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"	4897
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: 2026-04-08 04:06:27 -0400 (Wed, 08 Apr 2026)

EndedAt: 2026-04-08 04:21:43 -0400 (Wed, 08 Apr 2026)

EllapsedTime: 915.8 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 24.04.4 LTS
* using session charset: UTF-8
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.20.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 80 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable.  Move as many as possible to Suggests and
use conditionally.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... INFO
  installed size is  7.0Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    extdata   1.6Mb
    shiny     3.0Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
  dedupRowNames.Rd: SingleCellExperiment-class
  detectCellOutlier.Rd: colData
  diffAbundanceFET.Rd: colData
  downSampleCells.Rd: SingleCellExperiment-class
  downSampleDepth.Rd: SingleCellExperiment-class
  featureIndex.Rd: SummarizedExperiment-class,
    SingleCellExperiment-class
  getBiomarker.Rd: SingleCellExperiment-class
  getDEGTopTable.Rd: SingleCellExperiment-class
  getEnrichRResult.Rd: SingleCellExperiment-class
  getFindMarkerTopTable.Rd: SingleCellExperiment-class
  getGenesetNamesFromCollection.Rd: SingleCellExperiment-class
  getPathwayResultNames.Rd: SingleCellExperiment-class
  getSampleSummaryStatsTable.Rd: SingleCellExperiment-class, assay,
    colData
  getSoupX.Rd: SingleCellExperiment-class
  getTSCANResults.Rd: SingleCellExperiment-class
  getTopHVG.Rd: SingleCellExperiment-class
  importAlevin.Rd: DelayedArray, readMM
  importAnnData.Rd: DelayedArray, readMM
  importBUStools.Rd: readMM
  importCellRanger.Rd: readMM, DelayedArray
  importCellRangerV2Sample.Rd: readMM, DelayedArray
  importCellRangerV3Sample.Rd: readMM, DelayedArray
  importDropEst.Rd: DelayedArray, readMM
  importExampleData.Rd: scRNAseq, Matrix, DelayedArray,
    ReprocessedFluidigmData, ReprocessedAllenData, NestorowaHSCData
  importFromFiles.Rd: readMM, DelayedArray, SingleCellExperiment-class
  importGeneSetsFromCollection.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GeneSetCollection, GSEABase, metadata
  importGeneSetsFromGMT.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, getGmt, GSEABase, metadata
  importGeneSetsFromList.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GSEABase, metadata
  importGeneSetsFromMSigDB.Rd: SingleCellExperiment-class, msigdbr,
    GeneSetCollection-class, GSEABase, metadata
  importMitoGeneSet.Rd: SingleCellExperiment-class,
    GeneSetCollection-class, GSEABase, metadata
  importMultipleSources.Rd: DelayedArray
  importOptimus.Rd: readMM, DelayedArray
  importSEQC.Rd: readMM, DelayedArray
  importSTARsolo.Rd: readMM, DelayedArray
  iterateSimulations.Rd: SingleCellExperiment-class
  listSampleSummaryStatsTables.Rd: SingleCellExperiment-class, metadata
  plotBarcodeRankDropsResults.Rd: SingleCellExperiment-class
  plotBarcodeRankScatter.Rd: SingleCellExperiment-class
  plotBatchCorrCompare.Rd: SingleCellExperiment-class
  plotBatchVariance.Rd: SingleCellExperiment-class
  plotBcdsResults.Rd: SingleCellExperiment-class
  plotClusterAbundance.Rd: colData
  plotCxdsResults.Rd: SingleCellExperiment-class
  plotDEGHeatmap.Rd: SingleCellExperiment-class
  plotDEGRegression.Rd: SingleCellExperiment-class
  plotDEGViolin.Rd: SingleCellExperiment-class
  plotDEGVolcano.Rd: SingleCellExperiment-class
  plotDecontXResults.Rd: SingleCellExperiment-class
  plotDoubletFinderResults.Rd: SingleCellExperiment-class
  plotEmptyDropsResults.Rd: SingleCellExperiment-class
  plotEmptyDropsScatter.Rd: SingleCellExperiment-class
  plotEnrichR.Rd: SingleCellExperiment-class
  plotFindMarkerHeatmap.Rd: SingleCellExperiment-class
  plotPCA.Rd: SingleCellExperiment-class
  plotPathway.Rd: SingleCellExperiment-class
  plotRunPerCellQCResults.Rd: SingleCellExperiment-class
  plotSCEBarAssayData.Rd: SingleCellExperiment-class
  plotSCEBarColData.Rd: SingleCellExperiment-class
  plotSCEBatchFeatureMean.Rd: SingleCellExperiment-class
  plotSCEDensity.Rd: SingleCellExperiment-class
  plotSCEDensityAssayData.Rd: SingleCellExperiment-class
  plotSCEDensityColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceFeatures.Rd: SingleCellExperiment-class
  plotSCEHeatmap.Rd: SingleCellExperiment-class
  plotSCEScatter.Rd: SingleCellExperiment-class
  plotSCEViolin.Rd: SingleCellExperiment-class
  plotSCEViolinAssayData.Rd: SingleCellExperiment-class
  plotSCEViolinColData.Rd: SingleCellExperiment-class
  plotScDblFinderResults.Rd: SingleCellExperiment-class
  plotScdsHybridResults.Rd: SingleCellExperiment-class
  plotScrubletResults.Rd: SingleCellExperiment-class
  plotSoupXResults.Rd: SingleCellExperiment-class
  plotTSCANClusterDEG.Rd: SingleCellExperiment-class
  plotTSCANClusterPseudo.Rd: SingleCellExperiment-class
  plotTSCANDimReduceFeatures.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeGenes.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeHeatmap.Rd: SingleCellExperiment-class
  plotTSCANResults.Rd: SingleCellExperiment-class
  plotTSNE.Rd: SingleCellExperiment-class
  plotUMAP.Rd: SingleCellExperiment-class
  readSingleCellMatrix.Rd: DelayedArray
  reportCellQC.Rd: SingleCellExperiment-class
  reportClusterAbundance.Rd: colData
  reportDiffAbundanceFET.Rd: colData
  retrieveSCEIndex.Rd: SingleCellExperiment-class
  runBBKNN.Rd: SingleCellExperiment-class
  runBarcodeRankDrops.Rd: SingleCellExperiment-class, colData
  runBcds.Rd: SingleCellExperiment-class, colData
  runCellQC.Rd: colData
  runComBatSeq.Rd: SingleCellExperiment-class
  runCxds.Rd: SingleCellExperiment-class, colData
  runCxdsBcdsHybrid.Rd: colData
  runDEAnalysis.Rd: SingleCellExperiment-class
  runDecontX.Rd: colData
  runDimReduce.Rd: SingleCellExperiment-class
  runDoubletFinder.Rd: SingleCellExperiment-class
  runDropletQC.Rd: colData
  runEmptyDrops.Rd: SingleCellExperiment-class, colData
  runEnrichR.Rd: SingleCellExperiment-class
  runFastMNN.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runFeatureSelection.Rd: SingleCellExperiment-class
  runFindMarker.Rd: SingleCellExperiment-class
  runGSVA.Rd: SingleCellExperiment-class
  runHarmony.Rd: SingleCellExperiment-class
  runKMeans.Rd: SingleCellExperiment-class, colData
  runLimmaBC.Rd: SingleCellExperiment-class, assay
  runMNNCorrect.Rd: SingleCellExperiment-class, assay,
    BiocParallelParam-class
  runModelGeneVar.Rd: SingleCellExperiment-class
  runPerCellQC.Rd: SingleCellExperiment-class, BiocParallelParam,
    colData
  runSCANORAMA.Rd: SingleCellExperiment-class, assay
  runSCMerge.Rd: SingleCellExperiment-class, colData, assay,
    BiocParallelParam-class
  runScDblFinder.Rd: SingleCellExperiment-class, colData
  runScranSNN.Rd: SingleCellExperiment-class, reducedDim, assay,
    altExp, colData, igraph
  runScrublet.Rd: SingleCellExperiment-class, colData
  runSingleR.Rd: SingleCellExperiment-class
  runSoupX.Rd: SingleCellExperiment-class
  runTSCAN.Rd: SingleCellExperiment-class
  runTSCANClusterDEAnalysis.Rd: SingleCellExperiment-class
  runTSCANDEG.Rd: SingleCellExperiment-class
  runTSNE.Rd: SingleCellExperiment-class
  runUMAP.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runVAM.Rd: SingleCellExperiment-class
  runZINBWaVE.Rd: SingleCellExperiment-class, colData,
    BiocParallelParam-class
  sampleSummaryStats.Rd: SingleCellExperiment-class, assay, colData
  scaterPCA.Rd: SingleCellExperiment-class, BiocParallelParam-class
  scaterlogNormCounts.Rd: logNormCounts
  sctkListGeneSetCollections.Rd: GeneSetCollection-class
  sctkPythonInstallConda.Rd: conda_install, reticulate, conda_create
  sctkPythonInstallVirtualEnv.Rd: virtualenv_install, reticulate,
    virtualenv_create
  selectSCTKConda.Rd: reticulate
  selectSCTKVirtualEnvironment.Rd: reticulate
  setRowNames.Rd: SingleCellExperiment-class
  setSCTKDisplayRow.Rd: SingleCellExperiment-class
  singleCellTK.Rd: SingleCellExperiment-class
  subsetSCECols.Rd: SingleCellExperiment-class
  subsetSCERows.Rd: SingleCellExperiment-class, altExp
  summarizeSCE.Rd: SingleCellExperiment-class
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
plotDoubletFinderResults 36.123  1.515  37.640
runDoubletFinder         32.612  1.163  33.774
plotScDblFinderResults   30.344  1.593  28.858
runSeuratSCTransform     29.540  1.728  31.276
runScDblFinder           16.602  0.717  14.280
plotBatchCorrCompare     11.492  0.196  11.688
importExampleData        10.096  0.396  11.092
plotDecontXResults        7.805  0.004   7.809
runUMAP                   6.838  0.408   7.246
plotUMAP                  6.613  0.629   7.241
plotScdsHybridResults     6.273  0.869   7.141
runDecontX                6.722  0.393   7.115
plotEmptyDropsScatter     6.635  0.128   6.763
plotEmptyDropsResults     6.631  0.099   6.730
plotCxdsResults           6.484  0.087   6.570
detectCellOutlier         6.303  0.156   6.459
runEmptyDrops             6.265  0.033   6.298
plotDEGViolin             5.491  0.132   5.617
plotBcdsResults           5.170  0.078   5.248
plotTSCANClusterDEG       4.955  0.287   5.242
getEnrichRResult          0.480  0.029   8.081
* 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 
  0.144   0.041   0.173

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%
[04:19:38] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

[04:19:39] 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 
272.758   4.001 276.641

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.002	0.002	0.002
SEG	0.002	0.000	0.002
calcEffectSizes	0.197	0.002	0.199
combineSCE	0.738	0.001	0.739
computeZScore	0.224	0.004	0.229
convertSCEToSeurat	4.361	0.101	4.462
convertSeuratToSCE	0.331	0.010	0.342
dedupRowNames	0.056	0.000	0.056
detectCellOutlier	6.303	0.156	6.459
diffAbundanceFET	0.050	0.000	0.051
discreteColorPalette	0.005	0.000	0.005
distinctColors	0.002	0.001	0.002
downSampleCells	0.478	0.057	0.536
downSampleDepth	0.403	0.002	0.405
expData-ANY-character-method	0.122	0.000	0.123
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.158	0.001	0.159
expData-set	0.143	0.000	0.144
expData	0.123	0.008	0.131
expDataNames-ANY-method	0.113	0.013	0.128
expDataNames	0.113	0.012	0.126
expDeleteDataTag	0.031	0.003	0.034
expSetDataTag	0.022	0.003	0.025
expTaggedData	0.024	0.000	0.026
exportSCE	0.022	0.001	0.023
exportSCEtoAnnData	0.092	0.009	0.101
exportSCEtoFlatFile	0.088	0.010	0.098
featureIndex	0.032	0.005	0.037
generateSimulatedData	0.072	0.008	0.081
getBiomarker	0.06	0.00	0.06
getDEGTopTable	0.643	0.026	0.669
getDiffAbundanceResults	0.046	0.000	0.046
getEnrichRResult	0.480	0.029	8.081
getFindMarkerTopTable	1.453	0.044	1.498
getMSigDBTable	0.003	0.000	0.003
getPathwayResultNames	0.020	0.002	0.021
getSampleSummaryStatsTable	0.186	0.000	0.186
getSoupX	0	0	0
getTSCANResults	0.918	0.008	0.925
getTopHVG	0.811	0.009	0.819
importAnnData	0.001	0.000	0.001
importBUStools	0.139	0.001	0.140
importCellRanger	0.722	0.011	0.733
importCellRangerV2Sample	0.144	0.000	0.144
importCellRangerV3Sample	0.276	0.004	0.281
importDropEst	1.338	0.010	1.348
importExampleData	10.096	0.396	11.092
importGeneSetsFromCollection	1.722	0.077	1.798
importGeneSetsFromGMT	0.061	0.000	0.062
importGeneSetsFromList	0.119	0.000	0.120
importGeneSetsFromMSigDB	0.978	0.079	1.057
importMitoGeneSet	0.048	0.007	0.055
importOptimus	0.002	0.000	0.002
importSEQC	0.133	0.022	0.155
importSTARsolo	0.182	0.009	0.192
iterateSimulations	0.167	0.000	0.167
listSampleSummaryStatsTables	0.235	0.002	0.237
mergeSCEColData	0.325	0.007	0.333
mouseBrainSubsetSCE	0.033	0.001	0.034
msigdb_table	0.001	0.000	0.002
plotBarcodeRankDropsResults	0.851	0.005	0.857
plotBarcodeRankScatter	0.785	0.001	0.786
plotBatchCorrCompare	11.492	0.196	11.688
plotBatchVariance	0.442	0.011	0.453
plotBcdsResults	5.170	0.078	5.248
plotBubble	0.776	0.003	0.780
plotClusterAbundance	1.306	0.003	1.308
plotCxdsResults	6.484	0.087	6.570
plotDEGHeatmap	1.988	0.006	1.993
plotDEGRegression	4.251	0.030	4.275
plotDEGViolin	5.491	0.132	5.617
plotDEGVolcano	0.898	0.001	0.898
plotDecontXResults	7.805	0.004	7.809
plotDimRed	0.275	0.000	0.274
plotDoubletFinderResults	36.123	1.515	37.640
plotEmptyDropsResults	6.631	0.099	6.730
plotEmptyDropsScatter	6.635	0.128	6.763
plotFindMarkerHeatmap	3.854	0.276	4.131
plotMASTThresholdGenes	1.209	0.034	1.243
plotPCA	0.368	0.000	0.368
plotPathway	0.633	0.004	0.638
plotRunPerCellQCResults	2.919	0.034	2.953
plotSCEBarAssayData	0.264	0.007	0.271
plotSCEBarColData	0.223	0.013	0.237
plotSCEBatchFeatureMean	0.38	0.01	0.39
plotSCEDensity	0.323	0.006	0.329
plotSCEDensityAssayData	0.257	0.021	0.278
plotSCEDensityColData	0.298	0.010	0.308
plotSCEDimReduceColData	0.744	0.015	0.759
plotSCEDimReduceFeatures	0.363	0.021	0.384
plotSCEHeatmap	0.407	0.025	0.432
plotSCEScatter	0.371	0.019	0.389
plotSCEViolin	0.355	0.022	0.377
plotSCEViolinAssayData	0.373	0.011	0.383
plotSCEViolinColData	0.355	0.010	0.365
plotScDblFinderResults	30.344	1.593	28.858
plotScanpyDotPlot	0.023	0.000	0.023
plotScanpyEmbedding	0.023	0.000	0.022
plotScanpyHVG	0.022	0.000	0.021
plotScanpyHeatmap	0.022	0.000	0.021
plotScanpyMarkerGenes	0.021	0.000	0.021
plotScanpyMarkerGenesDotPlot	0.022	0.000	0.022
plotScanpyMarkerGenesHeatmap	0.025	0.010	0.035
plotScanpyMarkerGenesMatrixPlot	0.021	0.003	0.025
plotScanpyMarkerGenesViolin	0.021	0.001	0.023
plotScanpyMatrixPlot	0.021	0.001	0.022
plotScanpyPCA	0.022	0.000	0.022
plotScanpyPCAGeneRanking	0.022	0.000	0.022
plotScanpyPCAVariance	0.023	0.000	0.023
plotScanpyViolin	0.022	0.000	0.022
plotScdsHybridResults	6.273	0.869	7.141
plotScrubletResults	0.021	0.002	0.023
plotSeuratElbow	0.022	0.000	0.022
plotSeuratHVG	0.023	0.000	0.023
plotSeuratJackStraw	0.023	0.000	0.023
plotSeuratReduction	0.026	0.012	0.037
plotSoupXResults	0	0	0
plotTSCANClusterDEG	4.955	0.287	5.242
plotTSCANClusterPseudo	1.521	0.100	1.622
plotTSCANDimReduceFeatures	1.513	0.074	1.587
plotTSCANPseudotimeGenes	1.707	0.107	1.814
plotTSCANPseudotimeHeatmap	1.427	0.079	1.507
plotTSCANResults	1.277	0.069	1.346
plotTSNE	0.383	0.011	0.394
plotTopHVG	0.627	0.056	0.684
plotUMAP	6.613	0.629	7.241
readSingleCellMatrix	0.005	0.000	0.005
reportCellQC	0.077	0.001	0.077
reportDropletQC	0.022	0.012	0.034
reportQCTool	0.080	0.004	0.085
retrieveSCEIndex	0.027	0.001	0.028
runBBKNN	0	0	0
runBarcodeRankDrops	0.219	0.008	0.226
runBcds	0.077	0.002	0.080
runCellQC	0.078	0.000	0.078
runClusterSummaryMetrics	0.355	0.006	0.362
runComBatSeq	0.423	0.032	0.455
runCxds	0.371	0.017	0.389
runCxdsBcdsHybrid	0.079	0.006	0.085
runDEAnalysis	0.374	0.029	0.403
runDecontX	6.722	0.393	7.115
runDimReduce	0.275	0.002	0.277
runDoubletFinder	32.612	1.163	33.774
runDropletQC	0.022	0.001	0.023
runEmptyDrops	6.265	0.033	6.298
runEnrichR	0.491	0.026	3.484
runFastMNN	1.710	0.115	1.826
runFeatureSelection	0.208	0.002	0.210
runFindMarker	1.493	0.048	1.541
runGSVA	0.829	0.034	0.863
runHarmony	0.039	0.010	0.049
runKMeans	0.177	0.011	0.189
runLimmaBC	0.081	0.012	0.093
runMNNCorrect	0.420	0.023	0.443
runModelGeneVar	0.307	0.001	0.309
runNormalization	2.295	0.270	2.566
runPerCellQC	0.323	0.007	0.330
runSCANORAMA	0	0	0
runSCMerge	0.004	0.000	0.004
runScDblFinder	16.602	0.717	14.280
runScanpyFindClusters	0.022	0.001	0.023
runScanpyFindHVG	0.021	0.000	0.022
runScanpyFindMarkers	0.021	0.001	0.022
runScanpyNormalizeData	0.095	0.001	0.097
runScanpyPCA	0.022	0.000	0.022
runScanpyScaleData	0.021	0.000	0.022
runScanpyTSNE	0.021	0.000	0.022
runScanpyUMAP	0.021	0.000	0.022
runScranSNN	0.283	0.001	0.284
runScrublet	0.021	0.001	0.022
runSeuratFindClusters	0.022	0.000	0.021
runSeuratFindHVG	0.475	0.011	0.487
runSeuratHeatmap	0.022	0.000	0.022
runSeuratICA	0.022	0.000	0.022
runSeuratJackStraw	0.022	0.000	0.022
runSeuratNormalizeData	0.022	0.000	0.022
runSeuratPCA	0.022	0.000	0.021
runSeuratSCTransform	29.540	1.728	31.276
runSeuratScaleData	0.019	0.004	0.022
runSeuratUMAP	0.021	0.000	0.021
runSingleR	0.035	0.000	0.035
runSoupX	0	0	0
runTSCAN	0.649	0.005	0.656
runTSCANClusterDEAnalysis	0.731	0.040	0.771
runTSCANDEG	0.690	0.027	0.716
runTSNE	0.721	0.003	0.724
runUMAP	6.838	0.408	7.246
runVAM	0.284	0.003	0.287
runZINBWaVE	0.003	0.001	0.004
sampleSummaryStats	0.153	0.000	0.153
scaterCPM	0.128	0.012	0.141
scaterPCA	0.433	0.000	0.434
scaterlogNormCounts	0.227	0.019	0.246
sce	0.021	0.001	0.022
sctkListGeneSetCollections	0.080	0.002	0.082
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.082	0.005	0.086
setSCTKDisplayRow	0.428	0.017	0.445
singleCellTK	0	0	0
subDiffEx	0.345	0.030	0.375
subsetSCECols	0.077	0.002	0.078
subsetSCERows	0.205	0.002	0.206
summarizeSCE	0.068	0.002	0.069
trimCounts	0.201	0.005	0.206