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	4.5.2 (2025-10-31) -- "[Not] Part in a Rumble"	4893
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: 2.20.1

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_2.20.1.tar.gz

StartedAt: 2026-03-23 04:01:41 -0400 (Mon, 23 Mar 2026)

EndedAt: 2026-03-23 04:16:41 -0400 (Mon, 23 Mar 2026)

EllapsedTime: 899.8 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_2.20.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.2 (2025-10-31)
* 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 ‘2.20.1’
* 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
plotDoubletFinderResults 33.626  0.551  34.260
runDoubletFinder         30.651  0.816  31.469
runSeuratSCTransform     28.302  0.469  28.776
plotScDblFinderResults   27.874  0.794  25.706
runScDblFinder           16.547  0.804  14.279
importExampleData        11.349  1.269  13.058
plotBatchCorrCompare     11.719  0.593  12.502
plotDecontXResults        7.742  0.270   8.013
plotScdsHybridResults     6.242  0.956   7.276
plotCxdsResults           6.444  0.272   6.796
runDecontX                6.324  0.386   6.710
plotUMAP                  6.195  0.491   6.764
plotEmptyDropsResults     6.587  0.017   6.604
plotEmptyDropsScatter     6.552  0.019   6.570
runEmptyDrops             6.271  0.032   6.304
detectCellOutlier         5.886  0.159   6.047
runUMAP                   5.627  0.010   5.716
plotDEGViolin             5.412  0.177   5.584
plotBcdsResults           5.059  0.282   5.419
plotTSCANClusterDEG       4.769  0.318   5.087
* 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 ‘2.20.1’
** 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 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble"
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 
  0.136   0.038   0.164

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble"
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%
[04:14:34] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

[04:14:35] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

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 19 | SKIP 0 | PASS 223 ]

[ FAIL 0 | WARN 19 | SKIP 0 | PASS 223 ]
> 
> proc.time()
   user  system elapsed 
266.249  11.526 279.699

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.000	0.002	0.002
SEG	0.002	0.000	0.003
calcEffectSizes	0.178	0.005	0.183
combineSCE	0.700	0.002	0.702
computeZScore	0.232	0.009	0.242
convertSCEToSeurat	4.265	0.084	4.349
convertSeuratToSCE	0.403	0.021	0.424
dedupRowNames	0.053	0.000	0.053
detectCellOutlier	5.886	0.159	6.047
diffAbundanceFET	0.055	0.000	0.055
discreteColorPalette	0.006	0.000	0.006
distinctColors	0.003	0.000	0.002
downSampleCells	0.505	0.066	0.570
downSampleDepth	0.390	0.004	0.394
expData-ANY-character-method	0.116	0.000	0.116
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.155	0.000	0.154
expData-set	0.145	0.001	0.145
expData	0.117	0.000	0.117
expDataNames-ANY-method	0.110	0.000	0.109
expDataNames	0.110	0.008	0.118
expDeleteDataTag	0.031	0.002	0.033
expSetDataTag	0.021	0.004	0.024
expTaggedData	0.023	0.002	0.024
exportSCE	0.022	0.000	0.022
exportSCEtoAnnData	0.092	0.008	0.099
exportSCEtoFlatFile	0.093	0.006	0.099
featureIndex	0.035	0.001	0.036
generateSimulatedData	0.070	0.001	0.071
getBiomarker	0.056	0.012	0.068
getDEGTopTable	0.634	0.031	0.665
getDiffAbundanceResults	0.044	0.001	0.045
getEnrichRResult	0.461	0.074	4.310
getFindMarkerTopTable	1.48	0.10	1.58
getMSigDBTable	0.004	0.000	0.004
getPathwayResultNames	0.020	0.003	0.022
getSampleSummaryStatsTable	0.206	0.021	0.227
getSoupX	0	0	0
getTSCANResults	0.976	0.104	1.080
getTopHVG	0.828	0.043	0.872
importAnnData	0.001	0.000	0.002
importBUStools	0.141	0.009	0.152
importCellRanger	0.701	0.027	0.730
importCellRangerV2Sample	0.144	0.010	0.154
importCellRangerV3Sample	0.264	0.008	0.273
importDropEst	0.234	0.035	0.269
importExampleData	11.349	1.269	13.058
importGeneSetsFromCollection	1.741	0.112	1.852
importGeneSetsFromGMT	0.059	0.001	0.061
importGeneSetsFromList	0.114	0.003	0.118
importGeneSetsFromMSigDB	0.982	0.091	1.074
importMitoGeneSet	0.055	0.005	0.059
importOptimus	0.002	0.000	0.001
importSEQC	0.146	0.026	0.172
importSTARsolo	0.186	0.014	0.200
iterateSimulations	0.169	0.001	0.170
listSampleSummaryStatsTables	0.237	0.000	0.238
mergeSCEColData	0.329	0.012	0.341
mouseBrainSubsetSCE	0.035	0.002	0.037
msigdb_table	0.000	0.001	0.001
plotBarcodeRankDropsResults	0.857	0.021	0.878
plotBarcodeRankScatter	0.822	0.051	0.874
plotBatchCorrCompare	11.719	0.593	12.502
plotBatchVariance	0.461	0.030	0.491
plotBcdsResults	5.059	0.282	5.419
plotBubble	0.757	0.004	0.762
plotClusterAbundance	1.323	0.049	1.372
plotCxdsResults	6.444	0.272	6.796
plotDEGHeatmap	1.991	0.047	2.039
plotDEGRegression	4.171	0.061	4.227
plotDEGViolin	5.412	0.177	5.584
plotDEGVolcano	0.942	0.015	0.958
plotDecontXResults	7.742	0.270	8.013
plotDimRed	0.265	0.009	0.275
plotDoubletFinderResults	33.626	0.551	34.260
plotEmptyDropsResults	6.587	0.017	6.604
plotEmptyDropsScatter	6.552	0.019	6.570
plotFindMarkerHeatmap	3.667	0.079	3.746
plotMASTThresholdGenes	1.243	0.039	1.282
plotPCA	0.382	0.010	0.392
plotPathway	0.651	0.013	0.665
plotRunPerCellQCResults	2.982	0.055	3.037
plotSCEBarAssayData	0.264	0.007	0.271
plotSCEBarColData	0.223	0.001	0.224
plotSCEBatchFeatureMean	0.402	0.000	0.402
plotSCEDensity	0.331	0.000	0.331
plotSCEDensityAssayData	0.263	0.000	0.263
plotSCEDensityColData	0.291	0.002	0.293
plotSCEDimReduceColData	0.773	0.002	0.775
plotSCEDimReduceFeatures	0.364	0.010	0.373
plotSCEHeatmap	0.412	0.007	0.419
plotSCEScatter	0.395	0.018	0.413
plotSCEViolin	0.355	0.013	0.367
plotSCEViolinAssayData	0.364	0.002	0.366
plotSCEViolinColData	0.345	0.004	0.349
plotScDblFinderResults	27.874	0.794	25.706
plotScanpyDotPlot	0.021	0.002	0.023
plotScanpyEmbedding	0.022	0.000	0.023
plotScanpyHVG	0.022	0.000	0.022
plotScanpyHeatmap	0.021	0.000	0.022
plotScanpyMarkerGenes	0.021	0.001	0.022
plotScanpyMarkerGenesDotPlot	0.022	0.000	0.021
plotScanpyMarkerGenesHeatmap	0.021	0.000	0.021
plotScanpyMarkerGenesMatrixPlot	0.022	0.000	0.022
plotScanpyMarkerGenesViolin	0.022	0.000	0.021
plotScanpyMatrixPlot	0.020	0.002	0.021
plotScanpyPCA	0.021	0.000	0.021
plotScanpyPCAGeneRanking	0.021	0.000	0.021
plotScanpyPCAVariance	0.020	0.001	0.021
plotScanpyViolin	0.021	0.000	0.021
plotScdsHybridResults	6.242	0.956	7.276
plotScrubletResults	0.022	0.000	0.022
plotSeuratElbow	0.021	0.000	0.021
plotSeuratHVG	0.021	0.001	0.021
plotSeuratJackStraw	0.021	0.000	0.021
plotSeuratReduction	0.021	0.000	0.021
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.769	0.318	5.087
plotTSCANClusterPseudo	1.406	0.096	1.503
plotTSCANDimReduceFeatures	1.361	0.057	1.419
plotTSCANPseudotimeGenes	1.610	0.112	1.722
plotTSCANPseudotimeHeatmap	1.291	0.054	1.345
plotTSCANResults	1.233	0.035	1.268
plotTSNE	0.355	0.003	0.358
plotTopHVG	0.611	0.001	0.613
plotUMAP	6.195	0.491	6.764
readSingleCellMatrix	0.005	0.000	0.005
reportCellQC	0.076	0.002	0.078
reportDropletQC	0.023	0.008	0.031
reportQCTool	0.076	0.003	0.079
retrieveSCEIndex	0.027	0.001	0.028
runBBKNN	0	0	0
runBarcodeRankDrops	0.220	0.004	0.224
runBcds	0.077	0.003	0.080
runCellQC	0.078	0.002	0.080
runClusterSummaryMetrics	0.359	0.005	0.364
runComBatSeq	0.414	0.021	0.435
runCxds	0.346	0.034	0.380
runCxdsBcdsHybrid	0.082	0.001	0.083
runDEAnalysis	0.365	0.026	0.391
runDecontX	6.324	0.386	6.710
runDimReduce	0.277	0.002	0.279
runDoubletFinder	30.651	0.816	31.469
runDropletQC	0.023	0.000	0.023
runEmptyDrops	6.271	0.032	6.304
runEnrichR	0.528	0.121	2.948
runFastMNN	1.738	0.232	1.970
runFeatureSelection	0.222	0.019	0.242
runFindMarker	1.425	0.198	1.624
runGSVA	0.881	0.119	1.000
runHarmony	0.045	0.001	0.046
runKMeans	0.187	0.001	0.188
runLimmaBC	0.077	0.008	0.084
runMNNCorrect	0.407	0.039	0.445
runModelGeneVar	0.303	0.040	0.343
runNormalization	2.517	0.412	2.930
runPerCellQC	0.320	0.015	0.335
runSCANORAMA	0	0	0
runSCMerge	0.004	0.000	0.004
runScDblFinder	16.547	0.804	14.279
runScanpyFindClusters	0.020	0.001	0.021
runScanpyFindHVG	0.021	0.000	0.021
runScanpyFindMarkers	0.020	0.001	0.021
runScanpyNormalizeData	0.091	0.000	0.092
runScanpyPCA	0.021	0.000	0.021
runScanpyScaleData	0.021	0.000	0.021
runScanpyTSNE	0.020	0.001	0.021
runScanpyUMAP	0.020	0.000	0.021
runScranSNN	0.267	0.001	0.268
runScrublet	0.020	0.001	0.021
runSeuratFindClusters	0.020	0.001	0.021
runSeuratFindHVG	0.422	0.001	0.423
runSeuratHeatmap	0.021	0.000	0.021
runSeuratICA	0.021	0.000	0.021
runSeuratJackStraw	0.018	0.002	0.020
runSeuratNormalizeData	0.02	0.00	0.02
runSeuratPCA	0.02	0.00	0.02
runSeuratSCTransform	28.302	0.469	28.776
runSeuratScaleData	0.020	0.003	0.023
runSeuratUMAP	0.021	0.000	0.021
runSingleR	0.035	0.001	0.036
runSoupX	0	0	0
runTSCAN	0.619	0.001	0.620
runTSCANClusterDEAnalysis	0.693	0.006	0.699
runTSCANDEG	0.672	0.008	0.681
runTSNE	0.709	0.002	0.711
runUMAP	5.627	0.010	5.716
runVAM	0.276	0.001	0.277
runZINBWaVE	0.003	0.001	0.004
sampleSummaryStats	0.158	0.000	0.158
scaterCPM	0.129	0.002	0.131
scaterPCA	0.421	0.001	0.422
scaterlogNormCounts	0.223	0.006	0.229
sce	0.020	0.001	0.021
sctkListGeneSetCollections	0.077	0.000	0.077
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.080	0.000	0.081
setSCTKDisplayRow	0.403	0.002	0.406
singleCellTK	0	0	0
subDiffEx	0.334	0.003	0.337
subsetSCECols	0.074	0.001	0.075
subsetSCERows	0.198	0.000	0.198
summarizeSCE	0.062	0.000	0.062
trimCounts	0.195	0.003	0.198