampleData	11.043	0.351	11.880
importGeneSetsFromCollection	1.756	0.082	1.838
importGeneSetsFromGMT	0.062	0.001	0.063
importGeneSetsFromList	0.123	0.000	0.123
importGeneSetsFromMSigDB	45.116	0.711	45.830
importMitoGeneSet	0.054	0.003	0.057
importOptimus	0.001	0.000	0.002
importSEQC	0.139	0.024	0.164
importSTARsolo	0.151	0.022	0.173
iterateSimulations	0.187	0.020	0.207
listSampleSummaryStatsTables	0.251	0.026	0.277
mergeSCEColData	0.400	0.011	0.412
mouseBrainSubsetSCE	0.037	0.000	0.038
msigdb_table	0.001	0.000	0.002
plotBarcodeRankDropsResults	0.852	0.005	0.857
plotBarcodeRankScatter	0.804	0.001	0.806
plotBatchCorrCompare	12.587	0.079	12.853
plotBatchVariance	0.470	0.004	0.474
plotBcdsResults	9.036	0.109	8.399
plotBubble	0.766	0.003	0.769
plotClusterAbundance	1.284	0.000	1.284
plotCxdsResults	7.480	0.077	7.637
plotDEGHeatmap	1.984	0.047	2.031
plotDEGRegression	4.296	0.012	4.301
plotDEGViolin	6.758	0.133	6.885
plotDEGVolcano	0.921	0.024	0.945
plotDecontXResults	8.685	0.058	8.743
plotDimRed	0.284	0.000	0.283
plotDoubletFinderResults	38.655	0.298	39.030
plotEmptyDropsResults	6.875	0.054	6.929
plotEmptyDropsScatter	6.594	0.027	6.620
plotFindMarkerHeatmap	3.850	0.022	3.872
plotMASTThresholdGenes	1.275	0.006	1.282
plotPCA	0.377	0.004	0.381
plotPathway	0.733	0.003	0.737
plotRunPerCellQCResults	3.244	0.010	3.255
plotSCEBarAssayData	0.275	0.002	0.277
plotSCEBarColData	0.233	0.000	0.233
plotSCEBatchFeatureMean	0.397	0.000	0.397
plotSCEDensity	0.313	0.001	0.314
plotSCEDensityAssayData	0.304	0.001	0.305
plotSCEDensityColData	0.302	0.002	0.305
plotSCEDimReduceColData	0.776	0.002	0.779
plotSCEDimReduceFeatures	0.378	0.001	0.379
plotSCEHeatmap	0.429	0.000	0.429
plotSCEScatter	0.420	0.001	0.421
plotSCEViolin	0.361	0.005	0.366
plotSCEViolinAssayData	0.391	0.000	0.391
plotSCEViolinColData	0.386	0.002	0.388
plotScDblFinderResults	30.266	0.521	30.510
plotScanpyDotPlot	0.022	0.001	0.022
plotScanpyEmbedding	0.023	0.000	0.023
plotScanpyHVG	0.021	0.001	0.022
plotScanpyHeatmap	0.022	0.000	0.022
plotScanpyMarkerGenes	0.022	0.000	0.022
plotScanpyMarkerGenesDotPlot	0.021	0.000	0.021
plotScanpyMarkerGenesHeatmap	0.022	0.000	0.022
plotScanpyMarkerGenesMatrixPlot	0.021	0.000	0.021
plotScanpyMarkerGenesViolin	0.021	0.000	0.021
plotScanpyMatrixPlot	0.020	0.001	0.021
plotScanpyPCA	0.022	0.000	0.021
plotScanpyPCAGeneRanking	0.022	0.000	0.022
plotScanpyPCAVariance	0.021	0.000	0.021
plotScanpyViolin	0.022	0.000	0.022
plotScdsHybridResults	10.192	0.010	9.483
plotScrubletResults	0.022	0.000	0.022
plotSeuratElbow	0.021	0.001	0.023
plotSeuratHVG	0.022	0.000	0.022
plotSeuratJackStraw	0.022	0.000	0.022
plotSeuratReduction	0.022	0.000	0.022
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.902	0.003	4.905
plotTSCANClusterPseudo	1.347	0.010	1.358
plotTSCANDimReduceFeatures	1.424	0.002	1.427
plotTSCANPseudotimeGenes	1.712	0.001	1.713
plotTSCANPseudotimeHeatmap	1.430	0.007	1.437
plotTSCANResults	1.281	0.003	1.285
plotTSNE	0.396	0.000	0.396
plotTopHVG	0.621	0.001	0.623
plotUMAP	7.604	0.115	7.800
readSingleCellMatrix	0.005	0.000	0.005
reportCellQC	0.079	0.000	0.079
reportDropletQC	0.023	0.000	0.022
reportQCTool	0.082	0.000	0.081
retrieveSCEIndex	0.029	0.001	0.030
runBBKNN	0	0	0
runBarcodeRankDrops	0.227	0.000	0.227
runBcds	1.956	0.020	1.190
runCellQC	0.085	0.000	0.086
runClusterSummaryMetrics	0.394	0.004	0.398
runComBatSeq	0.442	0.003	0.445
runCxds	0.322	0.002	0.324
runCxdsBcdsHybrid	1.966	0.068	1.237
runDEAnalysis	0.436	0.001	0.438
runDecontX	7.470	0.013	7.485
runDimReduce	0.293	0.000	0.293
runDoubletFinder	34.529	0.355	34.886
runDropletQC	0.021	0.002	0.022
runEmptyDrops	6.252	0.026	6.278
runEnrichR	0.606	0.036	2.923
runFastMNN	1.774	0.075	1.849
runFeatureSelection	0.208	0.001	0.210
runFindMarker	1.507	0.002	1.509
runGSVA	0.717	0.008	0.725
runHarmony	0.041	0.006	0.048
runKMeans	0.257	0.002	0.260
runLimmaBC	0.077	0.000	0.077
runMNNCorrect	0.424	0.000	0.424
runModelGeneVar	0.305	0.001	0.307
runNormalization	2.669	0.041	2.709
runPerCellQC	0.33	0.00	0.33
runSCANORAMA	0	0	0
runSCMerge	0.004	0.000	0.004
runScDblFinder	20.345	0.364	20.403
runScanpyFindClusters	0.021	0.001	0.022
runScanpyFindHVG	0.022	0.000	0.022
runScanpyFindMarkers	0.021	0.001	0.022
runScanpyNormalizeData	0.091	0.001	0.093
runScanpyPCA	0.022	0.000	0.022
runScanpyScaleData	0.019	0.002	0.021
runScanpyTSNE	0.022	0.000	0.022
runScanpyUMAP	0.022	0.000	0.021
runScranSNN	0.283	0.000	0.283
runScrublet	0.021	0.000	0.022
runSeuratFindClusters	0.022	0.000	0.022
runSeuratFindHVG	0.450	0.001	0.450
runSeuratHeatmap	0.022	0.000	0.022
runSeuratICA	0.022	0.000	0.022
runSeuratJackStraw	0.022	0.000	0.022
runSeuratNormalizeData	0.022	0.000	0.022
runSeuratPCA	0.021	0.000	0.022
runSeuratSCTransform	28.318	0.206	28.526
runSeuratScaleData	0.022	0.000	0.023
runSeuratUMAP	0.022	0.000	0.023
runSingleR	0.037	0.000	0.038
runSoupX	0	0	0
runTSCAN	0.651	0.001	0.651
runTSCANClusterDEAnalysis	0.752	0.001	0.752
runTSCANDEG	0.752	0.014	0.766
runTSNE	0.736	0.001	0.737
runUMAP	7.687	0.129	7.894
runVAM	0.287	0.000	0.287
runZINBWaVE	0.004	0.000	0.004
sampleSummaryStats	0.152	0.001	0.153
scaterCPM	0.133	0.001	0.134
scaterPCA	0.443	0.000	0.443
scaterlogNormCounts	0.233	0.000	0.233
sce	0.022	0.000	0.021
sctkListGeneSetCollections	0.081	0.001	0.082
sctkPythonInstallConda	0.000	0.000	0.001
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.083	0.001	0.084
setSCTKDisplayRow	0.429	0.004	0.433
singleCellTK	0	0	0
subDiffEx	0.316	0.001	0.318
subsetSCECols	0.079	0.000	0.079
subsetSCERows	0.252	0.009	0.260
summarizeSCE	0.067	0.001	0.068
trimCounts	0.199	0.007	0.206