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"	4888
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: 2026-02-06 04:06:59 -0500 (Fri, 06 Feb 2026)

EndedAt: 2026-02-06 04:24:02 -0500 (Fri, 06 Feb 2026)

EllapsedTime: 1022.9 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
importGeneSetsFromMSigDB 44.797  1.776  46.574
plotDoubletFinderResults 38.885  0.512  39.483
runDoubletFinder         35.665  0.294  35.964
runSeuratSCTransform     30.172  1.139  31.315
plotScDblFinderResults   28.546  0.762  29.350
runScDblFinder           17.851  1.272  19.073
plotBatchCorrCompare     13.400  0.309  13.892
importExampleData        10.739  1.104  12.339
plotDecontXResults        8.980  0.128   9.109
runUMAP                   8.073  0.237   8.390
plotEmptyDropsResults     7.669  0.060   7.729
plotEmptyDropsScatter     7.610  0.031   7.641
plotUMAP                  7.394  0.086   7.557
runDecontX                7.399  0.055   7.455
plotCxdsResults           7.258  0.134   7.471
runEmptyDrops             7.364  0.011   7.376
plotScdsHybridResults     7.232  0.119   7.432
plotDEGViolin             6.513  0.194   6.702
plotBcdsResults           6.098  0.124   6.304
detectCellOutlier         5.799  0.183   5.984
* 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 
  0.157   0.037   0.183

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%
[04:21:50] WARNING: src/learner.cc:790: 
Parameters: { "nthreads" } are not used.

[04:21:51] WARNING: src/learner.cc:790: 
Parameters: { "nthreads" } are not used.

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

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

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Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 390
Number of edges: 9849

Running Louvain algorithm...
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[----|----|----|----|----|----|----|----|----|----|
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Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
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**************************************************|
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[----|----|----|----|----|----|----|----|----|----|
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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
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[ FAIL 0 | WARN 19 | SKIP 0 | PASS 223 ]

[ FAIL 0 | WARN 19 | SKIP 0 | PASS 223 ]
> 
> proc.time()
   user  system elapsed 
322.260   5.294 328.900

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.002	0.000	0.003
SEG	0.002	0.001	0.002
calcEffectSizes	0.199	0.003	0.204
combineSCE	0.738	0.000	0.739
computeZScore	0.230	0.012	0.243
convertSCEToSeurat	4.494	0.103	4.599
convertSeuratToSCE	0.343	0.006	0.350
dedupRowNames	0.053	0.000	0.054
detectCellOutlier	5.799	0.183	5.984
diffAbundanceFET	0.052	0.001	0.053
discreteColorPalette	0.006	0.000	0.005
distinctColors	0.002	0.000	0.002
downSampleCells	0.475	0.053	0.529
downSampleDepth	0.386	0.002	0.389
expData-ANY-character-method	0.118	0.001	0.118
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.150	0.001	0.150
expData-set	0.140	0.001	0.140
expData	0.118	0.000	0.118
expDataNames-ANY-method	0.113	0.001	0.114
expDataNames	0.105	0.011	0.115
expDeleteDataTag	0.029	0.002	0.032
expSetDataTag	0.021	0.003	0.024
expTaggedData	0.023	0.001	0.024
exportSCE	0.019	0.003	0.021
exportSCEtoAnnData	0.091	0.007	0.098
exportSCEtoFlatFile	0.089	0.017	0.106
featureIndex	0.033	0.005	0.037
generateSimulatedData	0.073	0.013	0.086
getBiomarker	0.052	0.005	0.057
getDEGTopTable	0.631	0.025	0.656
getDiffAbundanceResults	0.046	0.000	0.046
getEnrichRResult	0.509	0.050	3.184
getFindMarkerTopTable	1.555	0.092	1.646
getMSigDBTable	0.002	0.001	0.004
getPathwayResultNames	0.023	0.000	0.023
getSampleSummaryStatsTable	0.222	0.015	0.237
getSoupX	0.000	0.000	0.001
getTSCANResults	1.065	0.084	1.148
getTopHVG	0.875	0.018	0.893
importAnnData	0.001	0.000	0.002
importBUStools	0.143	0.001	0.145
importCellRanger	0.729	0.038	0.766
importCellRangerV2Sample	0.14	0.00	0.14
importCellRangerV3Sample	0.263	0.013	0.276
importDropEst	1.262	0.220	1.483
importExampleData	10.739	1.104	12.339
importGeneSetsFromCollection	1.793	0.188	1.980
importGeneSetsFromGMT	0.058	0.003	0.060
importGeneSetsFromList	0.117	0.000	0.117
importGeneSetsFromMSigDB	44.797	1.776	46.574
importMitoGeneSet	0.050	0.006	0.056
importOptimus	0.001	0.000	0.001
importSEQC	0.142	0.020	0.163
importSTARsolo	0.139	0.028	0.167
iterateSimulations	0.174	0.030	0.204
listSampleSummaryStatsTables	0.258	0.028	0.286
mergeSCEColData	0.384	0.017	0.402
mouseBrainSubsetSCE	0.036	0.000	0.037
msigdb_table	0.001	0.000	0.002
plotBarcodeRankDropsResults	0.872	0.021	0.893
plotBarcodeRankScatter	0.816	0.008	0.823
plotBatchCorrCompare	13.400	0.309	13.892
plotBatchVariance	0.474	0.008	0.483
plotBcdsResults	6.098	0.124	6.304
plotBubble	0.758	0.005	0.763
plotClusterAbundance	1.286	0.040	1.326
plotCxdsResults	7.258	0.134	7.471
plotDEGHeatmap	1.967	0.059	2.026
plotDEGRegression	4.225	0.029	4.248
plotDEGViolin	6.513	0.194	6.702
plotDEGVolcano	0.940	0.002	0.943
plotDecontXResults	8.980	0.128	9.109
plotDimRed	0.276	0.010	0.286
plotDoubletFinderResults	38.885	0.512	39.483
plotEmptyDropsResults	7.669	0.060	7.729
plotEmptyDropsScatter	7.610	0.031	7.641
plotFindMarkerHeatmap	3.667	0.120	3.786
plotMASTThresholdGenes	1.228	0.010	1.238
plotPCA	0.358	0.001	0.358
plotPathway	0.688	0.010	0.699
plotRunPerCellQCResults	3.025	0.004	3.029
plotSCEBarAssayData	0.324	0.002	0.324
plotSCEBarColData	0.217	0.001	0.218
plotSCEBatchFeatureMean	0.367	0.000	0.367
plotSCEDensity	0.302	0.000	0.302
plotSCEDensityAssayData	0.309	0.008	0.316
plotSCEDensityColData	0.296	0.002	0.298
plotSCEDimReduceColData	0.717	0.002	0.719
plotSCEDimReduceFeatures	0.403	0.002	0.405
plotSCEHeatmap	0.412	0.001	0.512
plotSCEScatter	0.358	0.001	0.359
plotSCEViolin	0.396	0.001	0.397
plotSCEViolinAssayData	0.354	0.004	0.357
plotSCEViolinColData	0.349	0.001	0.350
plotScDblFinderResults	28.546	0.762	29.350
plotScanpyDotPlot	0.023	0.000	0.023
plotScanpyEmbedding	0.021	0.001	0.022
plotScanpyHVG	0.020	0.001	0.021
plotScanpyHeatmap	0.022	0.001	0.022
plotScanpyMarkerGenes	0.022	0.000	0.022
plotScanpyMarkerGenesDotPlot	0.022	0.000	0.022
plotScanpyMarkerGenesHeatmap	0.023	0.000	0.023
plotScanpyMarkerGenesMatrixPlot	0.023	0.000	0.023
plotScanpyMarkerGenesViolin	0.023	0.000	0.023
plotScanpyMatrixPlot	0.023	0.000	0.023
plotScanpyPCA	0.022	0.001	0.023
plotScanpyPCAGeneRanking	0.023	0.000	0.023
plotScanpyPCAVariance	0.023	0.000	0.022
plotScanpyViolin	0.023	0.000	0.023
plotScdsHybridResults	7.232	0.119	7.432
plotScrubletResults	0.021	0.000	0.021
plotSeuratElbow	0.021	0.000	0.021
plotSeuratHVG	0.021	0.000	0.020
plotSeuratJackStraw	0.021	0.000	0.021
plotSeuratReduction	0.021	0.000	0.021
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.783	0.006	4.790
plotTSCANClusterPseudo	1.357	0.007	1.364
plotTSCANDimReduceFeatures	1.286	0.001	1.288
plotTSCANPseudotimeGenes	1.593	0.002	1.596
plotTSCANPseudotimeHeatmap	1.321	0.005	1.326
plotTSCANResults	1.198	0.002	1.202
plotTSNE	0.365	0.000	0.366
plotTopHVG	0.644	0.001	0.646
plotUMAP	7.394	0.086	7.557
readSingleCellMatrix	0.005	0.000	0.005
reportCellQC	0.077	0.000	0.078
reportDropletQC	0.022	0.000	0.022
reportQCTool	0.075	0.000	0.075
retrieveSCEIndex	0.027	0.001	0.027
runBBKNN	0	0	0
runBarcodeRankDrops	0.215	0.001	0.216
runBcds	0.073	0.002	0.075
runCellQC	0.076	0.000	0.076
runClusterSummaryMetrics	0.353	0.000	0.353
runComBatSeq	0.415	0.005	0.419
runCxds	0.345	0.000	0.345
runCxdsBcdsHybrid	0.079	0.000	0.080
runDEAnalysis	0.370	0.001	0.371
runDecontX	7.399	0.055	7.455
runDimReduce	0.268	0.000	0.268
runDoubletFinder	35.665	0.294	35.964
runDropletQC	0.023	0.000	0.023
runEmptyDrops	7.364	0.011	7.376
runEnrichR	0.549	0.062	2.843
runFastMNN	1.683	0.127	1.811
runFeatureSelection	0.209	0.011	0.219
runFindMarker	1.383	0.149	1.533
runGSVA	0.870	0.131	1.001
runHarmony	0.041	0.002	0.043
runKMeans	0.170	0.009	0.179
runLimmaBC	0.079	0.010	0.089
runMNNCorrect	0.397	0.036	0.433
runModelGeneVar	0.303	0.028	0.331
runNormalization	2.645	0.695	3.341
runPerCellQC	0.328	0.025	0.353
runSCANORAMA	0	0	0
runSCMerge	0.004	0.000	0.004
runScDblFinder	17.851	1.272	19.073
runScanpyFindClusters	0.021	0.001	0.022
runScanpyFindHVG	0.021	0.000	0.021
runScanpyFindMarkers	0.021	0.000	0.021
runScanpyNormalizeData	0.092	0.000	0.092
runScanpyPCA	0.022	0.000	0.021
runScanpyScaleData	0.021	0.000	0.022
runScanpyTSNE	0.021	0.000	0.022
runScanpyUMAP	0.022	0.000	0.022
runScranSNN	0.279	0.010	0.290
runScrublet	0.022	0.000	0.022
runSeuratFindClusters	0.020	0.001	0.021
runSeuratFindHVG	0.433	0.003	0.436
runSeuratHeatmap	0.022	0.000	0.022
runSeuratICA	0.022	0.000	0.021
runSeuratJackStraw	0.021	0.000	0.022
runSeuratNormalizeData	0.020	0.002	0.021
runSeuratPCA	0.021	0.001	0.022
runSeuratSCTransform	30.172	1.139	31.315
runSeuratScaleData	0.022	0.001	0.023
runSeuratUMAP	0.021	0.001	0.022
runSingleR	0.037	0.000	0.037
runSoupX	0	0	0
runTSCAN	0.655	0.010	0.664
runTSCANClusterDEAnalysis	0.739	0.033	0.772
runTSCANDEG	0.739	0.030	0.769
runTSNE	0.744	0.001	0.745
runUMAP	8.073	0.237	8.390
runVAM	0.294	0.000	0.294
runZINBWaVE	0.003	0.001	0.005
sampleSummaryStats	0.151	0.001	0.152
scaterCPM	0.131	0.017	0.149
scaterPCA	0.414	0.014	0.429
scaterlogNormCounts	0.231	0.006	0.238
sce	0.021	0.001	0.021
sctkListGeneSetCollections	0.074	0.003	0.077
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0.001	0.000	0.000
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.080	0.003	0.083
setSCTKDisplayRow	0.417	0.012	0.428
singleCellTK	0	0	0
subDiffEx	0.311	0.005	0.315
subsetSCECols	0.077	0.000	0.077
subsetSCERows	0.245	0.006	0.251
summarizeSCE	0.064	0.001	0.064
trimCounts	0.200	0.015	0.214