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"	4892
Click on any hostname to see more info about the system (e.g. compilers) () as reported by 'uname -p', except on Windows and Mac OS X*

CHECK results for singleCellTK on nebbiolo2

To the developers/maintainers of the singleCellTK package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/singleCellTK.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results

Summary

Package: singleCellTK

Version: 2.20.1

Command: /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings singleCellTK_2.20.1.tar.gz

StartedAt: 2026-03-19 04:15:03 -0400 (Thu, 19 Mar 2026)

EndedAt: 2026-03-19 04:37:23 -0400 (Thu, 19 Mar 2026)

EllapsedTime: 1340.2 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 24.04.3 LTS
* using session charset: UTF-8
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.20.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 80 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable.  Move as many as possible to Suggests and
use conditionally.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... INFO
  installed size is  7.0Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    extdata   1.6Mb
    shiny     3.0Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
  dedupRowNames.Rd: SingleCellExperiment-class
  detectCellOutlier.Rd: colData
  diffAbundanceFET.Rd: colData
  downSampleCells.Rd: SingleCellExperiment-class
  downSampleDepth.Rd: SingleCellExperiment-class
  featureIndex.Rd: SummarizedExperiment-class,
    SingleCellExperiment-class
  getBiomarker.Rd: SingleCellExperiment-class
  getDEGTopTable.Rd: SingleCellExperiment-class
  getEnrichRResult.Rd: SingleCellExperiment-class
  getFindMarkerTopTable.Rd: SingleCellExperiment-class
  getGenesetNamesFromCollection.Rd: SingleCellExperiment-class
  getPathwayResultNames.Rd: SingleCellExperiment-class
  getSampleSummaryStatsTable.Rd: SingleCellExperiment-class, assay,
    colData
  getSoupX.Rd: SingleCellExperiment-class
  getTSCANResults.Rd: SingleCellExperiment-class
  getTopHVG.Rd: SingleCellExperiment-class
  importAlevin.Rd: DelayedArray, readMM
  importAnnData.Rd: DelayedArray, readMM
  importBUStools.Rd: readMM
  importCellRanger.Rd: readMM, DelayedArray
  importCellRangerV2Sample.Rd: readMM, DelayedArray
  importCellRangerV3Sample.Rd: readMM, DelayedArray
  importDropEst.Rd: DelayedArray, readMM
  importExampleData.Rd: scRNAseq, Matrix, DelayedArray,
    ReprocessedFluidigmData, ReprocessedAllenData, NestorowaHSCData
  importFromFiles.Rd: readMM, DelayedArray, SingleCellExperiment-class
  importGeneSetsFromCollection.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GeneSetCollection, GSEABase, metadata
  importGeneSetsFromGMT.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, getGmt, GSEABase, metadata
  importGeneSetsFromList.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GSEABase, metadata
  importGeneSetsFromMSigDB.Rd: SingleCellExperiment-class, msigdbr,
    GeneSetCollection-class, GSEABase, metadata
  importMitoGeneSet.Rd: SingleCellExperiment-class,
    GeneSetCollection-class, GSEABase, metadata
  importMultipleSources.Rd: DelayedArray
  importOptimus.Rd: readMM, DelayedArray
  importSEQC.Rd: readMM, DelayedArray
  importSTARsolo.Rd: readMM, DelayedArray
  iterateSimulations.Rd: SingleCellExperiment-class
  listSampleSummaryStatsTables.Rd: SingleCellExperiment-class, metadata
  plotBarcodeRankDropsResults.Rd: SingleCellExperiment-class
  plotBarcodeRankScatter.Rd: SingleCellExperiment-class
  plotBatchCorrCompare.Rd: SingleCellExperiment-class
  plotBatchVariance.Rd: SingleCellExperiment-class
  plotBcdsResults.Rd: SingleCellExperiment-class
  plotClusterAbundance.Rd: colData
  plotCxdsResults.Rd: SingleCellExperiment-class
  plotDEGHeatmap.Rd: SingleCellExperiment-class
  plotDEGRegression.Rd: SingleCellExperiment-class
  plotDEGViolin.Rd: SingleCellExperiment-class
  plotDEGVolcano.Rd: SingleCellExperiment-class
  plotDecontXResults.Rd: SingleCellExperiment-class
  plotDoubletFinderResults.Rd: SingleCellExperiment-class
  plotEmptyDropsResults.Rd: SingleCellExperiment-class
  plotEmptyDropsScatter.Rd: SingleCellExperiment-class
  plotEnrichR.Rd: SingleCellExperiment-class
  plotFindMarkerHeatmap.Rd: SingleCellExperiment-class
  plotPCA.Rd: SingleCellExperiment-class
  plotPathway.Rd: SingleCellExperiment-class
  plotRunPerCellQCResults.Rd: SingleCellExperiment-class
  plotSCEBarAssayData.Rd: SingleCellExperiment-class
  plotSCEBarColData.Rd: SingleCellExperiment-class
  plotSCEBatchFeatureMean.Rd: SingleCellExperiment-class
  plotSCEDensity.Rd: SingleCellExperiment-class
  plotSCEDensityAssayData.Rd: SingleCellExperiment-class
  plotSCEDensityColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceFeatures.Rd: SingleCellExperiment-class
  plotSCEHeatmap.Rd: SingleCellExperiment-class
  plotSCEScatter.Rd: SingleCellExperiment-class
  plotSCEViolin.Rd: SingleCellExperiment-class
  plotSCEViolinAssayData.Rd: SingleCellExperiment-class
  plotSCEViolinColData.Rd: SingleCellExperiment-class
  plotScDblFinderResults.Rd: SingleCellExperiment-class
  plotScdsHybridResults.Rd: SingleCellExperiment-class
  plotScrubletResults.Rd: SingleCellExperiment-class
  plotSoupXResults.Rd: SingleCellExperiment-class
  plotTSCANClusterDEG.Rd: SingleCellExperiment-class
  plotTSCANClusterPseudo.Rd: SingleCellExperiment-class
  plotTSCANDimReduceFeatures.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeGenes.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeHeatmap.Rd: SingleCellExperiment-class
  plotTSCANResults.Rd: SingleCellExperiment-class
  plotTSNE.Rd: SingleCellExperiment-class
  plotUMAP.Rd: SingleCellExperiment-class
  readSingleCellMatrix.Rd: DelayedArray
  reportCellQC.Rd: SingleCellExperiment-class
  reportClusterAbundance.Rd: colData
  reportDiffAbundanceFET.Rd: colData
  retrieveSCEIndex.Rd: SingleCellExperiment-class
  runBBKNN.Rd: SingleCellExperiment-class
  runBarcodeRankDrops.Rd: SingleCellExperiment-class, colData
  runBcds.Rd: SingleCellExperiment-class, colData
  runCellQC.Rd: colData
  runComBatSeq.Rd: SingleCellExperiment-class
  runCxds.Rd: SingleCellExperiment-class, colData
  runCxdsBcdsHybrid.Rd: colData
  runDEAnalysis.Rd: SingleCellExperiment-class
  runDecontX.Rd: colData
  runDimReduce.Rd: SingleCellExperiment-class
  runDoubletFinder.Rd: SingleCellExperiment-class
  runDropletQC.Rd: colData
  runEmptyDrops.Rd: SingleCellExperiment-class, colData
  runEnrichR.Rd: SingleCellExperiment-class
  runFastMNN.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runFeatureSelection.Rd: SingleCellExperiment-class
  runFindMarker.Rd: SingleCellExperiment-class
  runGSVA.Rd: SingleCellExperiment-class
  runHarmony.Rd: SingleCellExperiment-class
  runKMeans.Rd: SingleCellExperiment-class, colData
  runLimmaBC.Rd: SingleCellExperiment-class, assay
  runMNNCorrect.Rd: SingleCellExperiment-class, assay,
    BiocParallelParam-class
  runModelGeneVar.Rd: SingleCellExperiment-class
  runPerCellQC.Rd: SingleCellExperiment-class, BiocParallelParam,
    colData
  runSCANORAMA.Rd: SingleCellExperiment-class, assay
  runSCMerge.Rd: SingleCellExperiment-class, colData, assay,
    BiocParallelParam-class
  runScDblFinder.Rd: SingleCellExperiment-class, colData
  runScranSNN.Rd: SingleCellExperiment-class, reducedDim, assay,
    altExp, colData, igraph
  runScrublet.Rd: SingleCellExperiment-class, colData
  runSingleR.Rd: SingleCellExperiment-class
  runSoupX.Rd: SingleCellExperiment-class
  runTSCAN.Rd: SingleCellExperiment-class
  runTSCANClusterDEAnalysis.Rd: SingleCellExperiment-class
  runTSCANDEG.Rd: SingleCellExperiment-class
  runTSNE.Rd: SingleCellExperiment-class
  runUMAP.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runVAM.Rd: SingleCellExperiment-class
  runZINBWaVE.Rd: SingleCellExperiment-class, colData,
    BiocParallelParam-class
  sampleSummaryStats.Rd: SingleCellExperiment-class, assay, colData
  scaterPCA.Rd: SingleCellExperiment-class, BiocParallelParam-class
  scaterlogNormCounts.Rd: logNormCounts
  sctkListGeneSetCollections.Rd: GeneSetCollection-class
  sctkPythonInstallConda.Rd: conda_install, reticulate, conda_create
  sctkPythonInstallVirtualEnv.Rd: virtualenv_install, reticulate,
    virtualenv_create
  selectSCTKConda.Rd: reticulate
  selectSCTKVirtualEnvironment.Rd: reticulate
  setRowNames.Rd: SingleCellExperiment-class
  setSCTKDisplayRow.Rd: SingleCellExperiment-class
  singleCellTK.Rd: SingleCellExperiment-class
  subsetSCECols.Rd: SingleCellExperiment-class
  subsetSCERows.Rd: SingleCellExperiment-class, altExp
  summarizeSCE.Rd: SingleCellExperiment-class
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
plotDoubletFinderResults 34.148  0.253  34.486
runDoubletFinder         31.166  0.236  31.407
runSeuratSCTransform     29.291  1.044  30.338
plotScDblFinderResults   27.397  0.440  24.959
runScDblFinder           16.628  0.738  14.309
plotBatchCorrCompare     11.685  0.332  12.203
importExampleData        10.350  1.019  11.870
plotDecontXResults        7.340  0.110   7.450
runUMAP                   6.713  0.126   6.921
detectCellOutlier         6.604  0.189   6.794
plotCxdsResults           6.483  0.135   6.698
plotEmptyDropsScatter     6.563  0.016   6.580
plotEmptyDropsResults     6.570  0.005   6.574
runEmptyDrops             6.260  0.015   6.275
runDecontX                6.248  0.007   6.256
plotUMAP                  5.884  0.014   5.976
plotScdsHybridResults     5.804  0.059   5.942
plotDEGViolin             5.160  0.071   5.224
runEnrichR                0.564  0.178 152.992
getEnrichRResult          0.571  0.127 154.302
* 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   0.036   0.174

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%
[04:35:22] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

[04:35:23] 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

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

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 19 | SKIP 0 | PASS 223 ]

[ FAIL 0 | WARN 19 | SKIP 0 | PASS 223 ]
> 
> proc.time()
   user  system elapsed 
260.484  10.111 422.868

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.002	0.001	0.003
SEG	0.001	0.001	0.003
calcEffectSizes	0.186	0.004	0.190
combineSCE	0.703	0.002	0.705
computeZScore	0.227	0.009	0.236
convertSCEToSeurat	4.727	0.110	4.836
convertSeuratToSCE	0.359	0.005	0.365
dedupRowNames	0.057	0.000	0.057
detectCellOutlier	6.604	0.189	6.794
diffAbundanceFET	0.053	0.001	0.054
discreteColorPalette	0.005	0.000	0.005
distinctColors	0.002	0.000	0.002
downSampleCells	0.495	0.062	0.556
downSampleDepth	0.422	0.027	0.448
expData-ANY-character-method	0.135	0.007	0.142
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.172	0.002	0.173
expData-set	0.145	0.004	0.148
expData	0.124	0.005	0.129
expDataNames-ANY-method	0.115	0.006	0.122
expDataNames	0.108	0.013	0.121
expDeleteDataTag	0.030	0.001	0.032
expSetDataTag	0.020	0.004	0.024
expTaggedData	0.024	0.000	0.024
exportSCE	0.019	0.002	0.022
exportSCEtoAnnData	0.093	0.005	0.097
exportSCEtoFlatFile	0.086	0.012	0.097
featureIndex	0.032	0.004	0.036
generateSimulatedData	0.073	0.001	0.074
getBiomarker	0.055	0.004	0.059
getDEGTopTable	0.630	0.009	0.639
getDiffAbundanceResults	0.046	0.000	0.045
getEnrichRResult	0.571	0.127	154.302
getFindMarkerTopTable	1.555	0.165	1.720
getMSigDBTable	0.003	0.001	0.004
getPathwayResultNames	0.021	0.002	0.023
getSampleSummaryStatsTable	0.201	0.026	0.227
getSoupX	0	0	0
getTSCANResults	0.956	0.091	1.047
getTopHVG	0.839	0.079	0.918
importAnnData	0.002	0.000	0.002
importBUStools	0.139	0.008	0.147
importCellRanger	0.719	0.056	0.777
importCellRangerV2Sample	0.137	0.000	0.138
importCellRangerV3Sample	0.266	0.020	0.287
importDropEst	1.202	0.205	1.407
importExampleData	10.350	1.019	11.870
importGeneSetsFromCollection	1.743	0.087	1.831
importGeneSetsFromGMT	0.06	0.00	0.06
importGeneSetsFromList	0.117	0.000	0.116
importGeneSetsFromMSigDB	0.960	0.096	1.057
importMitoGeneSet	0.046	0.008	0.054
importOptimus	0.001	0.000	0.001
importSEQC	0.138	0.015	0.153
importSTARsolo	0.173	0.018	0.191
iterateSimulations	0.171	0.004	0.174
listSampleSummaryStatsTables	0.240	0.002	0.242
mergeSCEColData	0.323	0.010	0.334
mouseBrainSubsetSCE	0.036	0.000	0.036
msigdb_table	0.001	0.000	0.001
plotBarcodeRankDropsResults	0.854	0.011	0.865
plotBarcodeRankScatter	0.815	0.018	0.833
plotBatchCorrCompare	11.685	0.332	12.203
plotBatchVariance	0.460	0.022	0.482
plotBcdsResults	4.797	0.108	4.984
plotBubble	0.758	0.005	0.762
plotClusterAbundance	1.271	0.041	1.312
plotCxdsResults	6.483	0.135	6.698
plotDEGHeatmap	1.988	0.030	2.018
plotDEGRegression	4.236	0.055	4.284
plotDEGViolin	5.160	0.071	5.224
plotDEGVolcano	0.897	0.008	0.905
plotDecontXResults	7.34	0.11	7.45
plotDimRed	0.273	0.002	0.276
plotDoubletFinderResults	34.148	0.253	34.486
plotEmptyDropsResults	6.570	0.005	6.574
plotEmptyDropsScatter	6.563	0.016	6.580
plotFindMarkerHeatmap	3.572	0.019	3.591
plotMASTThresholdGenes	1.179	0.023	1.202
plotPCA	0.358	0.001	0.359
plotPathway	0.609	0.001	0.611
plotRunPerCellQCResults	2.837	0.007	2.844
plotSCEBarAssayData	0.258	0.001	0.260
plotSCEBarColData	0.217	0.001	0.218
plotSCEBatchFeatureMean	0.364	0.000	0.364
plotSCEDensity	0.316	0.002	0.319
plotSCEDensityAssayData	0.251	0.001	0.252
plotSCEDensityColData	0.292	0.000	0.291
plotSCEDimReduceColData	0.729	0.003	0.731
plotSCEDimReduceFeatures	0.345	0.002	0.346
plotSCEHeatmap	0.407	0.001	0.407
plotSCEScatter	0.364	0.001	0.365
plotSCEViolin	0.335	0.012	0.348
plotSCEViolinAssayData	0.365	0.016	0.382
plotSCEViolinColData	0.346	0.003	0.350
plotScDblFinderResults	27.397	0.440	24.959
plotScanpyDotPlot	0.023	0.000	0.022
plotScanpyEmbedding	0.021	0.001	0.023
plotScanpyHVG	0.022	0.000	0.023
plotScanpyHeatmap	0.022	0.000	0.023
plotScanpyMarkerGenes	0.022	0.000	0.023
plotScanpyMarkerGenesDotPlot	0.022	0.001	0.024
plotScanpyMarkerGenesHeatmap	0.023	0.000	0.023
plotScanpyMarkerGenesMatrixPlot	0.023	0.000	0.023
plotScanpyMarkerGenesViolin	0.022	0.001	0.023
plotScanpyMatrixPlot	0.023	0.000	0.023
plotScanpyPCA	0.022	0.001	0.023
plotScanpyPCAGeneRanking	0.022	0.000	0.022
plotScanpyPCAVariance	0.022	0.000	0.022
plotScanpyViolin	0.022	0.000	0.023
plotScdsHybridResults	5.804	0.059	5.942
plotScrubletResults	0.023	0.000	0.023
plotSeuratElbow	0.023	0.000	0.023
plotSeuratHVG	0.024	0.000	0.024
plotSeuratJackStraw	0.023	0.000	0.023
plotSeuratReduction	0.023	0.000	0.023
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.573	0.037	4.610
plotTSCANClusterPseudo	1.341	0.021	1.362
plotTSCANDimReduceFeatures	1.350	0.005	1.355
plotTSCANPseudotimeGenes	1.541	0.002	1.543
plotTSCANPseudotimeHeatmap	1.245	0.005	1.250
plotTSCANResults	1.191	0.007	1.198
plotTSNE	0.354	0.000	0.354
plotTopHVG	0.602	0.002	0.604
plotUMAP	5.884	0.014	5.976
readSingleCellMatrix	0.005	0.000	0.005
reportCellQC	0.075	0.000	0.076
reportDropletQC	0.022	0.001	0.023
reportQCTool	0.075	0.000	0.076
retrieveSCEIndex	0.028	0.001	0.028
runBBKNN	0	0	0
runBarcodeRankDrops	0.208	0.000	0.208
runBcds	0.075	0.001	0.076
runCellQC	0.074	0.001	0.075
runClusterSummaryMetrics	0.343	0.001	0.344
runComBatSeq	0.414	0.002	0.416
runCxds	0.336	0.000	0.337
runCxdsBcdsHybrid	0.077	0.001	0.079
runDEAnalysis	0.360	0.002	0.362
runDecontX	6.248	0.007	6.256
runDimReduce	0.268	0.000	0.269
runDoubletFinder	31.166	0.236	31.407
runDropletQC	0.021	0.000	0.022
runEmptyDrops	6.260	0.015	6.275
runEnrichR	0.564	0.178	152.992
runFastMNN	1.756	0.194	1.950
runFeatureSelection	0.214	0.016	0.230
runFindMarker	1.537	0.144	1.681
runGSVA	0.854	0.111	0.965
runHarmony	0.044	0.001	0.044
runKMeans	0.186	0.002	0.188
runLimmaBC	0.079	0.004	0.083
runMNNCorrect	0.40	0.03	0.43
runModelGeneVar	0.296	0.020	0.317
runNormalization	2.329	0.388	2.717
runPerCellQC	0.312	0.004	0.317
runSCANORAMA	0	0	0
runSCMerge	0.004	0.000	0.004
runScDblFinder	16.628	0.738	14.309
runScanpyFindClusters	0.022	0.001	0.022
runScanpyFindHVG	0.023	0.000	0.023
runScanpyFindMarkers	0.022	0.000	0.023
runScanpyNormalizeData	0.096	0.000	0.096
runScanpyPCA	0.023	0.000	0.023
runScanpyScaleData	0.022	0.001	0.022
runScanpyTSNE	0.022	0.001	0.022
runScanpyUMAP	0.021	0.001	0.023
runScranSNN	0.285	0.002	0.287
runScrublet	0.022	0.001	0.023
runSeuratFindClusters	0.023	0.000	0.023
runSeuratFindHVG	0.469	0.003	0.472
runSeuratHeatmap	0.022	0.000	0.023
runSeuratICA	0.022	0.000	0.023
runSeuratJackStraw	0.023	0.000	0.022
runSeuratNormalizeData	0.021	0.001	0.023
runSeuratPCA	0.022	0.000	0.022
runSeuratSCTransform	29.291	1.044	30.338
runSeuratScaleData	0.023	0.001	0.023
runSeuratUMAP	0.019	0.003	0.022
runSingleR	0.039	0.001	0.040
runSoupX	0	0	0
runTSCAN	0.662	0.003	0.666
runTSCANClusterDEAnalysis	0.747	0.010	0.757
runTSCANDEG	0.751	0.013	0.764
runTSNE	0.722	0.003	0.725
runUMAP	6.713	0.126	6.921
runVAM	0.299	0.001	0.300
runZINBWaVE	0.003	0.001	0.004
sampleSummaryStats	0.159	0.000	0.159
scaterCPM	0.133	0.001	0.134
scaterPCA	0.441	0.002	0.484
scaterlogNormCounts	0.225	0.010	0.236
sce	0.022	0.000	0.022
sctkListGeneSetCollections	0.079	0.001	0.080
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.084	0.000	0.085
setSCTKDisplayRow	0.432	0.015	0.448
singleCellTK	0	0	0
subDiffEx	0.335	0.013	0.348
subsetSCECols	0.079	0.000	0.080
subsetSCERows	0.217	0.001	0.218
summarizeSCE	0.067	0.000	0.067
trimCounts	0.210	0.004	0.213