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"	4891
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: 2026-02-28 04:11:44 -0500 (Sat, 28 Feb 2026)

EndedAt: 2026-02-28 04:29:12 -0500 (Sat, 28 Feb 2026)

EllapsedTime: 1047.8 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 44.995  0.708  45.703
plotDoubletFinderResults 41.682  0.285  42.046
runDoubletFinder         36.988  1.149  38.141
runSeuratSCTransform     30.958  2.089  33.054
plotScDblFinderResults   28.090  1.115  26.262
runScDblFinder           17.545  0.909  15.436
plotBatchCorrCompare     14.326  0.099  14.611
importExampleData        10.674  0.350  11.452
runUMAP                   9.231  0.512   9.822
plotDecontXResults        9.628  0.061   9.688
plotScdsHybridResults     7.777  1.074   8.929
plotUMAP                  7.600  0.705   8.381
runDecontX                7.581  0.443   8.024
plotCxdsResults           7.634  0.098   7.813
plotEmptyDropsResults     6.670  0.025   6.695
detectCellOutlier         6.493  0.166   6.659
plotEmptyDropsScatter     6.579  0.048   6.627
plotBcdsResults           6.312  0.064   6.454
runEmptyDrops             6.268  0.038   6.306
plotDEGViolin             5.760  0.139   5.893
plotTSCANClusterDEG       4.976  0.213   5.191
* 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 
  0.152   0.037   0.177

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%
[04:26:57] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

[04:26:58] 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 
327.282   9.114 338.255

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.002	0.000	0.002
SEG	0.002	0.000	0.003
calcEffectSizes	0.178	0.003	0.181
combineSCE	0.715	0.002	0.718
computeZScore	0.232	0.008	0.242
convertSCEToSeurat	4.639	0.109	4.749
convertSeuratToSCE	0.328	0.003	0.332
dedupRowNames	0.053	0.001	0.054
detectCellOutlier	6.493	0.166	6.659
diffAbundanceFET	0.053	0.001	0.054
discreteColorPalette	0.004	0.001	0.006
distinctColors	0.002	0.000	0.002
downSampleCells	0.489	0.064	0.552
downSampleDepth	0.406	0.002	0.408
expData-ANY-character-method	0.127	0.018	0.145
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.162	0.008	0.170
expData-set	0.151	0.003	0.153
expData	0.124	0.001	0.124
expDataNames-ANY-method	0.114	0.008	0.122
expDataNames	0.115	0.007	0.122
expDeleteDataTag	0.030	0.003	0.033
expSetDataTag	0.021	0.003	0.024
expTaggedData	0.024	0.000	0.024
exportSCE	0.021	0.001	0.022
exportSCEtoAnnData	0.092	0.006	0.098
exportSCEtoFlatFile	0.089	0.009	0.098
featureIndex	0.035	0.001	0.036
generateSimulatedData	0.074	0.005	0.078
getBiomarker	0.060	0.001	0.061
getDEGTopTable	0.646	0.020	0.666
getDiffAbundanceResults	0.049	0.000	0.050
getEnrichRResult	0.564	0.035	3.921
getFindMarkerTopTable	1.545	0.054	1.599
getMSigDBTable	0.003	0.000	0.003
getPathwayResultNames	0.023	0.000	0.022
getSampleSummaryStatsTable	0.203	0.000	0.203
getSoupX	0	0	0
getTSCANResults	0.963	0.006	0.969
getTopHVG	0.849	0.009	0.859
importAnnData	0.002	0.000	0.002
importBUStools	0.148	0.000	0.150
importCellRanger	0.725	0.006	0.734
importCellRangerV2Sample	0.134	0.001	0.135
importCellRangerV3Sample	0.267	0.005	0.271
importDropEst	1.337	0.079	1.416
importExampleData	10.674	0.350	11.452
importGeneSetsFromCollection	1.772	0.024	1.796
importGeneSetsFromGMT	0.061	0.001	0.062
importGeneSetsFromList	0.124	0.001	0.125
importGeneSetsFromMSigDB	44.995	0.708	45.703
importMitoGeneSet	0.051	0.004	0.055
importOptimus	0.001	0.000	0.001
importSEQC	0.133	0.024	0.157
importSTARsolo	0.135	0.030	0.166
iterateSimulations	0.172	0.029	0.201
listSampleSummaryStatsTables	0.247	0.035	0.283
mergeSCEColData	0.374	0.013	0.388
mouseBrainSubsetSCE	0.035	0.000	0.035
msigdb_table	0.000	0.001	0.001
plotBarcodeRankDropsResults	0.830	0.008	0.838
plotBarcodeRankScatter	2.267	0.000	2.267
plotBatchCorrCompare	14.326	0.099	14.611
plotBatchVariance	0.487	0.009	0.496
plotBcdsResults	6.312	0.064	6.454
plotBubble	0.804	0.026	0.830
plotClusterAbundance	1.359	0.009	1.369
plotCxdsResults	7.634	0.098	7.813
plotDEGHeatmap	2.073	0.004	2.078
plotDEGRegression	4.329	0.056	4.379
plotDEGViolin	5.760	0.139	5.893
plotDEGVolcano	0.890	0.002	0.892
plotDecontXResults	9.628	0.061	9.688
plotDimRed	0.278	0.001	0.279
plotDoubletFinderResults	41.682	0.285	42.046
plotEmptyDropsResults	6.670	0.025	6.695
plotEmptyDropsScatter	6.579	0.048	6.627
plotFindMarkerHeatmap	3.891	0.081	3.972
plotMASTThresholdGenes	1.223	0.012	1.234
plotPCA	0.369	0.003	0.372
plotPathway	0.703	0.008	0.712
plotRunPerCellQCResults	3.111	0.015	3.126
plotSCEBarAssayData	0.335	0.003	0.338
plotSCEBarColData	0.224	0.000	0.224
plotSCEBatchFeatureMean	0.397	0.000	0.398
plotSCEDensity	0.330	0.001	0.332
plotSCEDensityAssayData	0.330	0.000	0.331
plotSCEDensityColData	0.301	0.001	0.302
plotSCEDimReduceColData	0.726	0.001	0.727
plotSCEDimReduceFeatures	0.395	0.003	0.398
plotSCEHeatmap	0.407	0.000	0.407
plotSCEScatter	0.347	0.001	0.349
plotSCEViolin	0.388	0.000	0.388
plotSCEViolinAssayData	0.366	0.000	0.366
plotSCEViolinColData	0.346	0.003	0.349
plotScDblFinderResults	28.090	1.115	26.262
plotScanpyDotPlot	0.021	0.000	0.022
plotScanpyEmbedding	0.021	0.000	0.022
plotScanpyHVG	0.020	0.001	0.022
plotScanpyHeatmap	0.021	0.000	0.021
plotScanpyMarkerGenes	0.021	0.000	0.021
plotScanpyMarkerGenesDotPlot	0.020	0.000	0.021
plotScanpyMarkerGenesHeatmap	0.021	0.000	0.021
plotScanpyMarkerGenesMatrixPlot	0.021	0.000	0.022
plotScanpyMarkerGenesViolin	0.021	0.000	0.021
plotScanpyMatrixPlot	0.021	0.000	0.021
plotScanpyPCA	0.020	0.001	0.021
plotScanpyPCAGeneRanking	0.022	0.000	0.021
plotScanpyPCAVariance	0.022	0.000	0.022
plotScanpyViolin	0.021	0.000	0.021
plotScdsHybridResults	7.777	1.074	8.929
plotScrubletResults	0.024	0.000	0.023
plotSeuratElbow	0.022	0.000	0.022
plotSeuratHVG	0.023	0.000	0.022
plotSeuratJackStraw	0.021	0.001	0.022
plotSeuratReduction	0.022	0.000	0.022
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.976	0.213	5.191
plotTSCANClusterPseudo	1.433	0.078	1.513
plotTSCANDimReduceFeatures	1.412	0.063	1.476
plotTSCANPseudotimeGenes	1.708	0.124	1.833
plotTSCANPseudotimeHeatmap	1.453	0.061	1.515
plotTSCANResults	1.264	0.062	1.327
plotTSNE	0.393	0.009	0.403
plotTopHVG	0.714	0.046	0.760
plotUMAP	7.600	0.705	8.381
readSingleCellMatrix	0.005	0.000	0.005
reportCellQC	0.077	0.002	0.079
reportDropletQC	0.023	0.009	0.031
reportQCTool	0.080	0.002	0.082
retrieveSCEIndex	0.027	0.001	0.027
runBBKNN	0	0	0
runBarcodeRankDrops	0.225	0.009	0.235
runBcds	0.079	0.003	0.082
runCellQC	0.083	0.003	0.087
runClusterSummaryMetrics	0.377	0.010	0.387
runComBatSeq	0.430	0.020	0.451
runCxds	0.376	0.014	0.390
runCxdsBcdsHybrid	0.087	0.001	0.087
runDEAnalysis	0.388	0.014	0.402
runDecontX	7.581	0.443	8.024
runDimReduce	0.271	0.004	0.275
runDoubletFinder	36.988	1.149	38.141
runDropletQC	0.022	0.001	0.023
runEmptyDrops	6.268	0.038	6.306
runEnrichR	0.548	0.111	2.901
runFastMNN	1.847	0.160	2.008
runFeatureSelection	0.223	0.009	0.232
runFindMarker	1.493	0.044	1.536
runGSVA	0.914	0.030	0.944
runHarmony	0.045	0.000	0.045
runKMeans	0.175	0.001	0.177
runLimmaBC	0.077	0.000	0.078
runMNNCorrect	0.407	0.008	0.415
runModelGeneVar	0.322	0.000	0.321
runNormalization	2.687	0.186	2.873
runPerCellQC	0.346	0.003	0.348
runSCANORAMA	0	0	0
runSCMerge	0.003	0.000	0.004
runScDblFinder	17.545	0.909	15.436
runScanpyFindClusters	0.022	0.002	0.023
runScanpyFindHVG	0.023	0.000	0.023
runScanpyFindMarkers	0.024	0.000	0.024
runScanpyNormalizeData	0.099	0.000	0.099
runScanpyPCA	0.023	0.000	0.023
runScanpyScaleData	0.021	0.001	0.022
runScanpyTSNE	0.022	0.000	0.023
runScanpyUMAP	0.022	0.001	0.023
runScranSNN	0.301	0.001	0.301
runScrublet	0.023	0.000	0.023
runSeuratFindClusters	0.023	0.000	0.023
runSeuratFindHVG	0.478	0.003	0.482
runSeuratHeatmap	0.022	0.000	0.023
runSeuratICA	0.023	0.000	0.023
runSeuratJackStraw	0.021	0.001	0.022
runSeuratNormalizeData	0.021	0.001	0.022
runSeuratPCA	0.022	0.001	0.023
runSeuratSCTransform	30.958	2.089	33.054
runSeuratScaleData	0.023	0.001	0.024
runSeuratUMAP	0.023	0.001	0.024
runSingleR	0.034	0.007	0.041
runSoupX	0	0	0
runTSCAN	0.687	0.007	0.694
runTSCANClusterDEAnalysis	0.810	0.055	0.866
runTSCANDEG	0.826	0.067	0.893
runTSNE	0.752	0.014	0.765
runUMAP	9.231	0.512	9.822
runVAM	0.321	0.019	0.340
runZINBWaVE	0.005	0.000	0.004
sampleSummaryStats	0.168	0.001	0.169
scaterCPM	0.144	0.005	0.149
scaterPCA	0.493	0.004	0.498
scaterlogNormCounts	0.264	0.008	0.273
sce	0.022	0.001	0.023
sctkListGeneSetCollections	0.089	0.004	0.093
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.102	0.004	0.106
setSCTKDisplayRow	0.457	0.044	0.501
singleCellTK	0	0	0
subDiffEx	0.346	0.017	0.362
subsetSCECols	0.086	0.001	0.088
subsetSCERows	0.287	0.011	0.297
summarizeSCE	0.072	0.003	0.076
trimCounts	0.224	0.026	0.251