Multiple platform build/check report for BioC 3.22 - CHECK results for singleCellTK on taishan

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"	4610
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 taishan

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.
- See Martin Grigorov's blog post for how to debug Linux ARM64 related issues on a x86_64 host.

raw results

Summary

Package: singleCellTK

Version: 2.20.0

Command: /home/biocbuild/R/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings singleCellTK_2.20.0.tar.gz

StartedAt: 2025-11-28 14:48:31 -0000 (Fri, 28 Nov 2025)

EndedAt: 2025-11-28 15:12:33 -0000 (Fri, 28 Nov 2025)

EllapsedTime: 1442.9 seconds

RetCode: 0

Status: OK

CheckDir: singleCellTK.Rcheck

Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings singleCellTK_2.20.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.0 (2025-04-11)
* using platform: aarch64-unknown-linux-gnu
* R was compiled by
    aarch64-unknown-linux-gnu-gcc (GCC) 14.2.0
    GNU Fortran (GCC) 14.2.0
* running under: openEuler 24.03 (LTS)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* 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 49.858  0.615  52.096
plotDoubletFinderResults 45.685  0.593  49.411
runSeuratSCTransform     44.987  0.906  48.354
runDoubletFinder         37.834  0.678  39.332
plotScDblFinderResults   35.080  0.558  36.173
runScDblFinder           23.308  0.629  25.063
importExampleData        16.179  0.517  25.672
plotBatchCorrCompare     15.537  0.642  17.787
plotScdsHybridResults    12.718  0.171  12.907
plotBcdsResults          11.905  0.731  13.899
plotDecontXResults       10.522  0.295  11.186
plotCxdsResults           9.253  0.661  11.545
plotDEGViolin             8.366  0.251   8.807
runDecontX                7.946  0.371   8.340
plotUMAP                  7.908  0.362   8.854
runUMAP                   7.834  0.159   8.027
plotTSCANClusterDEG       7.628  0.131   9.372
plotDEGRegression         7.166  0.252   8.343
convertSCEToSeurat        6.093  0.242   6.561
detectCellOutlier         5.925  0.171   6.116
plotFindMarkerHeatmap     5.927  0.057   6.003
plotEmptyDropsResults     5.893  0.051   6.513
plotEmptyDropsScatter     5.700  0.055   5.769
runEmptyDrops             5.253  0.020   5.292
runEnrichR                0.424  0.070  82.222
getEnrichRResult          0.444  0.017  13.112
* 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 running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* 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/R/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/R/R-4.5.0/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 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-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 
  0.198   0.020   0.229

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-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%
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  |======================================================================| 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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  |======================================================================| 100%

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  |======================================================================| 100%
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...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 100%

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

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  |======================================================================| 100%

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  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 21 | SKIP 0 | PASS 225 ]

[ FAIL 0 | WARN 21 | SKIP 0 | PASS 225 ]
> 
> proc.time()
   user  system elapsed 
365.953   6.683 422.505

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.003	0.000	0.003
SEG	0.002	0.000	0.003
calcEffectSizes	0.246	0.012	0.258
combineSCE	1.073	0.060	1.136
computeZScore	0.282	0.035	0.330
convertSCEToSeurat	6.093	0.242	6.561
convertSeuratToSCE	0.453	0.020	0.474
dedupRowNames	0.085	0.000	0.086
detectCellOutlier	5.925	0.171	6.116
diffAbundanceFET	0.067	0.000	0.067
discreteColorPalette	0.008	0.000	0.008
distinctColors	0.003	0.000	0.003
downSampleCells	0.746	0.092	0.840
downSampleDepth	0.634	0.000	0.635
expData-ANY-character-method	0.184	0.000	0.185
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.239	0.000	0.240
expData-set	0.221	0.008	0.230
expData	0.170	0.008	0.178
expDataNames-ANY-method	0.168	0.004	0.172
expDataNames	0.152	0.004	0.158
expDeleteDataTag	0.037	0.004	0.041
expSetDataTag	0.030	0.000	0.029
expTaggedData	0.032	0.000	0.032
exportSCE	0.028	0.000	0.029
exportSCEtoAnnData	0.068	0.016	0.085
exportSCEtoFlatFile	0.073	0.004	0.077
featureIndex	0.051	0.000	0.051
generateSimulatedData	0.060	0.008	0.068
getBiomarker	0.074	0.008	0.082
getDEGTopTable	0.957	0.139	1.109
getDiffAbundanceResults	0.058	0.000	0.057
getEnrichRResult	0.444	0.017	13.112
getFindMarkerTopTable	2.187	0.038	2.247
getMSigDBTable	0.003	0.000	0.004
getPathwayResultNames	0.027	0.000	0.027
getSampleSummaryStatsTable	0.256	0.003	0.260
getSoupX	0	0	0
getTSCANResults	1.530	0.047	1.582
getTopHVG	1.140	0.004	1.172
importAnnData	0.001	0.000	0.001
importBUStools	0.259	0.004	0.265
importCellRanger	1.115	0.020	1.142
importCellRangerV2Sample	0.218	0.000	0.218
importCellRangerV3Sample	0.576	0.008	0.586
importDropEst	0.292	0.004	0.340
importExampleData	16.179	0.517	25.672
importGeneSetsFromCollection	0.988	0.063	1.056
importGeneSetsFromGMT	0.083	0.007	0.153
importGeneSetsFromList	0.187	0.001	0.232
importGeneSetsFromMSigDB	49.858	0.615	52.096
importMitoGeneSet	0.078	0.004	0.082
importOptimus	0.002	0.000	0.001
importSEQC	0.230	0.000	0.232
importSTARsolo	0.214	0.020	0.237
iterateSimulations	0.252	0.004	0.257
listSampleSummaryStatsTables	0.334	0.008	0.343
mergeSCEColData	0.510	0.016	0.528
mouseBrainSubsetSCE	0.041	0.000	0.041
msigdb_table	0.001	0.000	0.001
plotBarcodeRankDropsResults	2.780	0.044	2.832
plotBarcodeRankScatter	1.199	0.075	1.278
plotBatchCorrCompare	15.537	0.642	17.787
plotBatchVariance	0.743	0.100	0.846
plotBcdsResults	11.905	0.731	13.899
plotBubble	1.175	0.116	1.294
plotClusterAbundance	2.033	0.251	2.294
plotCxdsResults	9.253	0.661	11.545
plotDEGHeatmap	3.330	0.122	4.827
plotDEGRegression	7.166	0.252	8.343
plotDEGViolin	8.366	0.251	8.807
plotDEGVolcano	1.317	0.008	1.337
plotDecontXResults	10.522	0.295	11.186
plotDimRed	0.471	0.004	0.547
plotDoubletFinderResults	45.685	0.593	49.411
plotEmptyDropsResults	5.893	0.051	6.513
plotEmptyDropsScatter	5.700	0.055	5.769
plotFindMarkerHeatmap	5.927	0.057	6.003
plotMASTThresholdGenes	1.964	0.023	1.996
plotPCA	0.540	0.012	0.554
plotPathway	1.106	0.008	1.119
plotRunPerCellQCResults	4.739	0.032	4.788
plotSCEBarAssayData	0.399	0.000	0.401
plotSCEBarColData	0.430	0.000	0.432
plotSCEBatchFeatureMean	0.611	0.000	0.613
plotSCEDensity	0.461	0.004	0.466
plotSCEDensityAssayData	0.419	0.000	0.421
plotSCEDensityColData	0.548	0.012	0.563
plotSCEDimReduceColData	1.131	0.028	1.163
plotSCEDimReduceFeatures	0.681	0.000	0.684
plotSCEHeatmap	0.661	0.012	0.676
plotSCEScatter	0.541	0.000	0.544
plotSCEViolin	0.647	0.000	0.649
plotSCEViolinAssayData	0.569	0.000	0.571
plotSCEViolinColData	0.567	0.028	0.598
plotScDblFinderResults	35.080	0.558	36.173
plotScanpyDotPlot	0.025	0.000	0.025
plotScanpyEmbedding	0.025	0.000	0.025
plotScanpyHVG	0.025	0.000	0.025
plotScanpyHeatmap	0.024	0.000	0.024
plotScanpyMarkerGenes	0.024	0.000	0.024
plotScanpyMarkerGenesDotPlot	0.025	0.000	0.025
plotScanpyMarkerGenesHeatmap	0.021	0.004	0.025
plotScanpyMarkerGenesMatrixPlot	0.026	0.000	0.027
plotScanpyMarkerGenesViolin	0.026	0.000	0.026
plotScanpyMatrixPlot	0.026	0.000	0.026
plotScanpyPCA	0.027	0.000	0.027
plotScanpyPCAGeneRanking	0.025	0.000	0.025
plotScanpyPCAVariance	0.026	0.000	0.025
plotScanpyViolin	0.025	0.000	0.025
plotScdsHybridResults	12.718	0.171	12.907
plotScrubletResults	0.028	0.000	0.027
plotSeuratElbow	0.027	0.000	0.026
plotSeuratHVG	0.026	0.000	0.026
plotSeuratJackStraw	0.026	0.000	0.027
plotSeuratReduction	0.026	0.000	0.027
plotSoupXResults	0	0	0
plotTSCANClusterDEG	7.628	0.131	9.372
plotTSCANClusterPseudo	2.156	0.020	2.494
plotTSCANDimReduceFeatures	2.315	0.028	2.358
plotTSCANPseudotimeGenes	2.649	0.016	2.679
plotTSCANPseudotimeHeatmap	2.012	0.012	2.040
plotTSCANResults	1.892	0.016	1.924
plotTSNE	0.563	0.008	0.573
plotTopHVG	1.001	0.000	1.005
plotUMAP	7.908	0.362	8.854
readSingleCellMatrix	0.007	0.000	0.007
reportCellQC	0.118	0.008	0.126
reportDropletQC	0.028	0.000	0.029
reportQCTool	0.116	0.000	0.116
retrieveSCEIndex	0.037	0.000	0.037
runBBKNN	0	0	0
runBarcodeRankDrops	0.322	0.020	0.343
runBcds	3.049	0.092	2.884
runCellQC	0.115	0.008	0.123
runClusterSummaryMetrics	0.538	0.032	0.571
runComBatSeq	0.682	0.020	0.704
runCxds	0.452	0.019	0.473
runCxdsBcdsHybrid	3.177	0.092	3.247
runDEAnalysis	0.630	0.064	0.717
runDecontX	7.946	0.371	8.340
runDimReduce	0.405	0.004	0.411
runDoubletFinder	37.834	0.678	39.332
runDropletQC	0.028	0.000	0.029
runEmptyDrops	5.253	0.020	5.292
runEnrichR	0.424	0.070	82.222
runFastMNN	2.565	0.224	2.966
runFeatureSelection	0.290	0.032	0.328
runFindMarker	2.206	0.147	2.936
runGSVA	1.060	0.072	1.145
runHarmony	0.057	0.004	0.062
runKMeans	0.393	0.031	0.427
runLimmaBC	0.123	0.004	0.126
runMNNCorrect	0.601	0.032	0.643
runModelGeneVar	0.444	0.024	0.470
runNormalization	2.999	0.258	3.303
runPerCellQC	0.479	0.012	0.498
runSCANORAMA	0	0	0
runSCMerge	0.001	0.004	0.004
runScDblFinder	23.308	0.629	25.063
runScanpyFindClusters	0.029	0.000	0.030
runScanpyFindHVG	0.028	0.000	0.028
runScanpyFindMarkers	0.028	0.000	0.029
runScanpyNormalizeData	0.146	0.000	0.146
runScanpyPCA	0.027	0.004	0.031
runScanpyScaleData	0.025	0.004	0.029
runScanpyTSNE	0.03	0.00	0.03
runScanpyUMAP	0.033	0.000	0.032
runScranSNN	0.456	0.004	0.891
runScrublet	0.022	0.004	0.026
runSeuratFindClusters	0.029	0.000	0.029
runSeuratFindHVG	0.686	0.012	0.701
runSeuratHeatmap	0.027	0.000	0.027
runSeuratICA	0.026	0.000	0.027
runSeuratJackStraw	0.028	0.000	0.029
runSeuratNormalizeData	0.028	0.000	0.028
runSeuratPCA	0.022	0.004	0.027
runSeuratSCTransform	44.987	0.906	48.354
runSeuratScaleData	0.03	0.00	0.03
runSeuratUMAP	0.029	0.000	0.029
runSingleR	0.054	0.000	0.054
runSoupX	0	0	0
runTSCAN	0.944	0.000	0.947
runTSCANClusterDEAnalysis	1.101	0.016	1.123
runTSCANDEG	1.074	0.008	1.085
runTSNE	1.006	0.020	1.028
runUMAP	7.834	0.159	8.027
runVAM	0.427	0.012	0.440
runZINBWaVE	0.005	0.000	0.005
sampleSummaryStats	0.227	0.012	0.240
scaterCPM	0.140	0.008	0.149
scaterPCA	0.634	0.004	0.644
scaterlogNormCounts	0.275	0.004	0.280
sce	0.026	0.000	0.026
sctkListGeneSetCollections	0.110	0.000	0.111
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.111	0.000	0.111
setSCTKDisplayRow	0.578	0.000	0.579
singleCellTK	0.001	0.000	0.000
subDiffEx	0.408	0.016	0.425
subsetSCECols	0.097	0.011	0.108
subsetSCERows	0.391	0.004	0.397
summarizeSCE	0.083	0.004	0.088
trimCounts	0.213	0.004	0.218