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: 2026-03-04 04:13:21 -0500 (Wed, 04 Mar 2026)

EndedAt: 2026-03-04 04:29:54 -0500 (Wed, 04 Mar 2026)

EllapsedTime: 992.8 seconds

RetCode: 0

Status: OK

CheckDir: singleCellTK.Rcheck

Warnings: 0

Command output

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


* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.2 (2025-10-31)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
    GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
* running under: Ubuntu 24.04.3 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 44.847  0.668  45.515
plotDoubletFinderResults 37.837  0.249  38.171
runDoubletFinder         35.027  0.490  35.519
runSeuratSCTransform     30.035  0.979  31.021
plotScDblFinderResults   27.419  0.875  25.341
runScDblFinder           16.557  0.566  14.164
plotBatchCorrCompare     13.414  0.076  13.672
importExampleData         9.714  0.286  10.431
plotScdsHybridResults     7.735  0.858   8.671
plotDecontXResults        8.127  0.072   8.200
plotUMAP                  7.418  0.463   7.961
plotCxdsResults           7.371  0.073   7.522
runUMAP                   7.102  0.172   7.351
runDecontX                6.838  0.197   7.036
plotEmptyDropsResults     6.613  0.024   6.637
plotEmptyDropsScatter     6.519  0.035   6.555
runEmptyDrops             6.342  0.011   6.354
detectCellOutlier         5.880  0.164   6.045
plotBcdsResults           5.697  0.070   5.841
plotDEGViolin             5.269  0.104   5.368
plotTSCANClusterDEG       4.819  0.198   5.019
* 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 
  0.138   0.039   0.165

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%
[04:27:41] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

[04:27:42] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

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

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

Number of nodes: 390
Number of edges: 9849

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

[ FAIL 0 | WARN 19 | SKIP 0 | PASS 223 ]
> 
> proc.time()
   user  system elapsed 
313.248   8.783 323.163

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.002	0.000	0.002
SEG	0.002	0.000	0.002
calcEffectSizes	0.180	0.005	0.185
combineSCE	0.721	0.000	0.721
computeZScore	0.221	0.011	0.232
convertSCEToSeurat	4.314	0.102	4.416
convertSeuratToSCE	0.337	0.003	0.340
dedupRowNames	0.054	0.000	0.053
detectCellOutlier	5.880	0.164	6.045
diffAbundanceFET	0.051	0.000	0.052
discreteColorPalette	0.005	0.000	0.005
distinctColors	0.002	0.000	0.002
downSampleCells	0.476	0.058	0.535
downSampleDepth	0.427	0.001	0.428
expData-ANY-character-method	0.139	0.001	0.140
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.178	0.000	0.178
expData-set	0.149	0.000	0.149
expData	0.132	0.009	0.141
expDataNames-ANY-method	0.120	0.011	0.130
expDataNames	0.116	0.006	0.122
expDeleteDataTag	0.032	0.002	0.034
expSetDataTag	0.024	0.001	0.025
expTaggedData	0.024	0.000	0.025
exportSCE	0.023	0.000	0.023
exportSCEtoAnnData	0.093	0.008	0.100
exportSCEtoFlatFile	0.085	0.014	0.098
featureIndex	0.034	0.005	0.039
generateSimulatedData	0.076	0.010	0.087
getBiomarker	0.062	0.002	0.064
getDEGTopTable	0.68	0.02	0.70
getDiffAbundanceResults	0.047	0.001	0.048
getEnrichRResult	0.584	0.036	3.391
getFindMarkerTopTable	1.471	0.045	1.516
getMSigDBTable	0.002	0.001	0.003
getPathwayResultNames	0.021	0.001	0.022
getSampleSummaryStatsTable	0.199	0.000	0.198
getSoupX	0.000	0.000	0.001
getTSCANResults	0.966	0.010	0.976
getTopHVG	0.834	0.009	0.843
importAnnData	0.001	0.000	0.002
importBUStools	0.144	0.001	0.145
importCellRanger	0.715	0.007	0.724
importCellRangerV2Sample	0.137	0.001	0.139
importCellRangerV3Sample	0.274	0.001	0.276
importDropEst	1.413	0.061	1.475
importExampleData	9.714	0.286	10.431
importGeneSetsFromCollection	1.742	0.089	1.832
importGeneSetsFromGMT	0.064	0.001	0.065
importGeneSetsFromList	0.121	0.000	0.120
importGeneSetsFromMSigDB	44.847	0.668	45.515
importMitoGeneSet	0.048	0.007	0.054
importOptimus	0.001	0.000	0.001
importSEQC	0.138	0.014	0.153
importSTARsolo	0.144	0.018	0.162
iterateSimulations	0.165	0.029	0.194
listSampleSummaryStatsTables	0.248	0.025	0.272
mergeSCEColData	0.366	0.015	0.381
mouseBrainSubsetSCE	0.035	0.000	0.035
msigdb_table	0.000	0.001	0.001
plotBarcodeRankDropsResults	0.837	0.003	0.840
plotBarcodeRankScatter	2.239	0.001	2.240
plotBatchCorrCompare	13.414	0.076	13.672
plotBatchVariance	0.442	0.008	0.450
plotBcdsResults	5.697	0.070	5.841
plotBubble	0.736	0.001	0.736
plotClusterAbundance	1.276	0.001	1.276
plotCxdsResults	7.371	0.073	7.522
plotDEGHeatmap	2.021	0.052	2.073
plotDEGRegression	4.237	0.006	4.236
plotDEGViolin	5.269	0.104	5.368
plotDEGVolcano	0.879	0.003	0.883
plotDecontXResults	8.127	0.072	8.200
plotDimRed	0.275	0.007	0.282
plotDoubletFinderResults	37.837	0.249	38.171
plotEmptyDropsResults	6.613	0.024	6.637
plotEmptyDropsScatter	6.519	0.035	6.555
plotFindMarkerHeatmap	3.625	0.055	3.680
plotMASTThresholdGenes	1.217	0.024	1.241
plotPCA	0.347	0.001	0.348
plotPathway	0.639	0.001	0.641
plotRunPerCellQCResults	2.831	0.002	2.833
plotSCEBarAssayData	0.303	0.000	0.303
plotSCEBarColData	0.218	0.001	0.218
plotSCEBatchFeatureMean	0.363	0.000	0.363
plotSCEDensity	0.290	0.002	0.292
plotSCEDensityAssayData	0.296	0.001	0.297
plotSCEDensityColData	0.290	0.026	0.317
plotSCEDimReduceColData	0.708	0.023	0.731
plotSCEDimReduceFeatures	0.400	0.008	0.408
plotSCEHeatmap	0.389	0.002	0.391
plotSCEScatter	0.342	0.001	0.343
plotSCEViolin	0.382	0.001	0.383
plotSCEViolinAssayData	0.351	0.003	0.353
plotSCEViolinColData	0.331	0.002	0.333
plotScDblFinderResults	27.419	0.875	25.341
plotScanpyDotPlot	0.021	0.001	0.023
plotScanpyEmbedding	0.022	0.000	0.022
plotScanpyHVG	0.020	0.001	0.021
plotScanpyHeatmap	0.020	0.000	0.021
plotScanpyMarkerGenes	0.021	0.000	0.021
plotScanpyMarkerGenesDotPlot	0.021	0.000	0.021
plotScanpyMarkerGenesHeatmap	0.021	0.000	0.022
plotScanpyMarkerGenesMatrixPlot	0.021	0.000	0.022
plotScanpyMarkerGenesViolin	0.024	0.007	0.031
plotScanpyMatrixPlot	0.022	0.000	0.021
plotScanpyPCA	0.021	0.000	0.021
plotScanpyPCAGeneRanking	0.020	0.001	0.022
plotScanpyPCAVariance	0.021	0.000	0.021
plotScanpyViolin	0.021	0.000	0.021
plotScdsHybridResults	7.735	0.858	8.671
plotScrubletResults	0.021	0.001	0.023
plotSeuratElbow	0.022	0.001	0.023
plotSeuratHVG	0.022	0.000	0.021
plotSeuratJackStraw	0.020	0.001	0.021
plotSeuratReduction	0.021	0.000	0.021
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.819	0.198	5.019
plotTSCANClusterPseudo	1.322	0.048	1.371
plotTSCANDimReduceFeatures	1.283	0.009	1.292
plotTSCANPseudotimeGenes	1.581	0.019	1.600
plotTSCANPseudotimeHeatmap	1.302	0.021	1.323
plotTSCANResults	1.163	0.004	1.167
plotTSNE	0.388	0.014	0.403
plotTopHVG	0.651	0.016	0.666
plotUMAP	7.418	0.463	7.961
readSingleCellMatrix	0.005	0.000	0.005
reportCellQC	0.075	0.001	0.076
reportDropletQC	0.021	0.000	0.021
reportQCTool	0.075	0.001	0.076
retrieveSCEIndex	0.024	0.003	0.027
runBBKNN	0	0	0
runBarcodeRankDrops	0.213	0.008	0.221
runBcds	0.075	0.001	0.075
runCellQC	0.078	0.000	0.078
runClusterSummaryMetrics	0.355	0.006	0.361
runComBatSeq	0.423	0.015	0.439
runCxds	0.357	0.017	0.374
runCxdsBcdsHybrid	0.084	0.000	0.084
runDEAnalysis	0.363	0.027	0.390
runDecontX	6.838	0.197	7.036
runDimReduce	0.269	0.000	0.269
runDoubletFinder	35.027	0.490	35.519
runDropletQC	0.020	0.002	0.022
runEmptyDrops	6.342	0.011	6.354
runEnrichR	0.449	0.038	2.577
runFastMNN	1.655	0.067	1.722
runFeatureSelection	0.207	0.003	0.210
runFindMarker	1.336	0.005	1.341
runGSVA	0.808	0.011	0.820
runHarmony	0.037	0.001	0.037
runKMeans	0.168	0.010	0.178
runLimmaBC	0.073	0.001	0.073
runMNNCorrect	0.370	0.006	0.376
runModelGeneVar	0.28	0.01	0.29
runNormalization	2.492	0.120	2.613
runPerCellQC	0.326	0.004	0.330
runSCANORAMA	0	0	0
runSCMerge	0.003	0.001	0.004
runScDblFinder	16.557	0.566	14.164
runScanpyFindClusters	0.021	0.001	0.022
runScanpyFindHVG	0.021	0.000	0.021
runScanpyFindMarkers	0.021	0.000	0.021
runScanpyNormalizeData	0.091	0.000	0.091
runScanpyPCA	0.023	0.006	0.029
runScanpyScaleData	0.021	0.000	0.021
runScanpyTSNE	0.022	0.000	0.022
runScanpyUMAP	0.021	0.000	0.021
runScranSNN	0.280	0.003	0.283
runScrublet	0.021	0.000	0.021
runSeuratFindClusters	0.021	0.000	0.021
runSeuratFindHVG	0.455	0.003	0.458
runSeuratHeatmap	0.021	0.000	0.022
runSeuratICA	0.021	0.000	0.021
runSeuratJackStraw	0.021	0.000	0.021
runSeuratNormalizeData	0.021	0.000	0.022
runSeuratPCA	0.022	0.000	0.022
runSeuratSCTransform	30.035	0.979	31.021
runSeuratScaleData	0.023	0.000	0.023
runSeuratUMAP	0.020	0.000	0.021
runSingleR	0.037	0.000	0.037
runSoupX	0	0	0
runTSCAN	0.656	0.012	0.669
runTSCANClusterDEAnalysis	0.754	0.004	0.758
runTSCANDEG	0.724	0.022	0.746
runTSNE	0.726	0.003	0.729
runUMAP	7.102	0.172	7.351
runVAM	0.302	0.002	0.304
runZINBWaVE	0.003	0.001	0.004
sampleSummaryStats	0.164	0.001	0.164
scaterCPM	0.138	0.015	0.153
scaterPCA	0.452	0.003	0.455
scaterlogNormCounts	0.235	0.011	0.247
sce	0.021	0.001	0.023
sctkListGeneSetCollections	0.083	0.002	0.085
sctkPythonInstallConda	0.000	0.000	0.001
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0.000	0.000	0.001
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.092	0.002	0.093
setSCTKDisplayRow	0.455	0.008	0.463
singleCellTK	0.000	0.000	0.001
subDiffEx	0.346	0.002	0.347
subsetSCECols	0.087	0.001	0.088
subsetSCERows	0.283	0.009	0.293
summarizeSCE	0.067	0.002	0.069
trimCounts	0.207	0.002	0.209