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: 2026-03-28 03:57:01 -0400 (Sat, 28 Mar 2026)

EndedAt: 2026-03-28 04:12:17 -0400 (Sat, 28 Mar 2026)

EllapsedTime: 915.8 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 24.04.4 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 34.268  0.341  34.608
runDoubletFinder         32.315  0.843  33.158
runSeuratSCTransform     30.316  1.687  32.009
plotScDblFinderResults   28.727  1.265  26.935
runScDblFinder           16.629  0.609  14.200
plotBatchCorrCompare     12.462  0.087  12.550
importExampleData        10.684  0.465  11.639
plotDecontXResults        7.761  0.015   7.776
plotScdsHybridResults     6.502  1.137   7.638
runUMAP                   6.963  0.303   7.267
plotUMAP                  6.584  0.418   7.003
runDecontX                6.692  0.289   6.981
plotEmptyDropsResults     6.633  0.024   6.657
plotEmptyDropsScatter     6.601  0.037   6.638
plotCxdsResults           6.496  0.104   6.600
detectCellOutlier         6.224  0.170   6.394
runEmptyDrops             6.264  0.021   6.286
plotDEGViolin             5.339  0.086   5.419
plotTSCANClusterDEG       4.914  0.292   5.206
* 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 
  0.151   0.033   0.173

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%
[04:10:09] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

[04:10:10] 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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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

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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 
277.078   7.092 285.166

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.001	0.000	0.002
SEG	0.003	0.001	0.003
calcEffectSizes	0.196	0.002	0.197
combineSCE	0.729	0.000	0.729
computeZScore	0.232	0.013	0.244
convertSCEToSeurat	4.545	0.100	4.644
convertSeuratToSCE	0.345	0.003	0.349
dedupRowNames	0.054	0.001	0.054
detectCellOutlier	6.224	0.170	6.394
diffAbundanceFET	0.053	0.000	0.053
discreteColorPalette	0.006	0.001	0.005
distinctColors	0.002	0.000	0.001
downSampleCells	0.468	0.060	0.529
downSampleDepth	0.392	0.002	0.396
expData-ANY-character-method	0.124	0.001	0.125
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.156	0.000	0.157
expData-set	0.148	0.012	0.160
expData	0.124	0.003	0.129
expDataNames-ANY-method	0.116	0.006	0.124
expDataNames	0.113	0.011	0.124
expDeleteDataTag	0.033	0.001	0.033
expSetDataTag	0.022	0.003	0.025
expTaggedData	0.022	0.001	0.025
exportSCE	0.020	0.003	0.022
exportSCEtoAnnData	0.093	0.007	0.100
exportSCEtoFlatFile	0.081	0.016	0.099
featureIndex	0.036	0.002	0.038
generateSimulatedData	0.072	0.009	0.083
getBiomarker	0.059	0.001	0.060
getDEGTopTable	0.678	0.013	0.691
getDiffAbundanceResults	0.045	0.001	0.047
getEnrichRResult	0.562	0.045	3.668
getFindMarkerTopTable	1.547	0.038	1.586
getMSigDBTable	0.002	0.001	0.004
getPathwayResultNames	0.021	0.001	0.022
getSampleSummaryStatsTable	0.196	0.002	0.198
getSoupX	0	0	0
getTSCANResults	0.938	0.011	0.949
getTopHVG	0.851	0.010	0.860
importAnnData	0.001	0.001	0.001
importBUStools	0.144	0.003	0.147
importCellRanger	0.713	0.004	0.718
importCellRangerV2Sample	0.136	0.001	0.136
importCellRangerV3Sample	0.266	0.001	0.268
importDropEst	1.289	0.063	1.352
importExampleData	10.684	0.465	11.639
importGeneSetsFromCollection	1.796	0.019	1.815
importGeneSetsFromGMT	0.063	0.001	0.064
importGeneSetsFromList	0.123	0.002	0.125
importGeneSetsFromMSigDB	0.997	0.080	1.077
importMitoGeneSet	0.058	0.003	0.061
importOptimus	0.001	0.000	0.002
importSEQC	0.158	0.018	0.177
importSTARsolo	0.201	0.009	0.210
iterateSimulations	0.187	0.003	0.189
listSampleSummaryStatsTables	0.256	0.000	0.256
mergeSCEColData	0.350	0.008	0.359
mouseBrainSubsetSCE	0.037	0.000	0.037
msigdb_table	0.000	0.001	0.002
plotBarcodeRankDropsResults	0.925	0.010	0.935
plotBarcodeRankScatter	0.831	0.002	0.833
plotBatchCorrCompare	12.462	0.087	12.550
plotBatchVariance	0.453	0.008	0.460
plotBcdsResults	4.911	0.055	4.966
plotBubble	0.801	0.036	0.837
plotClusterAbundance	1.313	0.011	1.324
plotCxdsResults	6.496	0.104	6.600
plotDEGHeatmap	2.014	0.003	2.017
plotDEGRegression	4.255	0.070	4.319
plotDEGViolin	5.339	0.086	5.419
plotDEGVolcano	0.922	0.004	0.926
plotDecontXResults	7.761	0.015	7.776
plotDimRed	0.277	0.001	0.278
plotDoubletFinderResults	34.268	0.341	34.608
plotEmptyDropsResults	6.633	0.024	6.657
plotEmptyDropsScatter	6.601	0.037	6.638
plotFindMarkerHeatmap	3.708	0.030	3.738
plotMASTThresholdGenes	1.265	0.010	1.275
plotPCA	0.359	0.002	0.361
plotPathway	0.640	0.003	0.643
plotRunPerCellQCResults	3.002	0.004	3.006
plotSCEBarAssayData	0.265	0.001	0.267
plotSCEBarColData	0.224	0.000	0.224
plotSCEBatchFeatureMean	0.395	0.002	0.397
plotSCEDensity	0.351	0.001	0.352
plotSCEDensityAssayData	0.268	0.001	0.269
plotSCEDensityColData	0.298	0.001	0.299
plotSCEDimReduceColData	0.790	0.000	0.791
plotSCEDimReduceFeatures	0.376	0.004	0.380
plotSCEHeatmap	0.444	0.000	0.443
plotSCEScatter	0.382	0.003	0.384
plotSCEViolin	0.358	0.000	0.358
plotSCEViolinAssayData	0.375	0.000	0.375
plotSCEViolinColData	0.359	0.000	0.359
plotScDblFinderResults	28.727	1.265	26.935
plotScanpyDotPlot	0.025	0.012	0.036
plotScanpyEmbedding	0.021	0.001	0.023
plotScanpyHVG	0.021	0.000	0.022
plotScanpyHeatmap	0.022	0.000	0.021
plotScanpyMarkerGenes	0.021	0.000	0.022
plotScanpyMarkerGenesDotPlot	0.020	0.001	0.022
plotScanpyMarkerGenesHeatmap	0.022	0.000	0.021
plotScanpyMarkerGenesMatrixPlot	0.021	0.000	0.021
plotScanpyMarkerGenesViolin	0.020	0.001	0.021
plotScanpyMatrixPlot	0.022	0.000	0.021
plotScanpyPCA	0.022	0.000	0.022
plotScanpyPCAGeneRanking	0.022	0.000	0.022
plotScanpyPCAVariance	0.020	0.001	0.022
plotScanpyViolin	0.020	0.001	0.021
plotScdsHybridResults	6.502	1.137	7.638
plotScrubletResults	0.022	0.001	0.022
plotSeuratElbow	0.021	0.001	0.021
plotSeuratHVG	0.020	0.001	0.021
plotSeuratJackStraw	0.021	0.000	0.022
plotSeuratReduction	0.021	0.000	0.021
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.914	0.292	5.206
plotTSCANClusterPseudo	1.449	0.056	1.504
plotTSCANDimReduceFeatures	1.371	0.080	1.451
plotTSCANPseudotimeGenes	1.600	0.094	1.694
plotTSCANPseudotimeHeatmap	1.29	0.07	1.36
plotTSCANResults	1.264	0.060	1.323
plotTSNE	0.362	0.005	0.367
plotTopHVG	0.619	0.014	0.633
plotUMAP	6.584	0.418	7.003
readSingleCellMatrix	0.005	0.000	0.004
reportCellQC	0.078	0.000	0.078
reportDropletQC	0.021	0.001	0.021
reportQCTool	0.077	0.000	0.076
retrieveSCEIndex	0.027	0.001	0.027
runBBKNN	0	0	0
runBarcodeRankDrops	0.216	0.012	0.228
runBcds	0.08	0.00	0.08
runCellQC	0.080	0.000	0.081
runClusterSummaryMetrics	0.363	0.003	0.367
runComBatSeq	0.418	0.022	0.440
runCxds	0.375	0.028	0.402
runCxdsBcdsHybrid	0.083	0.003	0.087
runDEAnalysis	0.383	0.034	0.418
runDecontX	6.692	0.289	6.981
runDimReduce	0.293	0.005	0.299
runDoubletFinder	32.315	0.843	33.158
runDropletQC	0.023	0.000	0.023
runEmptyDrops	6.264	0.021	6.286
runEnrichR	0.469	0.052	2.538
runFastMNN	1.733	0.128	1.860
runFeatureSelection	0.214	0.000	0.214
runFindMarker	1.415	0.032	1.446
runGSVA	0.855	0.029	0.885
runHarmony	0.041	0.000	0.041
runKMeans	0.185	0.001	0.187
runLimmaBC	0.075	0.008	0.084
runMNNCorrect	0.422	0.011	0.434
runModelGeneVar	0.319	0.011	0.330
runNormalization	2.492	0.231	2.724
runPerCellQC	0.341	0.001	0.343
runSCANORAMA	0	0	0
runSCMerge	0.004	0.000	0.004
runScDblFinder	16.629	0.609	14.200
runScanpyFindClusters	0.022	0.000	0.021
runScanpyFindHVG	0.022	0.000	0.021
runScanpyFindMarkers	0.022	0.000	0.022
runScanpyNormalizeData	0.096	0.004	0.100
runScanpyPCA	0.022	0.000	0.022
runScanpyScaleData	0.022	0.000	0.022
runScanpyTSNE	0.021	0.000	0.022
runScanpyUMAP	0.020	0.002	0.021
runScranSNN	0.288	0.005	0.293
runScrublet	0.022	0.000	0.021
runSeuratFindClusters	0.022	0.000	0.022
runSeuratFindHVG	0.445	0.022	0.468
runSeuratHeatmap	0.021	0.001	0.022
runSeuratICA	0.021	0.000	0.021
runSeuratJackStraw	0.021	0.000	0.021
runSeuratNormalizeData	0.020	0.000	0.021
runSeuratPCA	0.020	0.001	0.021
runSeuratSCTransform	30.316	1.687	32.009
runSeuratScaleData	0.020	0.004	0.024
runSeuratUMAP	0.022	0.001	0.023
runSingleR	0.04	0.00	0.04
runSoupX	0	0	0
runTSCAN	0.688	0.035	0.724
runTSCANClusterDEAnalysis	0.780	0.022	0.803
runTSCANDEG	0.767	0.070	0.839
runTSNE	0.759	0.007	0.766
runUMAP	6.963	0.303	7.267
runVAM	0.322	0.014	0.336
runZINBWaVE	0.004	0.000	0.004
sampleSummaryStats	0.160	0.010	0.171
scaterCPM	0.136	0.003	0.139
scaterPCA	0.447	0.005	0.453
scaterlogNormCounts	0.249	0.016	0.266
sce	0.020	0.002	0.023
sctkListGeneSetCollections	0.079	0.009	0.088
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0.001	0.000	0.000
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.090	0.002	0.092
setSCTKDisplayRow	0.458	0.012	0.470
singleCellTK	0	0	0
subDiffEx	0.344	0.022	0.367
subsetSCECols	0.081	0.000	0.081
subsetSCERows	0.209	0.008	0.218
summarizeSCE	0.071	0.009	0.080
trimCounts	0.211	0.005	0.217