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: 2026-03-09 04:01:12 -0400 (Mon, 09 Mar 2026)

EndedAt: 2026-03-09 04:17:50 -0400 (Mon, 09 Mar 2026)

EllapsedTime: 998.8 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 44.476  0.459  44.937
plotDoubletFinderResults 38.439  0.336  38.857
runDoubletFinder         34.850  1.295  36.147
runSeuratSCTransform     29.763  0.262  30.034
plotScDblFinderResults   26.859  0.624  24.527
runScDblFinder           16.549  0.612  14.194
plotBatchCorrCompare     12.807  0.174  13.163
importExampleData        10.305  0.379  11.090
plotDecontXResults        8.492  0.066   8.559
plotUMAP                  7.263  0.768   8.110
plotCxdsResults           7.788  0.084   7.951
plotScdsHybridResults     6.925  0.774   7.778
runUMAP                   7.475  0.211   7.767
runDecontX                7.013  0.432   7.448
plotDEGViolin             6.749  0.201   6.944
plotEmptyDropsResults     6.644  0.025   6.669
plotEmptyDropsScatter     6.534  0.037   6.572
runEmptyDrops             6.247  0.017   6.263
detectCellOutlier         5.997  0.167   6.165
plotBcdsResults           5.797  0.060   5.936
plotTSCANClusterDEG       4.849  0.350   5.199
* 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 
  0.153   0.048   0.186

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%
[04:15:37] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

[04:15:38] 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 
319.201   9.359 326.596

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.002	0.001	0.002
SEG	0.001	0.001	0.003
calcEffectSizes	0.186	0.001	0.186
combineSCE	0.714	0.000	0.714
computeZScore	0.227	0.009	0.235
convertSCEToSeurat	4.424	0.071	4.495
convertSeuratToSCE	0.337	0.014	0.352
dedupRowNames	0.055	0.000	0.055
detectCellOutlier	5.997	0.167	6.165
diffAbundanceFET	0.055	0.001	0.056
discreteColorPalette	0.006	0.000	0.006
distinctColors	0.002	0.000	0.002
downSampleCells	0.487	0.065	0.552
downSampleDepth	0.394	0.003	0.397
expData-ANY-character-method	0.12	0.00	0.12
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.153	0.001	0.154
expData-set	0.148	0.000	0.148
expData	0.120	0.001	0.121
expDataNames-ANY-method	0.116	0.000	0.116
expDataNames	0.109	0.009	0.117
expDeleteDataTag	0.030	0.002	0.032
expSetDataTag	0.022	0.002	0.024
expTaggedData	0.024	0.000	0.024
exportSCE	0.021	0.001	0.022
exportSCEtoAnnData	0.090	0.007	0.097
exportSCEtoFlatFile	0.085	0.012	0.097
featureIndex	0.034	0.002	0.036
generateSimulatedData	0.068	0.025	0.094
getBiomarker	0.057	0.002	0.059
getDEGTopTable	0.662	0.021	0.683
getDiffAbundanceResults	0.046	0.002	0.048
getEnrichRResult	0.582	0.037	2.937
getFindMarkerTopTable	1.493	0.060	1.553
getMSigDBTable	0.002	0.002	0.004
getPathwayResultNames	0.021	0.001	0.022
getSampleSummaryStatsTable	0.196	0.008	0.203
getSoupX	0	0	0
getTSCANResults	0.927	0.009	0.937
getTopHVG	0.810	0.005	0.816
importAnnData	0.001	0.001	0.002
importBUStools	0.137	0.002	0.139
importCellRanger	0.690	0.008	0.700
importCellRangerV2Sample	0.134	0.002	0.134
importCellRangerV3Sample	0.256	0.002	0.259
importDropEst	1.158	0.008	1.166
importExampleData	10.305	0.379	11.090
importGeneSetsFromCollection	1.835	0.052	1.888
importGeneSetsFromGMT	0.059	0.002	0.061
importGeneSetsFromList	0.121	0.001	0.124
importGeneSetsFromMSigDB	44.476	0.459	44.937
importMitoGeneSet	0.051	0.003	0.054
importOptimus	0.001	0.000	0.001
importSEQC	0.137	0.018	0.156
importSTARsolo	0.140	0.026	0.167
iterateSimulations	0.167	0.033	0.201
listSampleSummaryStatsTables	0.243	0.028	0.272
mergeSCEColData	0.360	0.011	0.372
mouseBrainSubsetSCE	0.036	0.000	0.036
msigdb_table	0.001	0.001	0.002
plotBarcodeRankDropsResults	0.829	0.002	0.831
plotBarcodeRankScatter	0.800	0.010	0.811
plotBatchCorrCompare	12.807	0.174	13.163
plotBatchVariance	0.450	0.002	0.451
plotBcdsResults	5.797	0.060	5.936
plotBubble	0.767	0.003	0.770
plotClusterAbundance	1.323	0.003	1.326
plotCxdsResults	7.788	0.084	7.951
plotDEGHeatmap	2.056	0.011	2.066
plotDEGRegression	4.390	0.064	4.448
plotDEGViolin	6.749	0.201	6.944
plotDEGVolcano	0.948	0.006	0.953
plotDecontXResults	8.492	0.066	8.559
plotDimRed	0.277	0.001	0.278
plotDoubletFinderResults	38.439	0.336	38.857
plotEmptyDropsResults	6.644	0.025	6.669
plotEmptyDropsScatter	6.534	0.037	6.572
plotFindMarkerHeatmap	3.554	0.037	3.591
plotMASTThresholdGenes	1.184	0.006	1.190
plotPCA	0.346	0.009	0.355
plotPathway	0.659	0.033	0.693
plotRunPerCellQCResults	2.894	0.012	2.906
plotSCEBarAssayData	0.334	0.001	0.335
plotSCEBarColData	0.243	0.002	0.246
plotSCEBatchFeatureMean	0.387	0.000	0.387
plotSCEDensity	0.307	0.000	0.307
plotSCEDensityAssayData	0.319	0.001	0.321
plotSCEDensityColData	0.297	0.003	0.300
plotSCEDimReduceColData	0.729	0.003	0.733
plotSCEDimReduceFeatures	0.422	0.002	0.424
plotSCEHeatmap	0.408	0.013	0.421
plotSCEScatter	0.345	0.015	0.360
plotSCEViolin	0.410	0.027	0.437
plotSCEViolinAssayData	0.368	0.003	0.371
plotSCEViolinColData	0.345	0.002	0.347
plotScDblFinderResults	26.859	0.624	24.527
plotScanpyDotPlot	0.022	0.000	0.022
plotScanpyEmbedding	0.022	0.000	0.022
plotScanpyHVG	0.021	0.000	0.021
plotScanpyHeatmap	0.022	0.000	0.022
plotScanpyMarkerGenes	0.022	0.000	0.022
plotScanpyMarkerGenesDotPlot	0.022	0.000	0.022
plotScanpyMarkerGenesHeatmap	0.022	0.000	0.021
plotScanpyMarkerGenesMatrixPlot	0.021	0.000	0.021
plotScanpyMarkerGenesViolin	0.021	0.000	0.021
plotScanpyMatrixPlot	0.020	0.001	0.021
plotScanpyPCA	0.020	0.001	0.021
plotScanpyPCAGeneRanking	0.020	0.001	0.022
plotScanpyPCAVariance	0.020	0.001	0.021
plotScanpyViolin	0.020	0.001	0.021
plotScdsHybridResults	6.925	0.774	7.778
plotScrubletResults	0.021	0.001	0.022
plotSeuratElbow	0.021	0.000	0.021
plotSeuratHVG	0.021	0.000	0.021
plotSeuratJackStraw	0.021	0.000	0.022
plotSeuratReduction	0.020	0.001	0.020
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.849	0.350	5.199
plotTSCANClusterPseudo	1.388	0.158	1.546
plotTSCANDimReduceFeatures	1.301	0.075	1.377
plotTSCANPseudotimeGenes	1.64	0.13	1.77
plotTSCANPseudotimeHeatmap	1.363	0.104	1.467
plotTSCANResults	1.214	0.031	1.245
plotTSNE	0.373	0.010	0.382
plotTopHVG	0.658	0.017	0.675
plotUMAP	7.263	0.768	8.110
readSingleCellMatrix	0.005	0.000	0.005
reportCellQC	0.078	0.001	0.079
reportDropletQC	0.022	0.000	0.022
reportQCTool	0.078	0.000	0.079
retrieveSCEIndex	0.027	0.001	0.029
runBBKNN	0	0	0
runBarcodeRankDrops	0.226	0.007	0.232
runBcds	0.080	0.000	0.079
runCellQC	0.073	0.001	0.074
runClusterSummaryMetrics	0.349	0.017	0.366
runComBatSeq	0.408	0.017	0.424
runCxds	0.339	0.012	0.352
runCxdsBcdsHybrid	0.078	0.000	0.078
runDEAnalysis	0.372	0.026	0.397
runDecontX	7.013	0.432	7.448
runDimReduce	0.263	0.003	0.267
runDoubletFinder	34.850	1.295	36.147
runDropletQC	0.022	0.000	0.022
runEmptyDrops	6.247	0.017	6.263
runEnrichR	0.482	0.061	2.655
runFastMNN	1.658	0.091	1.750
runFeatureSelection	0.208	0.000	0.208
runFindMarker	1.389	0.025	1.415
runGSVA	0.827	0.035	0.862
runHarmony	0.036	0.002	0.038
runKMeans	0.166	0.002	0.168
runLimmaBC	0.075	0.000	0.075
runMNNCorrect	0.393	0.000	0.394
runModelGeneVar	0.306	0.006	0.312
runNormalization	2.578	0.273	2.851
runPerCellQC	0.334	0.016	0.350
runSCANORAMA	0	0	0
runSCMerge	0.004	0.001	0.004
runScDblFinder	16.549	0.612	14.194
runScanpyFindClusters	0.021	0.001	0.022
runScanpyFindHVG	0.021	0.001	0.022
runScanpyFindMarkers	0.021	0.000	0.021
runScanpyNormalizeData	0.096	0.000	0.096
runScanpyPCA	0.021	0.000	0.022
runScanpyScaleData	0.022	0.000	0.022
runScanpyTSNE	0.022	0.000	0.021
runScanpyUMAP	0.022	0.000	0.022
runScranSNN	0.284	0.000	0.285
runScrublet	0.022	0.000	0.021
runSeuratFindClusters	0.022	0.000	0.021
runSeuratFindHVG	0.432	0.001	0.434
runSeuratHeatmap	0.021	0.000	0.021
runSeuratICA	0.021	0.000	0.021
runSeuratJackStraw	0.021	0.000	0.022
runSeuratNormalizeData	0.021	0.000	0.022
runSeuratPCA	0.021	0.000	0.021
runSeuratSCTransform	29.763	0.262	30.034
runSeuratScaleData	0.022	0.001	0.023
runSeuratUMAP	0.022	0.000	0.021
runSingleR	0.04	0.00	0.04
runSoupX	0	0	0
runTSCAN	0.693	0.004	0.697
runTSCANClusterDEAnalysis	0.792	0.005	0.797
runTSCANDEG	0.807	0.016	0.824
runTSNE	0.734	0.001	0.736
runUMAP	7.475	0.211	7.767
runVAM	0.289	0.001	0.290
runZINBWaVE	0.004	0.000	0.004
sampleSummaryStats	0.149	0.000	0.150
scaterCPM	0.130	0.008	0.137
scaterPCA	0.429	0.001	0.430
scaterlogNormCounts	0.227	0.005	0.232
sce	0.020	0.001	0.021
sctkListGeneSetCollections	0.077	0.001	0.078
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.080	0.004	0.084
setSCTKDisplayRow	0.428	0.004	0.432
singleCellTK	0.001	0.000	0.000
subDiffEx	0.351	0.004	0.355
subsetSCECols	0.086	0.002	0.088
subsetSCERows	0.267	0.022	0.289
summarizeSCE	0.074	0.001	0.075
trimCounts	0.212	0.007	0.220