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"	4896
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: 2026-04-01 04:06:17 -0400 (Wed, 01 Apr 2026)

EndedAt: 2026-04-01 04:21:22 -0400 (Wed, 01 Apr 2026)

EllapsedTime: 904.8 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 33.500  0.251  33.752
runDoubletFinder         31.853  0.783  32.635
runSeuratSCTransform     29.844  1.772  31.621
plotScDblFinderResults   28.954  0.902  26.879
runScDblFinder           16.682  0.693  14.294
plotBatchCorrCompare     11.909  0.093  12.001
importExampleData        10.522  0.357  11.345
plotDecontXResults        8.180  0.014   8.194
plotScdsHybridResults     6.744  1.156   7.899
runDecontX                7.128  0.306   7.433
detectCellOutlier         6.814  0.179   6.993
plotCxdsResults           6.873  0.064   6.936
plotUMAP                  6.341  0.535   6.876
runUMAP                   6.583  0.284   6.866
plotEmptyDropsScatter     6.586  0.047   6.633
plotEmptyDropsResults     6.603  0.029   6.632
runEmptyDrops             6.277  0.032   6.309
plotBcdsResults           5.919  0.126   6.045
plotTSCANClusterDEG       5.072  0.317   5.390
plotDEGViolin             5.209  0.088   5.292
* 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 
  0.149   0.033   0.169

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%
[04:19:16] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

[04:19:17] 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 
275.968   3.909 280.934

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.003	0.000	0.002
SEG	0.001	0.001	0.002
calcEffectSizes	0.188	0.002	0.189
combineSCE	0.716	0.000	0.715
computeZScore	0.232	0.009	0.241
convertSCEToSeurat	4.499	0.132	4.631
convertSeuratToSCE	0.335	0.006	0.341
dedupRowNames	0.055	0.001	0.055
detectCellOutlier	6.814	0.179	6.993
diffAbundanceFET	0.055	0.001	0.055
discreteColorPalette	0.006	0.000	0.005
distinctColors	0.002	0.000	0.002
downSampleCells	0.496	0.056	0.553
downSampleDepth	0.422	0.043	0.465
expData-ANY-character-method	0.124	0.001	0.125
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.170	0.001	0.171
expData-set	0.161	0.003	0.163
expData	0.123	0.002	0.124
expDataNames-ANY-method	0.117	0.003	0.119
expDataNames	0.116	0.007	0.124
expDeleteDataTag	0.032	0.001	0.033
expSetDataTag	0.022	0.002	0.024
expTaggedData	0.024	0.000	0.025
exportSCE	0.021	0.001	0.023
exportSCEtoAnnData	0.090	0.009	0.099
exportSCEtoFlatFile	0.090	0.008	0.097
featureIndex	0.037	0.002	0.039
generateSimulatedData	0.071	0.004	0.075
getBiomarker	0.056	0.002	0.059
getDEGTopTable	0.651	0.005	0.656
getDiffAbundanceResults	0.050	0.000	0.049
getEnrichRResult	0.552	0.037	3.610
getFindMarkerTopTable	1.608	0.047	1.655
getMSigDBTable	0.002	0.001	0.004
getPathwayResultNames	0.022	0.000	0.023
getSampleSummaryStatsTable	0.201	0.001	0.202
getSoupX	0	0	0
getTSCANResults	0.954	0.003	0.958
getTopHVG	0.921	0.009	0.931
importAnnData	0.000	0.001	0.001
importBUStools	0.151	0.002	0.153
importCellRanger	0.728	0.009	0.738
importCellRangerV2Sample	0.138	0.002	0.140
importCellRangerV3Sample	0.283	0.017	0.301
importDropEst	1.372	0.050	1.422
importExampleData	10.522	0.357	11.345
importGeneSetsFromCollection	1.948	0.025	1.973
importGeneSetsFromGMT	0.061	0.001	0.063
importGeneSetsFromList	0.125	0.001	0.126
importGeneSetsFromMSigDB	1.029	0.085	1.114
importMitoGeneSet	0.051	0.007	0.059
importOptimus	0.001	0.000	0.002
importSEQC	0.141	0.022	0.164
importSTARsolo	0.183	0.006	0.189
iterateSimulations	0.170	0.001	0.170
listSampleSummaryStatsTables	0.242	0.000	0.242
mergeSCEColData	0.325	0.005	0.330
mouseBrainSubsetSCE	0.035	0.000	0.035
msigdb_table	0.000	0.001	0.001
plotBarcodeRankDropsResults	0.871	0.012	0.883
plotBarcodeRankScatter	0.776	0.004	0.780
plotBatchCorrCompare	11.909	0.093	12.001
plotBatchVariance	0.494	0.004	0.497
plotBcdsResults	5.919	0.126	6.045
plotBubble	0.809	0.000	0.809
plotClusterAbundance	1.364	0.005	1.369
plotCxdsResults	6.873	0.064	6.936
plotDEGHeatmap	2.026	0.007	2.034
plotDEGRegression	4.236	0.069	4.299
plotDEGViolin	5.209	0.088	5.292
plotDEGVolcano	0.929	0.006	0.935
plotDecontXResults	8.180	0.014	8.194
plotDimRed	0.271	0.001	0.273
plotDoubletFinderResults	33.500	0.251	33.752
plotEmptyDropsResults	6.603	0.029	6.632
plotEmptyDropsScatter	6.586	0.047	6.633
plotFindMarkerHeatmap	3.633	0.032	3.665
plotMASTThresholdGenes	1.204	0.007	1.211
plotPCA	0.355	0.004	0.358
plotPathway	0.631	0.004	0.635
plotRunPerCellQCResults	3.012	0.001	3.013
plotSCEBarAssayData	0.267	0.001	0.268
plotSCEBarColData	0.223	0.000	0.223
plotSCEBatchFeatureMean	0.384	0.000	0.383
plotSCEDensity	0.326	0.003	0.329
plotSCEDensityAssayData	0.26	0.00	0.26
plotSCEDensityColData	0.289	0.001	0.290
plotSCEDimReduceColData	0.745	0.003	0.748
plotSCEDimReduceFeatures	0.370	0.001	0.371
plotSCEHeatmap	0.410	0.000	0.409
plotSCEScatter	0.371	0.001	0.372
plotSCEViolin	0.341	0.002	0.343
plotSCEViolinAssayData	0.372	0.002	0.374
plotSCEViolinColData	0.349	0.001	0.350
plotScDblFinderResults	28.954	0.902	26.879
plotScanpyDotPlot	0.023	0.000	0.023
plotScanpyEmbedding	0.022	0.000	0.022
plotScanpyHVG	0.021	0.001	0.022
plotScanpyHeatmap	0.021	0.001	0.022
plotScanpyMarkerGenes	0.022	0.000	0.022
plotScanpyMarkerGenesDotPlot	0.022	0.000	0.021
plotScanpyMarkerGenesHeatmap	0.024	0.009	0.032
plotScanpyMarkerGenesMatrixPlot	0.020	0.003	0.024
plotScanpyMarkerGenesViolin	0.022	0.001	0.023
plotScanpyMatrixPlot	0.022	0.000	0.022
plotScanpyPCA	0.023	0.000	0.022
plotScanpyPCAGeneRanking	0.022	0.000	0.022
plotScanpyPCAVariance	0.020	0.002	0.022
plotScanpyViolin	0.022	0.001	0.023
plotScdsHybridResults	6.744	1.156	7.899
plotScrubletResults	0.025	0.000	0.025
plotSeuratElbow	0.024	0.001	0.024
plotSeuratHVG	0.023	0.001	0.024
plotSeuratJackStraw	0.024	0.000	0.023
plotSeuratReduction	0.023	0.000	0.024
plotSoupXResults	0.000	0.000	0.001
plotTSCANClusterDEG	5.072	0.317	5.390
plotTSCANClusterPseudo	1.500	0.077	1.576
plotTSCANDimReduceFeatures	1.437	0.075	1.512
plotTSCANPseudotimeGenes	1.759	0.132	1.891
plotTSCANPseudotimeHeatmap	1.424	0.065	1.490
plotTSCANResults	1.273	0.057	1.330
plotTSNE	0.379	0.025	0.403
plotTopHVG	0.646	0.038	0.684
plotUMAP	6.341	0.535	6.876
readSingleCellMatrix	0.006	0.000	0.006
reportCellQC	0.077	0.003	0.081
reportDropletQC	0.020	0.001	0.021
reportQCTool	0.075	0.004	0.079
retrieveSCEIndex	0.025	0.002	0.028
runBBKNN	0	0	0
runBarcodeRankDrops	0.220	0.014	0.234
runBcds	0.080	0.007	0.087
runCellQC	0.078	0.002	0.080
runClusterSummaryMetrics	0.360	0.013	0.373
runComBatSeq	0.426	0.019	0.445
runCxds	0.352	0.023	0.375
runCxdsBcdsHybrid	0.082	0.006	0.088
runDEAnalysis	0.371	0.024	0.396
runDecontX	7.128	0.306	7.433
runDimReduce	0.285	0.026	0.311
runDoubletFinder	31.853	0.783	32.635
runDropletQC	0.022	0.001	0.023
runEmptyDrops	6.277	0.032	6.309
runEnrichR	0.457	0.036	2.490
runFastMNN	1.699	0.102	1.802
runFeatureSelection	0.208	0.003	0.211
runFindMarker	1.425	0.011	1.437
runGSVA	0.839	0.026	0.866
runHarmony	0.040	0.001	0.041
runKMeans	0.168	0.005	0.172
runLimmaBC	0.077	0.000	0.076
runMNNCorrect	0.406	0.015	0.421
runModelGeneVar	0.314	0.011	0.326
runNormalization	2.253	0.222	2.475
runPerCellQC	0.343	0.000	0.342
runSCANORAMA	0	0	0
runSCMerge	0.004	0.000	0.004
runScDblFinder	16.682	0.693	14.294
runScanpyFindClusters	0.022	0.001	0.022
runScanpyFindHVG	0.023	0.000	0.022
runScanpyFindMarkers	0.022	0.000	0.022
runScanpyNormalizeData	0.100	0.000	0.099
runScanpyPCA	0.023	0.000	0.023
runScanpyScaleData	0.021	0.001	0.023
runScanpyTSNE	0.021	0.000	0.022
runScanpyUMAP	0.021	0.000	0.022
runScranSNN	0.282	0.000	0.282
runScrublet	0.021	0.000	0.039
runSeuratFindClusters	0.022	0.000	0.022
runSeuratFindHVG	0.454	0.001	0.455
runSeuratHeatmap	0.023	0.000	0.023
runSeuratICA	0.022	0.000	0.022
runSeuratJackStraw	0.022	0.000	0.022
runSeuratNormalizeData	0.022	0.000	0.023
runSeuratPCA	0.021	0.001	0.023
runSeuratSCTransform	29.844	1.772	31.621
runSeuratScaleData	0.020	0.003	0.024
runSeuratUMAP	0.022	0.000	0.022
runSingleR	0.039	0.001	0.040
runSoupX	0	0	0
runTSCAN	0.688	0.036	0.723
runTSCANClusterDEAnalysis	0.769	0.011	0.780
runTSCANDEG	0.757	0.067	0.824
runTSNE	0.742	0.012	0.753
runUMAP	6.583	0.284	6.866
runVAM	0.302	0.015	0.317
runZINBWaVE	0.004	0.001	0.004
sampleSummaryStats	0.158	0.007	0.165
scaterCPM	0.129	0.005	0.134
scaterPCA	0.425	0.004	0.429
scaterlogNormCounts	0.247	0.018	0.265
sce	0.021	0.002	0.023
sctkListGeneSetCollections	0.081	0.004	0.085
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.084	0.004	0.087
setSCTKDisplayRow	0.432	0.021	0.454
singleCellTK	0	0	0
subDiffEx	0.346	0.010	0.356
subsetSCECols	0.078	0.002	0.079
subsetSCERows	0.204	0.013	0.217
summarizeSCE	0.067	0.000	0.067
trimCounts	0.192	0.024	0.216