Back to Multiple platform build/check report for BioC 3.23:   simplified   long
ABCDEFGHIJKLMNOPQR[S]TUVWXYZ

This page was generated on 2026-01-24 11:34 -0500 (Sat, 24 Jan 2026).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 24.04.3 LTS)x86_64R Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences" 4811
kjohnson3macOS 13.7.7 Venturaarm64R Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences" 4545
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

Package 2018/2345HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.21.1  (landing page)
Joshua David Campbell
Snapshot Date: 2026-01-23 13:40 -0500 (Fri, 23 Jan 2026)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: 15d4a13
git_last_commit_date: 2026-01-11 08:42:53 -0500 (Sun, 11 Jan 2026)
nebbiolo1Linux (Ubuntu 24.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
kjohnson3macOS 13.7.7 Ventura / arm64  OK    OK    OK    OK  NO, package depends on 'batchelor' which is only available as a source package that needs compilation


CHECK results for singleCellTK on nebbiolo1

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: 2.21.1
Command: /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.23-bioc/R/site-library --timings singleCellTK_2.21.1.tar.gz
StartedAt: 2026-01-24 04:07:04 -0500 (Sat, 24 Jan 2026)
EndedAt: 2026-01-24 04:24:33 -0500 (Sat, 24 Jan 2026)
EllapsedTime: 1049.2 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.23-bioc/R/site-library --timings singleCellTK_2.21.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.23-bioc/meat/singleCellTK.Rcheck’
* using R Under development (unstable) (2026-01-15 r89304)
* 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 ‘2.21.1’
* 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  5.6Mb
  sub-directories of 1Mb or more:
    R       1.0Mb
    shiny   2.3Mb
* 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 46.890  0.940  47.837
plotDoubletFinderResults 41.565  0.349  41.914
runDoubletFinder         34.344  0.211  34.558
runSeuratSCTransform     29.661  0.247  29.915
plotScDblFinderResults   27.641  0.747  28.355
runScDblFinder           17.828  0.427  18.225
plotBatchCorrCompare     14.040  0.229  14.269
importExampleData        11.717  0.626  12.680
plotScdsHybridResults     9.834  0.124   9.404
plotBcdsResults           9.035  0.139   8.626
plotDecontXResults        8.236  0.086   8.321
plotCxdsResults           8.095  0.077   8.172
runUMAP                   7.946  0.127   8.074
runDecontX                7.442  0.097   7.539
plotUMAP                  7.143  0.095   7.238
plotEmptyDropsResults     6.661  0.015   6.676
plotEmptyDropsScatter     6.565  0.001   6.565
runEmptyDrops             6.274  0.018   6.292
detectCellOutlier         5.714  0.199   5.913
plotDEGViolin             5.357  0.090   5.441
* 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-3.23-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.23-bioc/R/site-library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.21.1’
** 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 Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences"
Copyright (C) 2026 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 
  0.154   0.046   0.188 

singleCellTK.Rcheck/tests/testthat.Rout


R Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences"
Copyright (C) 2026 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%
[04:22:08] WARNING: src/learner.cc:790: 
Parameters: { "nthreads" } are not used.

[04:22:09] WARNING: src/learner.cc:790: 
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
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...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 19 | SKIP 0 | PASS 225 ]

[ FAIL 0 | WARN 19 | SKIP 0 | PASS 225 ]
> 
> proc.time()
   user  system elapsed 
338.742   8.862 353.102 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0020.0000.003
SEG0.0030.0000.002
calcEffectSizes0.1530.0120.166
combineSCE0.7180.0380.757
computeZScore0.2280.0140.242
convertSCEToSeurat4.5810.1644.745
convertSeuratToSCE0.3630.0080.371
dedupRowNames0.0580.0000.058
detectCellOutlier5.7140.1995.913
diffAbundanceFET0.0530.0020.055
discreteColorPalette0.0060.0000.006
distinctColors0.0020.0000.003
downSampleCells0.5170.0290.546
downSampleDepth0.4120.0030.415
expData-ANY-character-method0.1240.0030.126
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.1650.0010.166
expData-set0.1570.0010.157
expData0.1380.0110.149
expDataNames-ANY-method0.1160.0020.118
expDataNames0.1190.0040.124
expDeleteDataTag0.0350.0010.036
expSetDataTag0.0240.0000.025
expTaggedData0.0240.0010.025
exportSCE0.0220.0000.023
exportSCEtoAnnData0.0950.0060.100
exportSCEtoFlatFile0.0930.0060.098
featureIndex0.0360.0010.037
generateSimulatedData0.0530.0020.055
getBiomarker0.0550.0060.061
getDEGTopTable0.7160.0850.801
getDiffAbundanceResults0.0530.0020.055
getEnrichRResult0.3820.0153.326
getFindMarkerTopTable1.5990.0171.622
getMSigDBTable0.0030.0020.004
getPathwayResultNames0.0200.0020.022
getSampleSummaryStatsTable0.1830.0050.188
getSoupX0.0000.0010.000
getTSCANResults0.9630.0250.987
getTopHVG0.8460.0160.863
importAnnData0.0010.0000.002
importBUStools0.1380.0010.139
importCellRanger0.8160.0080.824
importCellRangerV2Sample0.1560.0000.156
importCellRangerV3Sample0.2770.0040.282
importDropEst0.2010.0000.202
importExampleData11.717 0.62612.680
importGeneSetsFromCollection1.7790.0291.809
importGeneSetsFromGMT0.0630.0010.064
importGeneSetsFromList0.1310.0010.132
importGeneSetsFromMSigDB46.890 0.94047.837
importMitoGeneSet0.0650.0050.070
importOptimus0.0020.0000.002
importSEQC0.1560.0220.179
importSTARsolo0.1530.0300.183
iterateSimulations0.1980.0180.218
listSampleSummaryStatsTables0.2830.0260.310
mergeSCEColData0.4180.0180.455
mouseBrainSubsetSCE0.0360.0020.038
msigdb_table0.0000.0010.002
plotBarcodeRankDropsResults0.8580.0070.865
plotBarcodeRankScatter0.8830.0170.899
plotBatchCorrCompare14.040 0.22914.269
plotBatchVariance0.4570.0070.464
plotBcdsResults9.0350.1398.626
plotBubble0.8090.0120.819
plotClusterAbundance1.3670.0261.395
plotCxdsResults8.0950.0778.172
plotDEGHeatmap2.1390.0032.143
plotDEGRegression4.3980.0294.422
plotDEGViolin5.3570.0905.441
plotDEGVolcano0.9280.0040.933
plotDecontXResults8.2360.0868.321
plotDimRed0.2750.0030.279
plotDoubletFinderResults41.565 0.34941.914
plotEmptyDropsResults6.6610.0156.676
plotEmptyDropsScatter6.5650.0016.565
plotFindMarkerHeatmap3.8000.0063.806
plotMASTThresholdGenes1.2800.0141.295
plotPCA0.4030.0000.402
plotPathway0.7620.0250.788
plotRunPerCellQCResults3.0260.0103.038
plotSCEBarAssayData0.2780.0020.280
plotSCEBarColData0.2770.0010.277
plotSCEBatchFeatureMean0.3790.0000.380
plotSCEDensity0.3090.0010.310
plotSCEDensityAssayData0.2670.0000.268
plotSCEDensityColData0.3400.0010.342
plotSCEDimReduceColData0.7140.0020.716
plotSCEDimReduceFeatures0.3890.0020.391
plotSCEHeatmap0.4690.0020.470
plotSCEScatter0.3510.0100.362
plotSCEViolin0.3520.0050.356
plotSCEViolinAssayData0.4040.0070.412
plotSCEViolinColData0.3370.0040.341
plotScDblFinderResults27.641 0.74728.355
plotScanpyDotPlot0.0190.0020.022
plotScanpyEmbedding0.0220.0000.022
plotScanpyHVG0.0210.0010.021
plotScanpyHeatmap0.0210.0010.022
plotScanpyMarkerGenes0.0220.0000.021
plotScanpyMarkerGenesDotPlot0.0210.0000.022
plotScanpyMarkerGenesHeatmap0.0210.0000.022
plotScanpyMarkerGenesMatrixPlot0.0220.0000.021
plotScanpyMarkerGenesViolin0.0200.0010.022
plotScanpyMatrixPlot0.0210.0010.022
plotScanpyPCA0.0210.0010.022
plotScanpyPCAGeneRanking0.0220.0000.022
plotScanpyPCAVariance0.0210.0000.021
plotScanpyViolin0.0220.0000.022
plotScdsHybridResults9.8340.1249.404
plotScrubletResults0.0210.0010.022
plotSeuratElbow0.0190.0020.021
plotSeuratHVG0.0200.0010.021
plotSeuratJackStraw0.0220.0000.021
plotSeuratReduction0.0200.0010.021
plotSoupXResults0.0000.0010.000
plotTSCANClusterDEG4.7240.0124.757
plotTSCANClusterPseudo1.2810.0031.285
plotTSCANDimReduceFeatures1.3320.0051.337
plotTSCANPseudotimeGenes1.5760.0051.582
plotTSCANPseudotimeHeatmap1.2660.0111.277
plotTSCANResults1.2140.0021.217
plotTSNE0.3590.0010.360
plotTopHVG0.6290.0030.632
plotUMAP7.1430.0957.238
readSingleCellMatrix0.0060.0000.005
reportCellQC0.0750.0030.078
reportDropletQC0.0210.0010.021
reportQCTool0.0740.0020.076
retrieveSCEIndex0.0270.0010.028
runBBKNN0.0010.0000.001
runBarcodeRankDrops0.220.000.22
runBcds1.4360.0050.902
runCellQC0.0770.0000.077
runClusterSummaryMetrics0.3550.0030.358
runComBatSeq0.4050.0110.415
runCxds0.320.000.32
runCxdsBcdsHybrid1.5510.0551.013
runDEAnalysis0.4400.0080.448
runDecontX7.4420.0977.539
runDimReduce0.2730.0020.276
runDoubletFinder34.344 0.21134.558
runDropletQC0.0210.0000.022
runEmptyDrops6.2740.0186.292
runEnrichR0.4990.0402.709
runFastMNN1.6760.0661.742
runFeatureSelection0.2070.0020.208
runFindMarker1.4410.0071.449
runGSVA0.7930.0170.811
runHarmony0.1140.0010.114
runKMeans0.1780.0020.179
runLimmaBC0.0810.0100.091
runMNNCorrect0.4140.0150.430
runModelGeneVar0.3080.0070.314
runNormalization2.5470.0662.613
runPerCellQC0.3290.0010.330
runSCANORAMA000
runSCMerge0.0040.0000.005
runScDblFinder17.828 0.42718.225
runScanpyFindClusters0.0240.0010.025
runScanpyFindHVG0.0240.0000.023
runScanpyFindMarkers0.0230.0000.024
runScanpyNormalizeData0.1030.0000.103
runScanpyPCA0.0230.0000.024
runScanpyScaleData0.0200.0020.023
runScanpyTSNE0.0220.0010.024
runScanpyUMAP0.0220.0010.023
runScranSNN0.2940.0030.297
runScrublet0.0220.0020.023
runSeuratFindClusters0.0230.0000.022
runSeuratFindHVG0.4570.0110.469
runSeuratHeatmap0.0220.0010.023
runSeuratICA0.0220.0000.022
runSeuratJackStraw0.0220.0000.022
runSeuratNormalizeData0.0220.0000.023
runSeuratPCA0.0220.0000.023
runSeuratSCTransform29.661 0.24729.915
runSeuratScaleData0.0240.0010.024
runSeuratUMAP0.0230.0000.023
runSingleR0.0370.0010.039
runSoupX000
runTSCAN0.6680.0020.670
runTSCANClusterDEAnalysis0.7610.0060.767
runTSCANDEG0.7780.0160.794
runTSNE0.7280.0020.731
runUMAP7.9460.1278.074
runVAM0.3050.0030.308
runZINBWaVE0.0030.0010.004
sampleSummaryStats0.1650.0010.166
scaterCPM0.1380.0100.148
scaterPCA0.4950.0000.495
scaterlogNormCounts0.2450.0060.250
sce0.0230.0010.023
sctkListGeneSetCollections0.0900.0000.091
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment0.0000.0000.001
setRowNames0.0940.0000.094
setSCTKDisplayRow0.4400.0020.443
singleCellTK000
subDiffEx0.3160.0050.321
subsetSCECols0.0840.0000.083
subsetSCERows0.2850.0030.288
summarizeSCE0.0680.0000.068
trimCounts0.2160.0050.221