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"	4889
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: 2026-02-13 05:38:31 -0500 (Fri, 13 Feb 2026)

EndedAt: 2026-02-13 05:55:33 -0500 (Fri, 13 Feb 2026)

EllapsedTime: 1021.9 seconds

RetCode: 0

Status: OK

CheckDir: singleCellTK.Rcheck

Warnings: 0

Command output

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


* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.2 (2025-10-31)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
    GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
* running under: Ubuntu 24.04.3 LTS
* using session charset: UTF-8
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.20.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 80 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable.  Move as many as possible to Suggests and
use conditionally.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... INFO
  installed size is  7.0Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    extdata   1.6Mb
    shiny     3.0Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
  dedupRowNames.Rd: SingleCellExperiment-class
  detectCellOutlier.Rd: colData
  diffAbundanceFET.Rd: colData
  downSampleCells.Rd: SingleCellExperiment-class
  downSampleDepth.Rd: SingleCellExperiment-class
  featureIndex.Rd: SummarizedExperiment-class,
    SingleCellExperiment-class
  getBiomarker.Rd: SingleCellExperiment-class
  getDEGTopTable.Rd: SingleCellExperiment-class
  getEnrichRResult.Rd: SingleCellExperiment-class
  getFindMarkerTopTable.Rd: SingleCellExperiment-class
  getGenesetNamesFromCollection.Rd: SingleCellExperiment-class
  getPathwayResultNames.Rd: SingleCellExperiment-class
  getSampleSummaryStatsTable.Rd: SingleCellExperiment-class, assay,
    colData
  getSoupX.Rd: SingleCellExperiment-class
  getTSCANResults.Rd: SingleCellExperiment-class
  getTopHVG.Rd: SingleCellExperiment-class
  importAlevin.Rd: DelayedArray, readMM
  importAnnData.Rd: DelayedArray, readMM
  importBUStools.Rd: readMM
  importCellRanger.Rd: readMM, DelayedArray
  importCellRangerV2Sample.Rd: readMM, DelayedArray
  importCellRangerV3Sample.Rd: readMM, DelayedArray
  importDropEst.Rd: DelayedArray, readMM
  importExampleData.Rd: scRNAseq, Matrix, DelayedArray,
    ReprocessedFluidigmData, ReprocessedAllenData, NestorowaHSCData
  importFromFiles.Rd: readMM, DelayedArray, SingleCellExperiment-class
  importGeneSetsFromCollection.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GeneSetCollection, GSEABase, metadata
  importGeneSetsFromGMT.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, getGmt, GSEABase, metadata
  importGeneSetsFromList.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GSEABase, metadata
  importGeneSetsFromMSigDB.Rd: SingleCellExperiment-class, msigdbr,
    GeneSetCollection-class, GSEABase, metadata
  importMitoGeneSet.Rd: SingleCellExperiment-class,
    GeneSetCollection-class, GSEABase, metadata
  importMultipleSources.Rd: DelayedArray
  importOptimus.Rd: readMM, DelayedArray
  importSEQC.Rd: readMM, DelayedArray
  importSTARsolo.Rd: readMM, DelayedArray
  iterateSimulations.Rd: SingleCellExperiment-class
  listSampleSummaryStatsTables.Rd: SingleCellExperiment-class, metadata
  plotBarcodeRankDropsResults.Rd: SingleCellExperiment-class
  plotBarcodeRankScatter.Rd: SingleCellExperiment-class
  plotBatchCorrCompare.Rd: SingleCellExperiment-class
  plotBatchVariance.Rd: SingleCellExperiment-class
  plotBcdsResults.Rd: SingleCellExperiment-class
  plotClusterAbundance.Rd: colData
  plotCxdsResults.Rd: SingleCellExperiment-class
  plotDEGHeatmap.Rd: SingleCellExperiment-class
  plotDEGRegression.Rd: SingleCellExperiment-class
  plotDEGViolin.Rd: SingleCellExperiment-class
  plotDEGVolcano.Rd: SingleCellExperiment-class
  plotDecontXResults.Rd: SingleCellExperiment-class
  plotDoubletFinderResults.Rd: SingleCellExperiment-class
  plotEmptyDropsResults.Rd: SingleCellExperiment-class
  plotEmptyDropsScatter.Rd: SingleCellExperiment-class
  plotEnrichR.Rd: SingleCellExperiment-class
  plotFindMarkerHeatmap.Rd: SingleCellExperiment-class
  plotPCA.Rd: SingleCellExperiment-class
  plotPathway.Rd: SingleCellExperiment-class
  plotRunPerCellQCResults.Rd: SingleCellExperiment-class
  plotSCEBarAssayData.Rd: SingleCellExperiment-class
  plotSCEBarColData.Rd: SingleCellExperiment-class
  plotSCEBatchFeatureMean.Rd: SingleCellExperiment-class
  plotSCEDensity.Rd: SingleCellExperiment-class
  plotSCEDensityAssayData.Rd: SingleCellExperiment-class
  plotSCEDensityColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceFeatures.Rd: SingleCellExperiment-class
  plotSCEHeatmap.Rd: SingleCellExperiment-class
  plotSCEScatter.Rd: SingleCellExperiment-class
  plotSCEViolin.Rd: SingleCellExperiment-class
  plotSCEViolinAssayData.Rd: SingleCellExperiment-class
  plotSCEViolinColData.Rd: SingleCellExperiment-class
  plotScDblFinderResults.Rd: SingleCellExperiment-class
  plotScdsHybridResults.Rd: SingleCellExperiment-class
  plotScrubletResults.Rd: SingleCellExperiment-class
  plotSoupXResults.Rd: SingleCellExperiment-class
  plotTSCANClusterDEG.Rd: SingleCellExperiment-class
  plotTSCANClusterPseudo.Rd: SingleCellExperiment-class
  plotTSCANDimReduceFeatures.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeGenes.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeHeatmap.Rd: SingleCellExperiment-class
  plotTSCANResults.Rd: SingleCellExperiment-class
  plotTSNE.Rd: SingleCellExperiment-class
  plotUMAP.Rd: SingleCellExperiment-class
  readSingleCellMatrix.Rd: DelayedArray
  reportCellQC.Rd: SingleCellExperiment-class
  reportClusterAbundance.Rd: colData
  reportDiffAbundanceFET.Rd: colData
  retrieveSCEIndex.Rd: SingleCellExperiment-class
  runBBKNN.Rd: SingleCellExperiment-class
  runBarcodeRankDrops.Rd: SingleCellExperiment-class, colData
  runBcds.Rd: SingleCellExperiment-class, colData
  runCellQC.Rd: colData
  runComBatSeq.Rd: SingleCellExperiment-class
  runCxds.Rd: SingleCellExperiment-class, colData
  runCxdsBcdsHybrid.Rd: colData
  runDEAnalysis.Rd: SingleCellExperiment-class
  runDecontX.Rd: colData
  runDimReduce.Rd: SingleCellExperiment-class
  runDoubletFinder.Rd: SingleCellExperiment-class
  runDropletQC.Rd: colData
  runEmptyDrops.Rd: SingleCellExperiment-class, colData
  runEnrichR.Rd: SingleCellExperiment-class
  runFastMNN.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runFeatureSelection.Rd: SingleCellExperiment-class
  runFindMarker.Rd: SingleCellExperiment-class
  runGSVA.Rd: SingleCellExperiment-class
  runHarmony.Rd: SingleCellExperiment-class
  runKMeans.Rd: SingleCellExperiment-class, colData
  runLimmaBC.Rd: SingleCellExperiment-class, assay
  runMNNCorrect.Rd: SingleCellExperiment-class, assay,
    BiocParallelParam-class
  runModelGeneVar.Rd: SingleCellExperiment-class
  runPerCellQC.Rd: SingleCellExperiment-class, BiocParallelParam,
    colData
  runSCANORAMA.Rd: SingleCellExperiment-class, assay
  runSCMerge.Rd: SingleCellExperiment-class, colData, assay,
    BiocParallelParam-class
  runScDblFinder.Rd: SingleCellExperiment-class, colData
  runScranSNN.Rd: SingleCellExperiment-class, reducedDim, assay,
    altExp, colData, igraph
  runScrublet.Rd: SingleCellExperiment-class, colData
  runSingleR.Rd: SingleCellExperiment-class
  runSoupX.Rd: SingleCellExperiment-class
  runTSCAN.Rd: SingleCellExperiment-class
  runTSCANClusterDEAnalysis.Rd: SingleCellExperiment-class
  runTSCANDEG.Rd: SingleCellExperiment-class
  runTSNE.Rd: SingleCellExperiment-class
  runUMAP.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runVAM.Rd: SingleCellExperiment-class
  runZINBWaVE.Rd: SingleCellExperiment-class, colData,
    BiocParallelParam-class
  sampleSummaryStats.Rd: SingleCellExperiment-class, assay, colData
  scaterPCA.Rd: SingleCellExperiment-class, BiocParallelParam-class
  scaterlogNormCounts.Rd: logNormCounts
  sctkListGeneSetCollections.Rd: GeneSetCollection-class
  sctkPythonInstallConda.Rd: conda_install, reticulate, conda_create
  sctkPythonInstallVirtualEnv.Rd: virtualenv_install, reticulate,
    virtualenv_create
  selectSCTKConda.Rd: reticulate
  selectSCTKVirtualEnvironment.Rd: reticulate
  setRowNames.Rd: SingleCellExperiment-class
  setSCTKDisplayRow.Rd: SingleCellExperiment-class
  singleCellTK.Rd: SingleCellExperiment-class
  subsetSCECols.Rd: SingleCellExperiment-class
  subsetSCERows.Rd: SingleCellExperiment-class, altExp
  summarizeSCE.Rd: SingleCellExperiment-class
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
importGeneSetsFromMSigDB 44.665  0.699  45.365
plotDoubletFinderResults 41.887  0.294  42.258
runDoubletFinder         36.087  0.711  36.801
runSeuratSCTransform     30.723  1.268  31.996
plotScDblFinderResults   27.230  0.803  25.073
runScDblFinder           17.146  0.699  14.789
plotBatchCorrCompare     13.260  0.087  13.531
importExampleData        10.645  0.337  11.400
plotDecontXResults        9.381  0.066   9.447
plotScdsHybridResults     7.975  1.206   9.260
plotUMAP                  8.088  1.047   9.214
runUMAP                   8.183  0.197   8.465
runDecontX                7.935  0.401   8.337
plotCxdsResults           7.444  0.085   7.605
plotDEGViolin             6.938  0.125   7.057
detectCellOutlier         6.617  0.186   6.805
plotEmptyDropsResults     6.711  0.026   6.738
plotEmptyDropsScatter     6.560  0.043   6.603
runEmptyDrops             6.277  0.021   6.299
plotBcdsResults           5.885  0.075   6.036
plotTSCANClusterDEG       4.993  0.295   5.290
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.

Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.22-bioc/R/site-library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.20.1’
** using staged installation
** R
** data
** exec
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (singleCellTK)

Tests output

singleCellTK.Rcheck/tests/spelling.Rout


R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
All Done!
> 
> proc.time()
   user  system elapsed 
  0.157   0.040   0.182

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%
[05:53:26] WARNING: src/learner.cc:782: 
Parameters: { "nthreads" } are not used.

[05:53:27] 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 
324.754   5.424 330.979

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.003	0.000	0.003
SEG	0.002	0.001	0.003
calcEffectSizes	0.194	0.003	0.198
combineSCE	0.714	0.000	0.714
computeZScore	0.232	0.006	0.238
convertSCEToSeurat	4.496	0.104	4.600
convertSeuratToSCE	0.342	0.004	0.346
dedupRowNames	0.056	0.000	0.056
detectCellOutlier	6.617	0.186	6.805
diffAbundanceFET	0.056	0.002	0.058
discreteColorPalette	0.006	0.000	0.006
distinctColors	0.002	0.000	0.002
downSampleCells	0.492	0.060	0.552
downSampleDepth	0.429	0.040	0.469
expData-ANY-character-method	0.125	0.003	0.129
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.161	0.001	0.162
expData-set	0.153	0.003	0.156
expData	0.123	0.003	0.126
expDataNames-ANY-method	0.115	0.003	0.118
expDataNames	0.111	0.012	0.123
expDeleteDataTag	0.030	0.002	0.032
expSetDataTag	0.022	0.003	0.025
expTaggedData	0.025	0.001	0.026
exportSCE	0.021	0.001	0.022
exportSCEtoAnnData	0.092	0.006	0.098
exportSCEtoFlatFile	0.087	0.011	0.098
featureIndex	0.034	0.003	0.038
generateSimulatedData	0.068	0.002	0.071
getBiomarker	0.057	0.000	0.057
getDEGTopTable	0.656	0.004	0.660
getDiffAbundanceResults	0.047	0.000	0.047
getEnrichRResult	0.573	0.039	3.111
getFindMarkerTopTable	1.518	0.039	1.557
getMSigDBTable	0.003	0.000	0.003
getPathwayResultNames	0.023	0.000	0.023
getSampleSummaryStatsTable	0.211	0.002	0.213
getSoupX	0	0	0
getTSCANResults	0.963	0.002	0.965
getTopHVG	0.869	0.010	0.880
importAnnData	0.001	0.000	0.002
importBUStools	0.160	0.002	0.163
importCellRanger	0.745	0.009	0.754
importCellRangerV2Sample	0.141	0.000	0.140
importCellRangerV3Sample	0.279	0.005	0.284
importDropEst	1.394	0.041	1.436
importExampleData	10.645	0.337	11.400
importGeneSetsFromCollection	1.775	0.021	1.797
importGeneSetsFromGMT	0.062	0.002	0.065
importGeneSetsFromList	0.113	0.002	0.115
importGeneSetsFromMSigDB	44.665	0.699	45.365
importMitoGeneSet	0.051	0.004	0.057
importOptimus	0.002	0.000	0.001
importSEQC	0.138	0.024	0.162
importSTARsolo	0.139	0.024	0.165
iterateSimulations	0.173	0.028	0.202
listSampleSummaryStatsTables	0.251	0.034	0.284
mergeSCEColData	0.40	0.01	0.41
mouseBrainSubsetSCE	0.038	0.000	0.037
msigdb_table	0.002	0.000	0.001
plotBarcodeRankDropsResults	0.840	0.013	0.853
plotBarcodeRankScatter	0.837	0.009	0.847
plotBatchCorrCompare	13.260	0.087	13.531
plotBatchVariance	0.475	0.003	0.478
plotBcdsResults	5.885	0.075	6.036
plotBubble	0.778	0.002	0.780
plotClusterAbundance	1.324	0.003	1.327
plotCxdsResults	7.444	0.085	7.605
plotDEGHeatmap	2.047	0.029	2.075
plotDEGRegression	4.416	0.012	4.422
plotDEGViolin	6.938	0.125	7.057
plotDEGVolcano	0.984	0.018	1.002
plotDecontXResults	9.381	0.066	9.447
plotDimRed	0.289	0.001	0.290
plotDoubletFinderResults	41.887	0.294	42.258
plotEmptyDropsResults	6.711	0.026	6.738
plotEmptyDropsScatter	6.560	0.043	6.603
plotFindMarkerHeatmap	3.876	0.066	3.942
plotMASTThresholdGenes	1.231	0.011	1.243
plotPCA	0.366	0.001	0.367
plotPathway	0.726	0.004	0.730
plotRunPerCellQCResults	3.134	0.006	3.139
plotSCEBarAssayData	0.328	0.001	0.328
plotSCEBarColData	0.228	0.002	0.231
plotSCEBatchFeatureMean	0.4	0.0	0.4
plotSCEDensity	0.317	0.001	0.318
plotSCEDensityAssayData	0.308	0.002	0.311
plotSCEDensityColData	0.292	0.003	0.295
plotSCEDimReduceColData	0.725	0.004	0.729
plotSCEDimReduceFeatures	0.413	0.001	0.414
plotSCEHeatmap	0.426	0.001	0.427
plotSCEScatter	0.348	0.001	0.349
plotSCEViolin	0.413	0.000	0.413
plotSCEViolinAssayData	0.367	0.001	0.368
plotSCEViolinColData	0.348	0.004	0.351
plotScDblFinderResults	27.230	0.803	25.073
plotScanpyDotPlot	0.023	0.000	0.022
plotScanpyEmbedding	0.022	0.000	0.022
plotScanpyHVG	0.021	0.000	0.022
plotScanpyHeatmap	0.022	0.000	0.022
plotScanpyMarkerGenes	0.023	0.000	0.022
plotScanpyMarkerGenesDotPlot	0.023	0.000	0.023
plotScanpyMarkerGenesHeatmap	0.023	0.000	0.023
plotScanpyMarkerGenesMatrixPlot	0.021	0.001	0.022
plotScanpyMarkerGenesViolin	0.021	0.001	0.022
plotScanpyMatrixPlot	0.022	0.000	0.022
plotScanpyPCA	0.022	0.000	0.021
plotScanpyPCAGeneRanking	0.022	0.001	0.023
plotScanpyPCAVariance	0.023	0.000	0.023
plotScanpyViolin	0.023	0.000	0.024
plotScdsHybridResults	7.975	1.206	9.260
plotScrubletResults	0.025	0.000	0.026
plotSeuratElbow	0.024	0.000	0.024
plotSeuratHVG	0.024	0.000	0.024
plotSeuratJackStraw	0.024	0.000	0.024
plotSeuratReduction	0.022	0.002	0.024
plotSoupXResults	0.000	0.000	0.001
plotTSCANClusterDEG	4.993	0.295	5.290
plotTSCANClusterPseudo	1.518	0.111	1.630
plotTSCANDimReduceFeatures	1.406	0.088	1.494
plotTSCANPseudotimeGenes	1.739	0.097	1.836
plotTSCANPseudotimeHeatmap	1.485	0.282	1.768
plotTSCANResults	1.316	0.134	1.452
plotTSNE	0.440	0.052	0.493
plotTopHVG	0.771	0.160	0.932
plotUMAP	8.088	1.047	9.214
readSingleCellMatrix	0.006	0.001	0.006
reportCellQC	0.083	0.003	0.086
reportDropletQC	0.023	0.000	0.023
reportQCTool	0.079	0.000	0.079
retrieveSCEIndex	0.027	0.001	0.029
runBBKNN	0	0	0
runBarcodeRankDrops	0.225	0.002	0.226
runBcds	0.081	0.001	0.082
runCellQC	0.080	0.001	0.081
runClusterSummaryMetrics	0.377	0.032	0.410
runComBatSeq	0.433	0.011	0.444
runCxds	0.394	0.017	0.411
runCxdsBcdsHybrid	0.085	0.001	0.086
runDEAnalysis	0.387	0.021	0.408
runDecontX	7.935	0.401	8.337
runDimReduce	0.283	0.001	0.284
runDoubletFinder	36.087	0.711	36.801
runDropletQC	0.022	0.002	0.024
runEmptyDrops	6.277	0.021	6.299
runEnrichR	0.578	0.067	2.796
runFastMNN	1.774	0.089	1.864
runFeatureSelection	0.218	0.006	0.225
runFindMarker	1.471	0.010	1.480
runGSVA	0.904	0.011	0.915
runHarmony	0.044	0.000	0.044
runKMeans	0.190	0.000	0.189
runLimmaBC	0.083	0.001	0.083
runMNNCorrect	0.410	0.000	0.409
runModelGeneVar	0.318	0.000	0.318
runNormalization	2.583	0.133	2.715
runPerCellQC	0.341	0.000	0.342
runSCANORAMA	0.000	0.000	0.001
runSCMerge	0.003	0.001	0.004
runScDblFinder	17.146	0.699	14.789
runScanpyFindClusters	0.023	0.001	0.024
runScanpyFindHVG	0.023	0.000	0.023
runScanpyFindMarkers	0.021	0.001	0.023
runScanpyNormalizeData	0.099	0.001	0.100
runScanpyPCA	0.024	0.000	0.023
runScanpyScaleData	0.022	0.000	0.022
runScanpyTSNE	0.023	0.000	0.022
runScanpyUMAP	0.024	0.002	0.026
runScranSNN	0.299	0.002	0.301
runScrublet	0.024	0.000	0.023
runSeuratFindClusters	0.023	0.000	0.023
runSeuratFindHVG	0.533	0.016	0.548
runSeuratHeatmap	0.023	0.001	0.024
runSeuratICA	0.022	0.000	0.023
runSeuratJackStraw	0.022	0.000	0.022
runSeuratNormalizeData	0.023	0.000	0.022
runSeuratPCA	0.022	0.000	0.023
runSeuratSCTransform	30.723	1.268	31.996
runSeuratScaleData	0.022	0.002	0.024
runSeuratUMAP	0.021	0.001	0.023
runSingleR	0.035	0.002	0.037
runSoupX	0	0	0
runTSCAN	0.673	0.004	0.677
runTSCANClusterDEAnalysis	0.815	0.019	0.834
runTSCANDEG	0.809	0.016	0.826
runTSNE	0.742	0.000	0.743
runUMAP	8.183	0.197	8.465
runVAM	0.343	0.011	0.355
runZINBWaVE	0.005	0.000	0.004
sampleSummaryStats	0.172	0.008	0.179
scaterCPM	0.143	0.005	0.149
scaterPCA	0.473	0.000	0.472
scaterlogNormCounts	0.242	0.007	0.249
sce	0.023	0.000	0.023
sctkListGeneSetCollections	0.083	0.002	0.085
sctkPythonInstallConda	0	0	0
sctkPythonInstallVirtualEnv	0	0	0
selectSCTKConda	0	0	0
selectSCTKVirtualEnvironment	0	0	0
setRowNames	0.090	0.000	0.089
setSCTKDisplayRow	0.454	0.009	0.463
singleCellTK	0	0	0
subDiffEx	0.345	0.007	0.352
subsetSCECols	0.087	0.001	0.089
subsetSCERows	0.268	0.006	0.274
summarizeSCE	0.068	0.001	0.070
trimCounts	0.207	0.007	0.214