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	4.5.2 (2025-10-31) -- "[Not] Part in a Rumble"	4878
merida1	macOS 12.7.6 Monterey	x86_64	4.5.2 (2025-10-31) -- "[Not] Part in a Rumble"	4624
taishan	Linux (openEuler 24.03 LTS)	aarch64	4.5.0 (2025-04-11) -- "How About a Twenty-Six"	4669
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: 2025-12-02 04:33:06 -0500 (Tue, 02 Dec 2025)

EndedAt: 2025-12-02 04:50:27 -0500 (Tue, 02 Dec 2025)

EllapsedTime: 1040.8 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_2.20.0.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 ‘2.20.0’
* 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 43.786  0.705  44.491
plotDoubletFinderResults 39.603  0.416  40.101
runDoubletFinder         33.856  0.277  34.134
plotScDblFinderResults   30.803  0.583  31.102
runSeuratSCTransform     28.812  0.195  29.008
runScDblFinder           20.480  0.370  20.545
plotBatchCorrCompare     12.963  0.077  13.218
importExampleData        11.131  0.364  11.972
plotScdsHybridResults    10.415  0.062   9.781
plotBcdsResults           9.299  0.119   8.670
plotDecontXResults        8.982  0.077   9.060
runUMAP                   7.995  0.118   8.189
runDecontX                7.806  0.116   7.922
plotUMAP                  7.455  0.137   7.670
plotCxdsResults           7.332  0.067   7.478
plotDEGViolin             7.000  0.132   7.127
plotEmptyDropsResults     6.690  0.096   6.786
plotEmptyDropsScatter     6.582  0.060   6.643
detectCellOutlier         6.190  0.148   6.339
runEmptyDrops             6.257  0.009   6.269
* 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 4.5.2 (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 
  0.150   0.037   0.176

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%
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 
336.514   5.406 343.465

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.003	0.000	0.002
SEG	0.003	0.000	0.003
calcEffectSizes	0.179	0.006	0.187
combineSCE	0.698	0.002	0.702
computeZScore	0.226	0.007	0.233
convertSCEToSeurat	4.149	0.080	4.228
convertSeuratToSCE	0.332	0.004	0.336
dedupRowNames	0.056	0.001	0.057
detectCellOutlier	6.190	0.148	6.339
diffAbundanceFET	0.055	0.001	0.056
discreteColorPalette	0.006	0.000	0.006
distinctColors	0.002	0.000	0.002
downSampleCells	0.488	0.062	0.550
downSampleDepth	0.385	0.007	0.393
expData-ANY-character-method	0.120	0.002	0.122
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.159	0.000	0.160
expData-set	0.148	0.002	0.151
expData	0.127	0.000	0.127
expDataNames-ANY-method	0.118	0.010	0.129
expDataNames	0.116	0.005	0.122
expDeleteDataTag	0.032	0.003	0.035
expSetDataTag	0.022	0.003	0.025
expTaggedData	0.024	0.002	0.025
exportSCE	0.022	0.001	0.023
exportSCEtoAnnData	0.088	0.012	0.099
exportSCEtoFlatFile	0.090	0.009	0.099
featureIndex	0.033	0.003	0.037
generateSimulatedData	0.051	0.005	0.057
getBiomarker	0.058	0.003	0.062
getDEGTopTable	0.674	0.053	0.727
getDiffAbundanceResults	0.050	0.000	0.049
getEnrichRResult	0.530	0.028	3.041
getFindMarkerTopTable	1.522	0.057	1.580
getMSigDBTable	0.003	0.001	0.004
getPathwayResultNames	0.023	0.002	0.024
getSampleSummaryStatsTable	0.191	0.010	0.200
getSoupX	0	0	0
getTSCANResults	1.071	0.018	1.089
getTopHVG	0.841	0.016	0.857
importAnnData	0.001	0.000	0.002
importBUStools	0.148	0.007	0.157
importCellRanger	0.748	0.010	0.759
importCellRangerV2Sample	0.136	0.001	0.137
importCellRangerV3Sample	0.260	0.005	0.265
importDropEst	0.188	0.001	0.189
importExampleData	11.131	0.364	11.972
importGeneSetsFromCollection	1.807	0.075	1.882
importGeneSetsFromGMT	0.061	0.001	0.062
importGeneSetsFromList	0.126	0.000	0.126
importGeneSetsFromMSigDB	43.786	0.705	44.491
importMitoGeneSet	0.051	0.003	0.054
importOptimus	0.002	0.000	0.001
importSEQC	0.140	0.016	0.155
importSTARsolo	0.151	0.019	0.170
iterateSimulations	0.166	0.031	0.197
listSampleSummaryStatsTables	0.254	0.024	0.278
mergeSCEColData	0.360	0.012	0.372
mouseBrainSubsetSCE	0.035	0.000	0.035
msigdb_table	0.001	0.000	0.002
plotBarcodeRankDropsResults	0.840	0.006	0.846
plotBarcodeRankScatter	0.856	0.000	0.857
plotBatchCorrCompare	12.963	0.077	13.218
plotBatchVariance	0.482	0.003	0.485
plotBcdsResults	9.299	0.119	8.670
plotBubble	0.751	0.001	0.752
plotClusterAbundance	1.294	0.001	1.296
plotCxdsResults	7.332	0.067	7.478
plotDEGHeatmap	2.027	0.048	2.075
plotDEGRegression	4.230	0.008	4.233
plotDEGViolin	7.000	0.132	7.127
plotDEGVolcano	0.912	0.005	0.916
plotDecontXResults	8.982	0.077	9.060
plotDimRed	0.277	0.000	0.278
plotDoubletFinderResults	39.603	0.416	40.101
plotEmptyDropsResults	6.690	0.096	6.786
plotEmptyDropsScatter	6.582	0.060	6.643
plotFindMarkerHeatmap	3.910	0.056	3.966
plotMASTThresholdGenes	1.288	0.022	1.311
plotPCA	0.397	0.008	0.406
plotPathway	0.671	0.015	0.686
plotRunPerCellQCResults	3.084	0.013	3.098
plotSCEBarAssayData	0.334	0.001	0.335
plotSCEBarColData	0.226	0.001	0.227
plotSCEBatchFeatureMean	0.403	0.001	0.404
plotSCEDensity	0.32	0.00	0.32
plotSCEDensityAssayData	0.311	0.002	0.313
plotSCEDensityColData	0.309	0.001	0.309
plotSCEDimReduceColData	0.813	0.003	0.816
plotSCEDimReduceFeatures	0.389	0.002	0.391
plotSCEHeatmap	0.453	0.001	0.454
plotSCEScatter	0.420	0.005	0.425
plotSCEViolin	0.373	0.001	0.374
plotSCEViolinAssayData	0.408	0.002	0.411
plotSCEViolinColData	0.380	0.001	0.380
plotScDblFinderResults	30.803	0.583	31.102
plotScanpyDotPlot	0.022	0.000	0.022
plotScanpyEmbedding	0.022	0.000	0.022
plotScanpyHVG	0.021	0.000	0.020
plotScanpyHeatmap	0.020	0.001	0.022
plotScanpyMarkerGenes	0.022	0.000	0.021
plotScanpyMarkerGenesDotPlot	0.022	0.000	0.021
plotScanpyMarkerGenesHeatmap	0.022	0.000	0.022
plotScanpyMarkerGenesMatrixPlot	0.021	0.001	0.022
plotScanpyMarkerGenesViolin	0.021	0.000	0.022
plotScanpyMatrixPlot	0.021	0.000	0.021
plotScanpyPCA	0.022	0.000	0.022
plotScanpyPCAGeneRanking	0.022	0.001	0.023
plotScanpyPCAVariance	0.021	0.000	0.022
plotScanpyViolin	0.021	0.000	0.021
plotScdsHybridResults	10.415	0.062	9.781
plotScrubletResults	0.022	0.000	0.023
plotSeuratElbow	0.021	0.000	0.021
plotSeuratHVG	0.021	0.000	0.021
plotSeuratJackStraw	0.021	0.000	0.021
plotSeuratReduction	0.021	0.000	0.021
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.881	0.013	4.895
plotTSCANClusterPseudo	1.335	0.006	1.342
plotTSCANDimReduceFeatures	1.387	0.004	1.391
plotTSCANPseudotimeGenes	1.658	0.003	1.662
plotTSCANPseudotimeHeatmap	1.295	0.005	1.300
plotTSCANResults	1.243	0.003	1.246
plotTSNE	0.367	0.002	0.368
plotTopHVG	0.615	0.002	0.616
plotUMAP	7.455	0.137	7.670
readSingleCellMatrix	0.004	0.002	0.006
reportCellQC	0.080	0.000	0.081
reportDropletQC	0.022	0.001	0.022
reportQCTool	0.082	0.000	0.082
retrieveSCEIndex	0.031	0.000	0.031
runBBKNN	0	0	0
runBarcodeRankDrops	0.230	0.000	0.231
runBcds	1.942	0.032	1.196
runCellQC	0.083	0.001	0.084
runClusterSummaryMetrics	0.374	0.012	0.386
runComBatSeq	0.418	0.005	0.424
runCxds	0.315	0.001	0.316
runCxdsBcdsHybrid	2.018	0.071	1.259
runDEAnalysis	0.443	0.009	0.452
runDecontX	7.806	0.116	7.922
runDimReduce	0.275	0.001	0.276
runDoubletFinder	33.856	0.277	34.134
runDropletQC	0.023	0.000	0.023
runEmptyDrops	6.257	0.009	6.269
runEnrichR	0.598	0.044	2.993
runFastMNN	1.698	0.037	1.735
runFeatureSelection	0.221	0.000	0.221
runFindMarker	1.480	0.015	1.494
runGSVA	0.809	0.026	0.835
runHarmony	0.135	0.006	0.141
runKMeans	0.186	0.003	0.190
runLimmaBC	0.085	0.001	0.086
runMNNCorrect	0.435	0.008	0.443
runModelGeneVar	0.303	0.002	0.305
runNormalization	2.686	0.048	2.733
runPerCellQC	0.327	0.000	0.327
runSCANORAMA	0	0	0
runSCMerge	0.003	0.001	0.004
runScDblFinder	20.480	0.370	20.545
runScanpyFindClusters	0.021	0.002	0.024
runScanpyFindHVG	0.022	0.000	0.021
runScanpyFindMarkers	0.021	0.001	0.022
runScanpyNormalizeData	0.096	0.000	0.095
runScanpyPCA	0.023	0.000	0.023
runScanpyScaleData	0.022	0.000	0.022
runScanpyTSNE	0.020	0.002	0.022
runScanpyUMAP	0.022	0.000	0.022
runScranSNN	0.283	0.003	0.286
runScrublet	0.020	0.002	0.022
runSeuratFindClusters	0.021	0.001	0.022
runSeuratFindHVG	0.458	0.001	0.458
runSeuratHeatmap	0.022	0.001	0.023
runSeuratICA	0.020	0.001	0.022
runSeuratJackStraw	0.022	0.000	0.023
runSeuratNormalizeData	0.022	0.000	0.022
runSeuratPCA	0.021	0.000	0.022
runSeuratSCTransform	28.812	0.195	29.008
runSeuratScaleData	0.022	0.002	0.024
runSeuratUMAP	0.023	0.000	0.022
runSingleR	0.037	0.001	0.038
runSoupX	0.000	0.000	0.001
runTSCAN	0.659	0.014	0.673
runTSCANClusterDEAnalysis	0.768	0.008	0.776
runTSCANDEG	0.764	0.017	0.782
runTSNE	0.721	0.002	0.722
runUMAP	7.995	0.118	8.189
runVAM	0.318	0.001	0.319
runZINBWaVE	0.004	0.001	0.005
sampleSummaryStats	0.165	0.000	0.165
scaterCPM	0.132	0.002	0.134
scaterPCA	0.459	0.012	0.471
scaterlogNormCounts	0.232	0.003	0.235
sce	0.021	0.002	0.022
sctkListGeneSetCollections	0.080	0.001	0.081
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0.000	0.000	0.001
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.086	0.001	0.086
setSCTKDisplayRow	0.443	0.012	0.454
singleCellTK	0	0	0
subDiffEx	0.334	0.005	0.338
subsetSCECols	0.080	0.002	0.082
subsetSCERows	0.260	0.008	0.268
summarizeSCE	0.068	0.000	0.068
trimCounts	0.201	0.000	0.201