Back to Multiple platform build/check report for BioC 3.22:   simplified   long
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This page was generated on 2025-09-08 12:04 -0400 (Mon, 08 Sep 2025).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 24.04.3 LTS)x86_644.5.1 (2025-06-13) -- "Great Square Root" 4823
lconwaymacOS 12.7.1 Montereyx86_644.5.1 (2025-06-13) -- "Great Square Root" 4618
kjohnson3macOS 13.7.7 Venturaarm644.5.1 Patched (2025-06-14 r88325) -- "Great Square Root" 4565
taishanLinux (openEuler 24.03 LTS)aarch644.5.0 (2025-04-11) -- "How About a Twenty-Six" 4544
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 1999/2322HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.19.1  (landing page)
Joshua David Campbell
Snapshot Date: 2025-09-07 13:45 -0400 (Sun, 07 Sep 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 nebbiolo2

To the developers/maintainers of the singleCellTK package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/singleCellTK.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results


Summary

Package: singleCellTK
Version: 2.19.1
Command: /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings singleCellTK_2.19.1.tar.gz
StartedAt: 2025-09-08 04:10:34 -0400 (Mon, 08 Sep 2025)
EndedAt: 2025-09-08 04:27:41 -0400 (Mon, 08 Sep 2025)
EllapsedTime: 1026.9 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings singleCellTK_2.19.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.1 (2025-06-13)
* 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 ‘2.19.1’
* 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  7.0Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    extdata   1.6Mb
    shiny     3.0Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
  dedupRowNames.Rd: SingleCellExperiment-class
  detectCellOutlier.Rd: colData
  diffAbundanceFET.Rd: colData
  downSampleCells.Rd: SingleCellExperiment-class
  downSampleDepth.Rd: SingleCellExperiment-class
  featureIndex.Rd: SummarizedExperiment-class,
    SingleCellExperiment-class
  getBiomarker.Rd: SingleCellExperiment-class
  getDEGTopTable.Rd: SingleCellExperiment-class
  getEnrichRResult.Rd: SingleCellExperiment-class
  getFindMarkerTopTable.Rd: SingleCellExperiment-class
  getGenesetNamesFromCollection.Rd: SingleCellExperiment-class
  getPathwayResultNames.Rd: SingleCellExperiment-class
  getSampleSummaryStatsTable.Rd: SingleCellExperiment-class, assay,
    colData
  getSoupX.Rd: SingleCellExperiment-class
  getTSCANResults.Rd: SingleCellExperiment-class
  getTopHVG.Rd: SingleCellExperiment-class
  importAlevin.Rd: DelayedArray, readMM
  importAnnData.Rd: DelayedArray, readMM
  importBUStools.Rd: readMM
  importCellRanger.Rd: readMM, DelayedArray
  importCellRangerV2Sample.Rd: readMM, DelayedArray
  importCellRangerV3Sample.Rd: readMM, DelayedArray
  importDropEst.Rd: DelayedArray, readMM
  importExampleData.Rd: scRNAseq, Matrix, DelayedArray,
    ReprocessedFluidigmData, ReprocessedAllenData, NestorowaHSCData
  importFromFiles.Rd: readMM, DelayedArray, SingleCellExperiment-class
  importGeneSetsFromCollection.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GeneSetCollection, GSEABase, metadata
  importGeneSetsFromGMT.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, getGmt, GSEABase, metadata
  importGeneSetsFromList.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GSEABase, metadata
  importGeneSetsFromMSigDB.Rd: SingleCellExperiment-class, msigdbr,
    GeneSetCollection-class, GSEABase, metadata
  importMitoGeneSet.Rd: SingleCellExperiment-class,
    GeneSetCollection-class, GSEABase, metadata
  importMultipleSources.Rd: DelayedArray
  importOptimus.Rd: readMM, DelayedArray
  importSEQC.Rd: readMM, DelayedArray
  importSTARsolo.Rd: readMM, DelayedArray
  iterateSimulations.Rd: SingleCellExperiment-class
  listSampleSummaryStatsTables.Rd: SingleCellExperiment-class, metadata
  plotBarcodeRankDropsResults.Rd: SingleCellExperiment-class
  plotBarcodeRankScatter.Rd: SingleCellExperiment-class
  plotBatchCorrCompare.Rd: SingleCellExperiment-class
  plotBatchVariance.Rd: SingleCellExperiment-class
  plotBcdsResults.Rd: SingleCellExperiment-class
  plotClusterAbundance.Rd: colData
  plotCxdsResults.Rd: SingleCellExperiment-class
  plotDEGHeatmap.Rd: SingleCellExperiment-class
  plotDEGRegression.Rd: SingleCellExperiment-class
  plotDEGViolin.Rd: SingleCellExperiment-class
  plotDEGVolcano.Rd: SingleCellExperiment-class
  plotDecontXResults.Rd: SingleCellExperiment-class
  plotDoubletFinderResults.Rd: SingleCellExperiment-class
  plotEmptyDropsResults.Rd: SingleCellExperiment-class
  plotEmptyDropsScatter.Rd: SingleCellExperiment-class
  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
plotScDblFinderResults   66.427  0.821  30.763
runScDblFinder           56.510  0.815  21.975
importGeneSetsFromMSigDB 46.118  0.510  46.630
plotDoubletFinderResults 38.342  0.322  38.745
runDoubletFinder         36.377  0.471  36.851
runSeuratSCTransform     29.517  1.013  30.537
plotBatchCorrCompare     13.584  0.248  14.017
importExampleData        10.505  0.452  11.374
plotBcdsResults           9.951  0.143   8.378
plotScdsHybridResults     9.957  0.057   8.707
runUMAP                   8.231  0.178   8.487
plotDecontXResults        8.064  0.153   8.218
runDecontX                7.386  0.267   7.653
plotUMAP                  6.879  0.559   7.520
plotCxdsResults           7.121  0.026   7.226
plotEmptyDropsResults     6.579  0.028   6.608
plotEmptyDropsScatter     6.423  0.018   6.441
runEmptyDrops             6.284  0.012   6.295
detectCellOutlier         5.075  0.116   5.192
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

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


Installation output

singleCellTK.Rcheck/00install.out

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


* installing to library ‘/home/biocbuild/bbs-3.22-bioc/R/site-library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.19.1’
** 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-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 
  0.147   0.037   0.171 

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-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%
[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 
364.292  10.511 338.128 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0010.0020.002
SEG0.0030.0000.003
calcEffectSizes0.1830.0110.195
combineSCE0.7110.0030.714
computeZScore0.2290.0020.232
convertSCEToSeurat4.1020.0584.160
convertSeuratToSCE0.2990.0010.299
dedupRowNames0.0530.0010.053
detectCellOutlier5.0750.1165.192
diffAbundanceFET0.0510.0000.052
discreteColorPalette0.0050.0000.005
distinctColors0.0020.0000.002
downSampleCells0.4740.0570.531
downSampleDepth0.3820.0010.384
expData-ANY-character-method0.1150.0030.119
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.1520.0020.154
expData-set0.1420.0000.142
expData0.1160.0010.118
expDataNames-ANY-method0.1100.0010.112
expDataNames0.1080.0070.115
expDeleteDataTag0.0310.0010.032
expSetDataTag0.0230.0000.024
expTaggedData0.0240.0000.025
exportSCE0.0190.0010.020
exportSCEtoAnnData0.0900.0050.094
exportSCEtoFlatFile0.0860.0090.094
featureIndex0.0330.0020.034
generateSimulatedData0.0490.0000.049
getBiomarker0.0570.0010.058
getDEGTopTable0.6370.0720.709
getDiffAbundanceResults0.0470.0000.047
getEnrichRResult0.5680.0763.013
getFindMarkerTopTable1.4690.0181.486
getMSigDBTable0.0030.0000.003
getPathwayResultNames0.0210.0000.022
getSampleSummaryStatsTable0.1710.0030.176
getSoupX000
getTSCANResults1.0520.0381.090
getTopHVG0.7630.0150.778
importAnnData0.0020.0010.001
importBUStools0.1440.0000.145
importCellRanger0.7040.0200.725
importCellRangerV2Sample0.1360.0000.136
importCellRangerV3Sample0.2810.0060.287
importDropEst0.1930.0000.194
importExampleData10.505 0.45211.374
importGeneSetsFromCollection0.7100.0190.729
importGeneSetsFromGMT0.0620.0020.065
importGeneSetsFromList0.1290.0000.129
importGeneSetsFromMSigDB46.118 0.51046.630
importMitoGeneSet0.0530.0040.057
importOptimus0.0020.0000.001
importSEQC0.1470.0230.170
importSTARsolo0.1520.0260.178
iterateSimulations0.1870.0220.208
listSampleSummaryStatsTables0.2610.0290.289
mergeSCEColData0.3820.0490.432
mouseBrainSubsetSCE0.0360.0010.037
msigdb_table0.0010.0000.001
plotBarcodeRankDropsResults0.5960.0030.600
plotBarcodeRankScatter0.6480.0040.652
plotBatchCorrCompare13.584 0.24814.017
plotBatchVariance0.3160.0000.316
plotBcdsResults9.9510.1438.378
plotBubble0.7780.0280.806
plotClusterAbundance0.8190.0010.819
plotCxdsResults7.1210.0267.226
plotDEGHeatmap2.1330.0102.144
plotDEGRegression3.4830.0253.484
plotDEGViolin4.1890.0464.212
plotDEGVolcano0.8760.0110.888
plotDecontXResults8.0640.1538.218
plotDimRed0.2350.0000.235
plotDoubletFinderResults38.342 0.32238.745
plotEmptyDropsResults6.5790.0286.608
plotEmptyDropsScatter6.4230.0186.441
plotFindMarkerHeatmap3.7270.0203.748
plotMASTThresholdGenes1.2490.0061.255
plotPCA0.3540.0020.357
plotPathway0.5970.0120.609
plotRunPerCellQCResults1.9280.0191.949
plotSCEBarAssayData0.1870.0010.188
plotSCEBarColData0.1390.0010.140
plotSCEBatchFeatureMean0.2530.0000.253
plotSCEDensity0.2050.0010.206
plotSCEDensityAssayData0.1620.0020.163
plotSCEDensityColData0.2040.0000.204
plotSCEDimReduceColData0.4890.0000.489
plotSCEDimReduceFeatures0.2620.0000.263
plotSCEHeatmap0.5130.0020.516
plotSCEScatter0.2420.0010.244
plotSCEViolin0.2410.0040.244
plotSCEViolinAssayData0.2530.0010.254
plotSCEViolinColData0.2450.0010.246
plotScDblFinderResults66.427 0.82130.763
plotScanpyDotPlot0.0220.0000.022
plotScanpyEmbedding0.0220.0000.021
plotScanpyHVG0.0220.0000.022
plotScanpyHeatmap0.0210.0000.021
plotScanpyMarkerGenes0.0200.0010.021
plotScanpyMarkerGenesDotPlot0.0210.0000.022
plotScanpyMarkerGenesHeatmap0.0210.0000.021
plotScanpyMarkerGenesMatrixPlot0.0220.0000.023
plotScanpyMarkerGenesViolin0.0190.0020.022
plotScanpyMatrixPlot0.0210.0000.022
plotScanpyPCA0.0210.0000.021
plotScanpyPCAGeneRanking0.0210.0000.021
plotScanpyPCAVariance0.0220.0000.021
plotScanpyViolin0.0210.0000.022
plotScdsHybridResults9.9570.0578.707
plotScrubletResults0.0210.0000.021
plotSeuratElbow0.0210.0000.021
plotSeuratHVG0.020.000.02
plotSeuratJackStraw0.0210.0000.021
plotSeuratReduction0.0220.0000.022
plotSoupXResults000
plotTSCANClusterDEG3.7650.1793.945
plotTSCANClusterPseudo1.1980.0771.276
plotTSCANDimReduceFeatures1.2500.0721.322
plotTSCANPseudotimeGenes1.3790.0511.430
plotTSCANPseudotimeHeatmap1.3070.0451.352
plotTSCANResults1.1990.0631.261
plotTSNE0.3000.0140.314
plotTopHVG0.5000.0350.535
plotUMAP6.8790.5597.520
readSingleCellMatrix0.0050.0000.006
reportCellQC0.0800.0020.082
reportDropletQC0.0240.0000.025
reportQCTool0.0800.0010.082
retrieveSCEIndex0.0290.0020.032
runBBKNN000
runBarcodeRankDrops0.2170.0010.217
runBcds2.9330.0931.246
runCellQC0.0780.0100.088
runClusterSummaryMetrics0.3700.0160.386
runComBatSeq0.4290.0070.435
runCxds0.3250.0040.329
runCxdsBcdsHybrid3.0650.1281.420
runDEAnalysis0.3590.0070.366
runDecontX7.3860.2677.653
runDimReduce0.2740.0000.274
runDoubletFinder36.377 0.47136.851
runDropletQC0.0220.0010.023
runEmptyDrops6.2840.0126.295
runEnrichR0.4680.0262.710
runFastMNN1.7370.0691.806
runFeatureSelection0.2090.0010.211
runFindMarker1.4040.0221.427
runGSVA0.7590.0100.768
runHarmony0.0380.0010.038
runKMeans0.1720.0010.172
runLimmaBC0.0760.0010.077
runMNNCorrect0.3980.0050.403
runModelGeneVar0.3030.0010.304
runNormalization2.4830.0912.574
runPerCellQC0.3410.0110.352
runSCANORAMA000
runSCMerge0.0040.0000.004
runScDblFinder56.510 0.81521.975
runScanpyFindClusters0.0210.0020.023
runScanpyFindHVG0.0220.0000.023
runScanpyFindMarkers0.0220.0010.023
runScanpyNormalizeData0.0980.0000.097
runScanpyPCA0.0240.0000.024
runScanpyScaleData0.0230.0000.023
runScanpyTSNE0.0230.0000.024
runScanpyUMAP0.0220.0000.023
runScranSNN0.2890.0090.298
runScrublet0.0220.0010.022
runSeuratFindClusters0.0220.0010.023
runSeuratFindHVG0.4650.0040.470
runSeuratHeatmap0.0230.0000.023
runSeuratICA0.0220.0020.022
runSeuratJackStraw0.0230.0000.023
runSeuratNormalizeData0.0230.0000.023
runSeuratPCA0.0210.0010.022
runSeuratSCTransform29.517 1.01330.537
runSeuratScaleData0.0240.0000.024
runSeuratUMAP0.0220.0000.023
runSingleR0.0380.0010.039
runSoupX000
runTSCAN0.6640.0070.670
runTSCANClusterDEAnalysis0.8070.0160.823
runTSCANDEG0.7750.0210.796
runTSNE0.7350.0030.738
runUMAP8.2310.1788.487
runVAM0.3210.0010.322
runZINBWaVE0.0050.0000.004
sampleSummaryStats0.1720.0010.173
scaterCPM0.1380.0040.142
scaterPCA0.4590.0090.467
scaterlogNormCounts0.2340.0020.236
sce0.0220.0000.023
sctkListGeneSetCollections0.0860.0020.088
sctkPythonInstallConda0.0000.0000.001
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0850.0050.089
setSCTKDisplayRow0.3250.0100.335
singleCellTK000
subDiffEx0.3490.0050.354
subsetSCECols0.0870.0020.089
subsetSCERows0.2940.0060.299
summarizeSCE0.0760.0000.076
trimCounts0.2170.0020.218