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This page was generated on 2025-09-01 12:06 -0400 (Mon, 01 Sep 2025).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 24.04.3 LTS)x86_644.5.1 (2025-06-13) -- "Great Square Root" 4824
lconwaymacOS 12.7.1 Montereyx86_644.5.1 (2025-06-13) -- "Great Square Root" 4615
kjohnson3macOS 13.7.7 Venturaarm644.5.1 Patched (2025-06-14 r88325) -- "Great Square Root" 4562
taishanLinux (openEuler 24.03 LTS)aarch644.5.0 (2025-04-11) -- "How About a Twenty-Six" 4541
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 1998/2320HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.19.1  (landing page)
Joshua David Campbell
Snapshot Date: 2025-08-31 13:45 -0400 (Sun, 31 Aug 2025)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: 565145a1
git_last_commit_date: 2025-07-01 15:36:15 -0400 (Tue, 01 Jul 2025)
nebbiolo2Linux (Ubuntu 24.04.3 LTS) / x86_64  OK    OK    OK  NO, package depends on 'MAST' which is not available
lconwaymacOS 12.7.1 Monterey / x86_64  OK    OK    OK    OK  NO, package depends on 'MAST' which is not available
kjohnson3macOS 13.7.7 Ventura / arm64  OK    OK    OK    OK  NO, package depends on 'MAST' which is not available
taishanLinux (openEuler 24.03 LTS) / aarch64  OK    OK    OK  


CHECK results for singleCellTK on kjohnson3

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: 2.19.1
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.19.1.tar.gz
StartedAt: 2025-08-31 21:42:17 -0400 (Sun, 31 Aug 2025)
EndedAt: 2025-08-31 21:48:52 -0400 (Sun, 31 Aug 2025)
EllapsedTime: 394.5 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.19.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.1 Patched (2025-06-14 r88325)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 16.0.0 (clang-1600.0.26.6)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Ventura 13.7.7
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.19.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 79 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  6.8Mb
  sub-directories of 1Mb or more:
    extdata   1.5Mb
    shiny     2.9Mb
* 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 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
  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 17.920  0.088  18.039
plotDoubletFinderResults 17.333  0.085  17.496
runDoubletFinder         16.178  0.063  16.259
plotScDblFinderResults   13.830  0.244  14.369
runScDblFinder            9.030  0.136   9.186
plotBatchCorrCompare      5.679  0.043   5.823
importExampleData         4.670  0.454   5.546
* 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 running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

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


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.19.1’
** 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 4.5.1 Patched (2025-06-14 r88325) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

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 
  0.067   0.018   0.083 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.1 Patched (2025-06-14 r88325) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

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%
[1]	train-logloss:0.452573 
Will train until train_logloss hasn't improved in 2 rounds.

[2]	train-logloss:0.320290 
[3]	train-logloss:0.237363 
[4]	train-logloss:0.182378 
[5]	train-logloss:0.144113 
[6]	train-logloss:0.117560 
[7]	train-logloss:0.098812 
[8]	train-logloss:0.084977 
[9]	train-logloss:0.075059 
[10]	train-logloss:0.067480 
[11]	train-logloss:0.061855 
[12]	train-logloss:0.057358 
[13]	train-logloss:0.053969 
[14]	train-logloss:0.050909 
[15]	train-logloss:0.047615 
[16]	train-logloss:0.045564 
[17]	train-logloss:0.043868 
[1]	train-logloss:0.453064 
Will train until train_logloss hasn't improved in 2 rounds.

[2]	train-logloss:0.321072 
[3]	train-logloss:0.238210 
[4]	train-logloss:0.183469 
[5]	train-logloss:0.145239 
[6]	train-logloss:0.118860 
[7]	train-logloss:0.100304 
[8]	train-logloss:0.086606 
[9]	train-logloss:0.076012 
[10]	train-logloss:0.068021 
[11]	train-logloss:0.062325 
[12]	train-logloss:0.057942 
[13]	train-logloss:0.054289 
[14]	train-logloss:0.051302 
[15]	train-logloss:0.048796 
[1]	train-logloss:0.453064 
Will train until train_logloss hasn't improved in 2 rounds.

[2]	train-logloss:0.321072 
[3]	train-logloss:0.238210 
[4]	train-logloss:0.183469 
[5]	train-logloss:0.145239 
[6]	train-logloss:0.118860 
[7]	train-logloss:0.100304 
[8]	train-logloss:0.086606 
[9]	train-logloss:0.076012 
[10]	train-logloss:0.068021 
[11]	train-logloss:0.062325 
[12]	train-logloss:0.057942 
[13]	train-logloss:0.054289 
[14]	train-logloss:0.051302 
[15]	train-logloss:0.048796 
[16]	train-logloss:0.046452 
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%

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

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]

[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]
> 
> proc.time()
   user  system elapsed 
116.769   2.450 125.715 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0010.0010.002
SEG0.0010.0010.002
calcEffectSizes0.0730.0040.077
combineSCE0.2160.0020.219
computeZScore0.0920.0020.094
convertSCEToSeurat1.5660.0521.620
convertSeuratToSCE0.0970.0020.101
dedupRowNames0.0210.0010.022
detectCellOutlier2.2770.0392.334
diffAbundanceFET0.0240.0010.026
discreteColorPalette0.0020.0000.002
distinctColors0.0010.0000.001
downSampleCells0.2040.0180.225
downSampleDepth0.1500.0100.162
expData-ANY-character-method0.0420.0010.043
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.0540.0010.056
expData-set0.0480.0020.052
expData0.0420.0030.045
expDataNames-ANY-method0.0390.0020.042
expDataNames0.0400.0020.044
expDeleteDataTag0.0170.0010.018
expSetDataTag0.0140.0010.014
expTaggedData0.0130.0010.017
exportSCE0.0120.0020.015
exportSCEtoAnnData0.0420.0010.045
exportSCEtoFlatFile0.0410.0010.043
featureIndex0.0170.0010.019
generateSimulatedData0.0220.0020.025
getBiomarker0.0240.0030.032
getDEGTopTable0.2370.0200.262
getDiffAbundanceResults0.0220.0010.023
getEnrichRResult0.1260.0173.045
getFindMarkerTopTable0.4570.0140.470
getMSigDBTable0.0010.0010.003
getPathwayResultNames0.0120.0020.014
getSampleSummaryStatsTable0.0630.0020.066
getSoupX000
getTSCANResults0.3780.0140.391
getTopHVG0.2630.0060.269
importAnnData0.0000.0000.001
importBUStools0.0450.0010.047
importCellRanger0.2310.0110.243
importCellRangerV2Sample0.0430.0000.044
importCellRangerV3Sample0.0930.0040.097
importDropEst0.0630.0010.064
importExampleData4.6700.4545.546
importGeneSetsFromCollection0.2590.0250.307
importGeneSetsFromGMT0.0270.0020.029
importGeneSetsFromList0.0670.0030.072
importGeneSetsFromMSigDB17.920 0.08818.039
importMitoGeneSet0.0220.0040.026
importOptimus000
importSEQC0.0520.0030.056
importSTARsolo0.0440.0050.049
iterateSimulations0.0660.0060.073
listSampleSummaryStatsTables0.0890.0060.095
mergeSCEColData0.1380.0130.154
mouseBrainSubsetSCE0.0170.0020.020
msigdb_table0.0010.0010.002
plotBarcodeRankDropsResults0.1910.0090.200
plotBarcodeRankScatter0.2000.0030.204
plotBatchCorrCompare5.6790.0435.823
plotBatchVariance0.1090.0090.117
plotBcdsResults3.6370.0593.722
plotBubble0.2570.0210.278
plotClusterAbundance0.2640.0030.267
plotCxdsResults2.9510.0293.005
plotDEGHeatmap0.7020.0230.730
plotDEGRegression1.0450.0161.070
plotDEGViolin1.2500.0391.292
plotDEGVolcano0.3200.0040.324
plotDecontXResults3.4540.0373.495
plotDimRed0.1180.0160.137
plotDoubletFinderResults17.333 0.08517.496
plotEmptyDropsResults2.1140.0092.122
plotEmptyDropsScatter2.0880.0112.105
plotFindMarkerHeatmap1.2230.0071.232
plotMASTThresholdGenes0.4300.0100.479
plotPCA0.1320.0040.153
plotPathway0.1620.0040.175
plotRunPerCellQCResults0.6310.0060.653
plotSCEBarAssayData0.0750.0030.083
plotSCEBarColData0.0530.0030.062
plotSCEBatchFeatureMean0.0940.0020.096
plotSCEDensity0.0750.0020.078
plotSCEDensityAssayData0.0600.0020.062
plotSCEDensityColData0.0720.0030.075
plotSCEDimReduceColData0.1620.0050.167
plotSCEDimReduceFeatures0.0880.0040.092
plotSCEHeatmap0.1600.0020.166
plotSCEScatter0.0840.0040.093
plotSCEViolin0.0820.0030.085
plotSCEViolinAssayData0.0870.0030.090
plotSCEViolinColData0.0820.0030.085
plotScDblFinderResults13.830 0.24414.369
plotScanpyDotPlot0.0120.0010.013
plotScanpyEmbedding0.0110.0010.012
plotScanpyHVG0.0110.0010.013
plotScanpyHeatmap0.0110.0010.012
plotScanpyMarkerGenes0.0110.0010.013
plotScanpyMarkerGenesDotPlot0.0110.0020.012
plotScanpyMarkerGenesHeatmap0.0110.0010.012
plotScanpyMarkerGenesMatrixPlot0.0100.0010.012
plotScanpyMarkerGenesViolin0.0100.0020.012
plotScanpyMatrixPlot0.0100.0010.012
plotScanpyPCA0.0110.0010.012
plotScanpyPCAGeneRanking0.0110.0010.012
plotScanpyPCAVariance0.0110.0010.012
plotScanpyViolin0.0110.0020.012
plotScdsHybridResults4.2120.0424.307
plotScrubletResults0.0120.0020.014
plotSeuratElbow0.0120.0010.013
plotSeuratHVG0.0120.0020.014
plotSeuratJackStraw0.0120.0020.014
plotSeuratReduction0.0120.0010.014
plotSoupXResults000
plotTSCANClusterDEG1.1500.0361.197
plotTSCANClusterPseudo0.3570.0060.362
plotTSCANDimReduceFeatures0.3850.0070.392
plotTSCANPseudotimeGenes0.4150.0060.422
plotTSCANPseudotimeHeatmap0.4150.0060.421
plotTSCANResults0.3560.0060.361
plotTSNE0.1060.0030.109
plotTopHVG0.1690.0030.172
plotUMAP3.2930.0193.361
readSingleCellMatrix0.0020.0000.002
reportCellQC0.0290.0010.032
reportDropletQC0.0130.0010.017
reportQCTool0.0290.0020.031
retrieveSCEIndex0.0140.0020.016
runBBKNN000
runBarcodeRankDrops0.0780.0030.080
runBcds0.6090.0210.641
runCellQC0.0310.0020.033
runClusterSummaryMetrics0.1290.0090.138
runComBatSeq0.1600.0070.183
runCxds0.1150.0070.122
runCxdsBcdsHybrid0.6630.0320.698
runDEAnalysis0.1340.0040.139
runDecontX3.2900.0413.342
runDimReduce0.0950.0020.098
runDoubletFinder16.178 0.06316.259
runDropletQC0.0130.0020.014
runEmptyDrops2.0290.0042.035
runEnrichR0.1160.0162.212
runFastMNN0.5440.0340.580
runFeatureSelection0.0770.0020.085
runFindMarker0.4410.0100.458
runGSVA0.2830.0140.298
runHarmony0.0140.0000.014
runKMeans0.0630.0030.065
runLimmaBC0.0230.0010.024
runMNNCorrect0.1380.0020.148
runModelGeneVar0.1020.0020.105
runNormalization1.1740.0131.191
runPerCellQC0.1060.0030.109
runSCANORAMA000
runSCMerge0.0020.0000.002
runScDblFinder9.0300.1369.186
runScanpyFindClusters0.0120.0010.013
runScanpyFindHVG0.0110.0010.012
runScanpyFindMarkers0.0110.0010.012
runScanpyNormalizeData0.0370.0020.038
runScanpyPCA0.0110.0010.012
runScanpyScaleData0.0110.0010.012
runScanpyTSNE0.0110.0010.012
runScanpyUMAP0.0110.0010.012
runScranSNN0.0910.0040.095
runScrublet0.0120.0020.013
runSeuratFindClusters0.0110.0010.013
runSeuratFindHVG0.1430.0040.146
runSeuratHeatmap0.0120.0020.014
runSeuratICA0.0110.0010.012
runSeuratJackStraw0.0100.0010.011
runSeuratNormalizeData0.0100.0010.011
runSeuratPCA0.0110.0010.012
runSeuratSCTransform1.4830.0511.534
runSeuratScaleData0.0120.0020.014
runSeuratUMAP0.0110.0010.013
runSingleR0.0120.0010.013
runSoupX000
runTSCAN0.2020.0060.207
runTSCANClusterDEAnalysis0.2390.0220.261
runTSCANDEG0.2420.0060.248
runTSNE0.2820.0160.298
runUMAP3.1940.0273.234
runVAM0.0950.0020.097
runZINBWaVE0.0020.0000.002
sampleSummaryStats0.0530.0030.056
scaterCPM0.0570.0040.061
scaterPCA0.1350.0030.139
scaterlogNormCounts0.0860.0050.090
sce0.0120.0030.015
sctkListGeneSetCollections0.0280.0030.032
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0300.0040.034
setSCTKDisplayRow0.1080.0090.117
singleCellTK000
subDiffEx0.1170.0120.129
subsetSCECols0.0300.0040.032
subsetSCERows0.0950.0070.101
summarizeSCE0.0270.0030.029
trimCounts0.0810.0070.088