Multiple platform build/check report for BioC 3.21 - CHECK results for singleCellTK on nebbiolo1

Hostname	OS	Arch (*)	R version	Installed pkgs
nebbiolo1	Linux (Ubuntu 24.04.3 LTS)	x86_64	4.5.1 (2025-06-13) -- "Great Square Root"	4824
merida1	macOS 12.7.5 Monterey	x86_64	4.5.1 RC (2025-06-05 r88288) -- "Great Square Root"	4606
kjohnson1	macOS 13.6.6 Ventura	arm64	4.5.1 Patched (2025-06-14 r88325) -- "Great Square Root"	4547
kunpeng2	Linux (openEuler 24.03 LTS)	aarch64	R Under development (unstable) (2025-02-19 r87757) -- "Unsuffered Consequences"	4579
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 nebbiolo1

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: 2.18.1

Command: /home/biocbuild/bbs-3.21-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.21-bioc/R/site-library --timings singleCellTK_2.18.1.tar.gz

StartedAt: 2025-09-01 02:56:39 -0400 (Mon, 01 Sep 2025)

EndedAt: 2025-09-01 03:13:17 -0400 (Mon, 01 Sep 2025)

EllapsedTime: 998.1 seconds

RetCode: 0

Status: OK

CheckDir: singleCellTK.Rcheck

Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.21-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.21-bioc/R/site-library --timings singleCellTK_2.18.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.21-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.1 (2025-06-13)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
    GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
* running under: Ubuntu 24.04.3 LTS
* using session charset: UTF-8
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.18.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 79 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  5.6Mb
  sub-directories of 1Mb or more:
    shiny   2.3Mb
* 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
  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 45.666  1.761  47.431
plotDoubletFinderResults 39.828  0.470  40.376
runDoubletFinder         36.942  0.199  37.144
plotScDblFinderResults   30.524  0.826  31.430
runSeuratSCTransform     28.719  0.279  29.000
runScDblFinder           20.603  0.403  21.005
importExampleData        12.009  2.267  14.694
plotBatchCorrCompare     12.624  0.191  13.000
plotScdsHybridResults     9.535  0.062   8.919
plotBcdsResults           8.998  0.234   8.466
runUMAP                   8.415  0.147   8.640
plotDecontXResults        8.220  0.188   8.408
plotCxdsResults           7.844  0.171   8.097
plotUMAP                  6.883  0.053   7.020
runDecontX                6.883  0.052   6.937
plotEmptyDropsResults     6.517  0.057   6.575
plotEmptyDropsScatter     6.468  0.035   6.504
runEmptyDrops             6.253  0.012   6.265
detectCellOutlier         5.289  0.163   5.454
* 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-3.21-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.

Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.21-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.21-bioc/R/site-library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.18.1’
** 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 4.5.1 (2025-06-13) -- "Great Square Root"
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 
  0.152   0.046   0.180

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.1 (2025-06-13) -- "Great Square Root"
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: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

The following objects are masked from 'package:matrixStats':

    anyMissing, rowMedians

Loading required package: SingleCellExperiment
Loading required package: DelayedArray
Loading required package: Matrix

Attaching package: 'Matrix'

The following object is masked from 'package:S4Vectors':

    expand

Loading required package: S4Arrays
Loading required package: abind

Attaching package: 'S4Arrays'

The following object is masked from 'package:abind':

    abind

The following object is masked from 'package:base':

    rowsum

Loading required package: SparseArray

Attaching package: 'DelayedArray'

The following objects are masked from 'package:base':

    apply, scale, sweep


Attaching package: 'singleCellTK'

The following object is masked from 'package:BiocGenerics':

    plotPCA

> 
> test_check("singleCellTK")
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 0 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 1 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Uploading data to Enrichr... Done.
  Querying HDSigDB_Human_2021... Done.
Parsing results... Done.
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
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Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 390
Number of edges: 9849

Running Louvain algorithm...
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
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**************************************************|
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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]

[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]
> 
> proc.time()
   user  system elapsed 
297.776   7.980 309.032

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.002	0.001	0.003
SEG	0.003	0.000	0.003
calcEffectSizes	0.190	0.011	0.201
combineSCE	0.693	0.006	0.699
computeZScore	0.240	0.007	0.246
convertSCEToSeurat	3.967	0.147	4.115
convertSeuratToSCE	0.324	0.009	0.333
dedupRowNames	0.050	0.000	0.051
detectCellOutlier	5.289	0.163	5.454
diffAbundanceFET	0.052	0.003	0.055
discreteColorPalette	0.006	0.000	0.006
distinctColors	0.002	0.000	0.003
downSampleCells	0.478	0.052	0.531
downSampleDepth	0.401	0.004	0.405
expData-ANY-character-method	0.119	0.003	0.123
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.150	0.001	0.151
expData-set	0.146	0.001	0.147
expData	0.115	0.001	0.115
expDataNames-ANY-method	0.109	0.003	0.111
expDataNames	0.109	0.001	0.110
expDeleteDataTag	0.033	0.001	0.033
expSetDataTag	0.022	0.001	0.023
expTaggedData	0.023	0.002	0.025
exportSCE	0.020	0.001	0.021
exportSCEtoAnnData	0.088	0.007	0.096
exportSCEtoFlatFile	0.087	0.008	0.095
featureIndex	0.031	0.003	0.035
generateSimulatedData	0.046	0.002	0.049
getBiomarker	0.054	0.002	0.056
getDEGTopTable	0.667	0.080	0.747
getDiffAbundanceResults	0.049	0.001	0.049
getEnrichRResult	0.463	0.041	3.122
getFindMarkerTopTable	1.455	0.035	1.490
getMSigDBTable	0.004	0.000	0.005
getPathwayResultNames	0.019	0.003	0.022
getSampleSummaryStatsTable	0.199	0.001	0.200
getSoupX	0	0	0
getTSCANResults	1.020	0.024	1.046
getTopHVG	0.810	0.032	0.843
importAnnData	0.002	0.000	0.001
importBUStools	0.160	0.017	0.179
importCellRanger	0.768	0.086	0.856
importCellRangerV2Sample	0.149	0.002	0.151
importCellRangerV3Sample	0.306	0.017	0.323
importDropEst	0.197	0.003	0.200
importExampleData	12.009	2.267	14.694
importGeneSetsFromCollection	0.671	0.034	0.705
importGeneSetsFromGMT	0.059	0.004	0.064
importGeneSetsFromList	0.117	0.003	0.120
importGeneSetsFromMSigDB	45.666	1.761	47.431
importMitoGeneSet	0.047	0.002	0.050
importOptimus	0.001	0.001	0.002
importSEQC	0.147	0.010	0.158
importSTARsolo	0.141	0.023	0.164
iterateSimulations	0.183	0.040	0.222
listSampleSummaryStatsTables	0.264	0.027	0.291
mergeSCEColData	0.383	0.053	0.436
mouseBrainSubsetSCE	0.034	0.002	0.036
msigdb_table	0.000	0.001	0.001
plotBarcodeRankDropsResults	0.582	0.018	0.600
plotBarcodeRankScatter	0.652	0.003	0.655
plotBatchCorrCompare	12.624	0.191	13.000
plotBatchVariance	0.368	0.011	0.379
plotBcdsResults	8.998	0.234	8.466
plotBubble	0.694	0.028	0.722
plotClusterAbundance	0.818	0.033	0.851
plotCxdsResults	7.844	0.171	8.097
plotDEGHeatmap	2.156	0.018	2.174
plotDEGRegression	3.369	0.010	3.372
plotDEGViolin	4.037	0.098	4.129
plotDEGVolcano	0.791	0.023	0.814
plotDecontXResults	8.220	0.188	8.408
plotDimRed	0.195	0.000	0.196
plotDoubletFinderResults	39.828	0.470	40.376
plotEmptyDropsResults	6.517	0.057	6.575
plotEmptyDropsScatter	6.468	0.035	6.504
plotFindMarkerHeatmap	3.847	0.062	3.909
plotMASTThresholdGenes	1.329	0.013	1.341
plotPCA	0.307	0.004	0.311
plotPathway	0.599	0.002	0.602
plotRunPerCellQCResults	1.928	0.015	1.942
plotSCEBarAssayData	0.187	0.001	0.189
plotSCEBarColData	0.145	0.001	0.145
plotSCEBatchFeatureMean	0.225	0.000	0.225
plotSCEDensity	0.213	0.002	0.216
plotSCEDensityAssayData	0.215	0.002	0.217
plotSCEDensityColData	0.204	0.001	0.205
plotSCEDimReduceColData	0.503	0.003	0.505
plotSCEDimReduceFeatures	0.261	0.003	0.264
plotSCEHeatmap	0.473	0.001	0.474
plotSCEScatter	0.243	0.002	0.245
plotSCEViolin	0.247	0.000	0.247
plotSCEViolinAssayData	0.261	0.001	0.262
plotSCEViolinColData	0.247	0.003	0.249
plotScDblFinderResults	30.524	0.826	31.430
plotScanpyDotPlot	0.023	0.000	0.022
plotScanpyEmbedding	0.022	0.001	0.022
plotScanpyHVG	0.022	0.000	0.021
plotScanpyHeatmap	0.021	0.001	0.022
plotScanpyMarkerGenes	0.021	0.001	0.022
plotScanpyMarkerGenesDotPlot	0.021	0.001	0.022
plotScanpyMarkerGenesHeatmap	0.023	0.000	0.022
plotScanpyMarkerGenesMatrixPlot	0.021	0.001	0.022
plotScanpyMarkerGenesViolin	0.021	0.001	0.022
plotScanpyMatrixPlot	0.022	0.000	0.022
plotScanpyPCA	0.020	0.001	0.021
plotScanpyPCAGeneRanking	0.022	0.000	0.022
plotScanpyPCAVariance	0.021	0.001	0.021
plotScanpyViolin	0.022	0.000	0.022
plotScdsHybridResults	9.535	0.062	8.919
plotScrubletResults	0.022	0.000	0.022
plotSeuratElbow	0.022	0.000	0.022
plotSeuratHVG	0.021	0.000	0.022
plotSeuratJackStraw	0.022	0.000	0.021
plotSeuratReduction	0.020	0.000	0.022
plotSoupXResults	0.000	0.000	0.001
plotTSCANClusterDEG	3.582	0.037	3.619
plotTSCANClusterPseudo	1.177	0.011	1.188
plotTSCANDimReduceFeatures	1.239	0.007	1.246
plotTSCANPseudotimeGenes	1.390	0.004	1.394
plotTSCANPseudotimeHeatmap	1.330	0.002	1.332
plotTSCANResults	1.190	0.007	1.200
plotTSNE	0.315	0.003	0.319
plotTopHVG	0.537	0.003	0.544
plotUMAP	6.883	0.053	7.020
readSingleCellMatrix	0.005	0.000	0.005
reportCellQC	0.076	0.000	0.076
reportDropletQC	0.021	0.000	0.021
reportQCTool	0.073	0.003	0.076
retrieveSCEIndex	0.026	0.001	0.027
runBBKNN	0.000	0.001	0.000
runBarcodeRankDrops	0.210	0.001	0.211
runBcds	1.980	0.042	1.203
runCellQC	0.075	0.005	0.080
runClusterSummaryMetrics	0.362	0.001	0.363
runComBatSeq	0.425	0.005	0.430
runCxds	0.315	0.000	0.315
runCxdsBcdsHybrid	1.981	0.061	1.234
runDEAnalysis	0.422	0.018	0.440
runDecontX	6.883	0.052	6.937
runDimReduce	0.275	0.006	0.281
runDoubletFinder	36.942	0.199	37.144
runDropletQC	0.023	0.000	0.023
runEmptyDrops	6.253	0.012	6.265
runEnrichR	0.507	0.026	2.584
runFastMNN	1.725	0.031	1.755
runFeatureSelection	0.217	0.002	0.219
runFindMarker	1.382	0.031	1.412
runGSVA	0.669	0.022	0.691
runHarmony	0.258	0.004	0.263
runKMeans	0.182	0.003	0.185
runLimmaBC	0.076	0.001	0.077
runMNNCorrect	0.394	0.002	0.397
runModelGeneVar	0.299	0.001	0.300
runNormalization	2.599	0.127	2.727
runPerCellQC	0.324	0.004	0.328
runSCANORAMA	0.000	0.001	0.000
runSCMerge	0.004	0.001	0.004
runScDblFinder	20.603	0.403	21.005
runScanpyFindClusters	0.024	0.000	0.024
runScanpyFindHVG	0.023	0.000	0.023
runScanpyFindMarkers	0.023	0.001	0.024
runScanpyNormalizeData	0.095	0.000	0.095
runScanpyPCA	0.023	0.000	0.023
runScanpyScaleData	0.022	0.001	0.023
runScanpyTSNE	0.022	0.000	0.022
runScanpyUMAP	0.022	0.000	0.022
runScranSNN	0.284	0.002	0.287
runScrublet	0.023	0.000	0.023
runSeuratFindClusters	0.023	0.000	0.022
runSeuratFindHVG	0.456	0.010	0.466
runSeuratHeatmap	0.022	0.001	0.023
runSeuratICA	0.024	0.000	0.024
runSeuratJackStraw	0.024	0.000	0.023
runSeuratNormalizeData	0.023	0.000	0.024
runSeuratPCA	0.023	0.000	0.023
runSeuratSCTransform	28.719	0.279	29.000
runSeuratScaleData	0.023	0.000	0.023
runSeuratUMAP	0.022	0.001	0.023
runSingleR	0.036	0.001	0.036
runSoupX	0	0	0
runTSCAN	0.673	0.002	0.675
runTSCANClusterDEAnalysis	0.816	0.010	0.826
runTSCANDEG	0.817	0.013	0.830
runTSNE	0.732	0.001	0.733
runUMAP	8.415	0.147	8.640
runVAM	0.305	0.002	0.306
runZINBWaVE	0.004	0.001	0.005
sampleSummaryStats	0.161	0.002	0.163
scaterCPM	0.134	0.008	0.142
scaterPCA	0.452	0.001	0.452
scaterlogNormCounts	0.239	0.001	0.241
sce	0.022	0.001	0.022
sctkListGeneSetCollections	0.072	0.002	0.074
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.081	0.000	0.082
setSCTKDisplayRow	0.308	0.001	0.310
singleCellTK	0	0	0
subDiffEx	0.328	0.007	0.334
subsetSCECols	0.088	0.001	0.089
subsetSCERows	0.225	0.002	0.227
summarizeSCE	0.070	0.001	0.071
trimCounts	0.222	0.007	0.229