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This page was generated on 2025-08-16 12:05 -0400 (Sat, 16 Aug 2025).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 24.04.3 LTS)x86_644.5.1 (2025-06-13) -- "Great Square Root" 4818
lconwaymacOS 12.7.1 Montereyx86_644.5.1 (2025-06-13) -- "Great Square Root" 4596
kjohnson3macOS 13.7.7 Venturaarm644.5.1 Patched (2025-06-14 r88325) -- "Great Square Root" 4538
taishanLinux (openEuler 24.03 LTS)aarch644.5.0 (2025-04-11) -- "How About a Twenty-Six" 4535
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 1995/2317HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.19.1  (landing page)
Joshua David Campbell
Snapshot Date: 2025-08-15 13:45 -0400 (Fri, 15 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 lconway

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: 2025-08-15 23:49:08 -0400 (Fri, 15 Aug 2025)
EndedAt: 2025-08-16 00:06:41 -0400 (Sat, 16 Aug 2025)
EllapsedTime: 1052.8 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 (2025-06-13)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Monterey 12.7.6
* 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 50.254  0.507  51.025
plotDoubletFinderResults 41.453  0.233  41.849
runDoubletFinder         35.888  0.213  36.242
plotScDblFinderResults   31.267  0.805  32.205
runScDblFinder           20.513  0.460  21.072
importExampleData        15.034  1.828  17.318
plotBatchCorrCompare     13.929  0.142  14.133
plotScdsHybridResults     9.887  0.123  10.071
plotBcdsResults           9.028  0.152   9.227
plotDecontXResults        8.674  0.083   8.798
plotCxdsResults           7.658  0.077   7.762
runUMAP                   7.617  0.089   7.748
plotUMAP                  7.581  0.083   7.700
runDecontX                7.479  0.063   7.597
plotEmptyDropsScatter     6.081  0.034   6.145
plotEmptyDropsResults     5.984  0.036   6.042
runEmptyDrops             5.757  0.025   5.796
detectCellOutlier         5.380  0.167   5.579
* 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-x86_64/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 (2025-06-13) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-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)
NULL
> 
> proc.time()
   user  system elapsed 
  0.206   0.075   0.280 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.1 (2025-06-13) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-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:0.452540 
Will train until train_logloss hasn't improved in 2 rounds.

[2]	train-logloss:0.320237 
[3]	train-logloss:0.237326 
[4]	train-logloss:0.182355 
[5]	train-logloss:0.144099 
[6]	train-logloss:0.117553 
[7]	train-logloss:0.098814 
[8]	train-logloss:0.084978 
[9]	train-logloss:0.075063 
[10]	train-logloss:0.067483 
[11]	train-logloss:0.061861 
[12]	train-logloss:0.057362 
[13]	train-logloss:0.053725 
[14]	train-logloss:0.050620 
[15]	train-logloss:0.047937 
[16]	train-logloss:0.045355 
[17]	train-logloss:0.043608 
[18]	train-logloss:0.042678 
[1]	train-logloss:0.452932 
Will train until train_logloss hasn't improved in 2 rounds.

[2]	train-logloss:0.320861 
[3]	train-logloss:0.238138 
[4]	train-logloss:0.183327 
[5]	train-logloss:0.145234 
[6]	train-logloss:0.118471 
[7]	train-logloss:0.099668 
[8]	train-logloss:0.085972 
[9]	train-logloss:0.076338 
[10]	train-logloss:0.068629 
[11]	train-logloss:0.062967 
[12]	train-logloss:0.057971 
[13]	train-logloss:0.053386 
[14]	train-logloss:0.050623 
[1]	train-logloss:0.453030 
Will train until train_logloss hasn't improved in 2 rounds.

[2]	train-logloss:0.321019 
[3]	train-logloss:0.238344 
[4]	train-logloss:0.183572 
[5]	train-logloss:0.145515 
[6]	train-logloss:0.118784 
[7]	train-logloss:0.100283 
[8]	train-logloss:0.086178 
[9]	train-logloss:0.076766 
[10]	train-logloss:0.069198 
[11]	train-logloss:0.063614 
[12]	train-logloss:0.059085 
[13]	train-logloss:0.055346 
[14]	train-logloss:0.052474 
[15]	train-logloss:0.049706 
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 
312.505   7.470 324.332 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0020.0020.004
SEG0.0030.0020.005
calcEffectSizes0.2190.0120.231
combineSCE0.9200.0190.945
computeZScore0.2430.0090.254
convertSCEToSeurat4.6420.2194.885
convertSeuratToSCE0.3110.0070.318
dedupRowNames0.0570.0030.060
detectCellOutlier5.3800.1675.579
diffAbundanceFET0.0590.0040.063
discreteColorPalette0.0080.0010.007
distinctColors0.0030.0000.002
downSampleCells0.5450.0620.609
downSampleDepth0.4260.0320.460
expData-ANY-character-method0.1410.0080.151
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.1730.0060.179
expData-set0.1620.0060.168
expData0.1420.0060.150
expDataNames-ANY-method0.1240.0050.130
expDataNames0.1370.0080.146
expDeleteDataTag0.0410.0050.046
expSetDataTag0.0280.0040.032
expTaggedData0.0240.0030.027
exportSCE0.0240.0040.029
exportSCEtoAnnData0.0730.0030.076
exportSCEtoFlatFile0.0660.0030.069
featureIndex0.0410.0040.045
generateSimulatedData0.0630.0070.070
getBiomarker0.0690.0060.075
getDEGTopTable0.8000.0810.890
getDiffAbundanceResults0.0610.0040.064
getEnrichRResult0.3230.0422.725
getFindMarkerTopTable1.7530.0581.826
getMSigDBTable0.0040.0030.005
getPathwayResultNames0.0270.0040.031
getSampleSummaryStatsTable0.2230.0050.230
getSoupX0.0010.0000.000
getTSCANResults1.2100.0541.281
getTopHVG0.8100.0120.826
importAnnData0.0020.0000.003
importBUStools0.1730.0050.180
importCellRanger0.8430.0400.895
importCellRangerV2Sample0.1860.0030.190
importCellRangerV3Sample0.3680.0160.391
importDropEst0.2350.0050.242
importExampleData15.034 1.82817.318
importGeneSetsFromCollection0.8560.1411.008
importGeneSetsFromGMT0.0720.0060.079
importGeneSetsFromList0.2150.0090.226
importGeneSetsFromMSigDB50.254 0.50751.025
importMitoGeneSet0.0600.0070.068
importOptimus0.0020.0000.002
importSEQC0.1480.0090.158
importSTARsolo0.1640.0120.176
iterateSimulations0.1860.0170.205
listSampleSummaryStatsTables0.2680.0230.294
mergeSCEColData0.4530.0570.516
mouseBrainSubsetSCE0.0420.0040.048
msigdb_table0.0020.0020.004
plotBarcodeRankDropsResults0.6930.0350.732
plotBarcodeRankScatter0.7380.0120.753
plotBatchCorrCompare13.929 0.14214.133
plotBatchVariance0.4040.0180.426
plotBcdsResults9.0280.1529.227
plotBubble0.7530.0430.797
plotClusterAbundance0.8220.0080.831
plotCxdsResults7.6580.0777.762
plotDEGHeatmap2.6030.0502.673
plotDEGRegression4.0500.0634.131
plotDEGViolin4.7950.1104.927
plotDEGVolcano1.0730.0171.095
plotDecontXResults8.6740.0838.798
plotDimRed0.3400.0180.359
plotDoubletFinderResults41.453 0.23341.849
plotEmptyDropsResults5.9840.0366.042
plotEmptyDropsScatter6.0810.0346.145
plotFindMarkerHeatmap4.6470.0394.713
plotMASTThresholdGenes1.6030.0371.652
plotPCA0.4540.0090.465
plotPathway0.6250.0160.646
plotRunPerCellQCResults2.3650.0272.407
plotSCEBarAssayData0.2380.0060.246
plotSCEBarColData0.1780.0060.185
plotSCEBatchFeatureMean0.3060.0040.311
plotSCEDensity0.2540.0070.264
plotSCEDensityAssayData0.1990.0070.207
plotSCEDensityColData0.2440.0060.251
plotSCEDimReduceColData0.5820.0110.597
plotSCEDimReduceFeatures0.3140.0070.323
plotSCEHeatmap0.5370.0080.548
plotSCEScatter0.2900.0080.299
plotSCEViolin0.2900.0090.300
plotSCEViolinAssayData0.3150.0070.324
plotSCEViolinColData0.2910.0080.301
plotScDblFinderResults31.267 0.80532.205
plotScanpyDotPlot0.0290.0050.034
plotScanpyEmbedding0.0270.0030.031
plotScanpyHVG0.0250.0020.027
plotScanpyHeatmap0.0290.0030.032
plotScanpyMarkerGenes0.0280.0030.032
plotScanpyMarkerGenesDotPlot0.0270.0040.031
plotScanpyMarkerGenesHeatmap0.0250.0030.028
plotScanpyMarkerGenesMatrixPlot0.0240.0020.025
plotScanpyMarkerGenesViolin0.0290.0030.032
plotScanpyMatrixPlot0.0260.0040.030
plotScanpyPCA0.0260.0040.030
plotScanpyPCAGeneRanking0.0270.0070.034
plotScanpyPCAVariance0.0250.0030.028
plotScanpyViolin0.0230.0030.026
plotScdsHybridResults 9.887 0.12310.071
plotScrubletResults0.0270.0040.031
plotSeuratElbow0.0300.0040.035
plotSeuratHVG0.0300.0030.033
plotSeuratJackStraw0.0280.0030.031
plotSeuratReduction0.0300.0030.033
plotSoupXResults000
plotTSCANClusterDEG4.2210.0984.344
plotTSCANClusterPseudo1.4670.0291.507
plotTSCANDimReduceFeatures1.3950.0251.428
plotTSCANPseudotimeGenes1.5710.0251.605
plotTSCANPseudotimeHeatmap1.5840.0271.620
plotTSCANResults1.3930.0281.430
plotTSNE0.3610.0110.373
plotTopHVG0.5830.0180.605
plotUMAP7.5810.0837.700
readSingleCellMatrix0.0060.0010.008
reportCellQC0.0950.0060.101
reportDropletQC0.0290.0030.033
reportQCTool0.0960.0090.105
retrieveSCEIndex0.0360.0060.042
runBBKNN000
runBarcodeRankDrops0.2530.0070.263
runBcds1.7840.0501.843
runCellQC0.1030.0040.107
runClusterSummaryMetrics0.4520.0120.467
runComBatSeq0.4770.0190.499
runCxds0.3560.0130.371
runCxdsBcdsHybrid1.8860.1412.039
runDEAnalysis0.4070.0090.418
runDecontX7.4790.0637.597
runDimReduce0.2970.0090.308
runDoubletFinder35.888 0.21336.242
runDropletQC0.0260.0040.030
runEmptyDrops5.7570.0255.796
runEnrichR0.2800.0322.847
runFastMNN1.8470.0561.910
runFeatureSelection0.2470.0060.253
runFindMarker1.6750.0411.726
runGSVA0.8760.0540.937
runHarmony0.0470.0020.050
runKMeans0.2160.0130.230
runLimmaBC0.0900.0020.092
runMNNCorrect0.4540.0060.462
runModelGeneVar0.3440.0070.352
runNormalization2.5340.0392.586
runPerCellQC0.3890.0120.403
runSCANORAMA0.0000.0010.000
runSCMerge0.0040.0020.006
runScDblFinder20.513 0.46021.072
runScanpyFindClusters0.0270.0030.030
runScanpyFindHVG0.0240.0050.030
runScanpyFindMarkers0.0270.0040.030
runScanpyNormalizeData0.1110.0040.114
runScanpyPCA0.0250.0040.028
runScanpyScaleData0.0270.0050.032
runScanpyTSNE0.0300.0060.037
runScanpyUMAP0.0250.0040.029
runScranSNN0.3310.0120.344
runScrublet0.0310.0040.035
runSeuratFindClusters0.0280.0030.032
runSeuratFindHVG0.5170.0100.529
runSeuratHeatmap0.0280.0030.032
runSeuratICA0.0240.0040.026
runSeuratJackStraw0.0270.0040.031
runSeuratNormalizeData0.0240.0030.027
runSeuratPCA0.0250.0050.029
runSeuratSCTransform3.9950.0774.108
runSeuratScaleData0.0270.0040.032
runSeuratUMAP0.0290.0040.033
runSingleR0.0470.0040.050
runSoupX000
runTSCAN0.7470.0200.774
runTSCANClusterDEAnalysis0.8490.0270.882
runTSCANDEG0.8680.0250.898
runTSNE0.7080.0150.730
runUMAP7.6170.0897.748
runVAM0.3860.0110.400
runZINBWaVE0.0040.0010.005
sampleSummaryStats0.1960.0050.202
scaterCPM0.1460.0070.155
scaterPCA0.5410.0130.557
scaterlogNormCounts0.2740.0070.283
sce0.0300.0070.039
sctkListGeneSetCollections0.1060.0080.114
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.1080.0040.113
setSCTKDisplayRow0.3640.0100.375
singleCellTK000
subDiffEx0.4000.0260.431
subsetSCECols0.0950.0070.102
subsetSCERows0.3140.0220.338
summarizeSCE0.0770.0050.083
trimCounts0.1850.0090.194