Back to Multiple platform build/check report for BioC 3.22:   simplified   long
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This page was generated on 2025-10-03 12:07 -0400 (Fri, 03 Oct 2025).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 24.04.3 LTS)x86_644.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" 4845
lconwaymacOS 12.7.1 Montereyx86_644.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" 4632
kjohnson3macOS 13.7.7 Venturaarm644.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" 4577
taishanLinux (openEuler 24.03 LTS)aarch644.5.0 (2025-04-11) -- "How About a Twenty-Six" 4576
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 2011/2337HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.19.2  (landing page)
Joshua David Campbell
Snapshot Date: 2025-10-02 13:45 -0400 (Thu, 02 Oct 2025)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: 238aed05
git_last_commit_date: 2025-09-26 08:22:06 -0400 (Fri, 26 Sep 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 taishan

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.
- See Martin Grigorov's blog post for how to debug Linux ARM64 related issues on a x86_64 host.

raw results


Summary

Package: singleCellTK
Version: 2.19.2
Command: /home/biocbuild/R/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings singleCellTK_2.19.2.tar.gz
StartedAt: 2025-10-03 11:41:14 -0000 (Fri, 03 Oct 2025)
EndedAt: 2025-10-03 12:03:40 -0000 (Fri, 03 Oct 2025)
EllapsedTime: 1345.7 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings singleCellTK_2.19.2.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.0 (2025-04-11)
* using platform: aarch64-unknown-linux-gnu
* R was compiled by
    aarch64-unknown-linux-gnu-gcc (GCC) 14.2.0
    GNU Fortran (GCC) 14.2.0
* running under: openEuler 24.03 (LTS)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.19.2’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 80 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable.  Move as many as possible to Suggests and
use conditionally.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... INFO
  installed size is  7.0Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    extdata   1.6Mb
    shiny     3.0Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
  dedupRowNames.Rd: SingleCellExperiment-class
  detectCellOutlier.Rd: colData
  diffAbundanceFET.Rd: colData
  downSampleCells.Rd: SingleCellExperiment-class
  downSampleDepth.Rd: SingleCellExperiment-class
  featureIndex.Rd: SummarizedExperiment-class,
    SingleCellExperiment-class
  getBiomarker.Rd: SingleCellExperiment-class
  getDEGTopTable.Rd: SingleCellExperiment-class
  getEnrichRResult.Rd: SingleCellExperiment-class
  getFindMarkerTopTable.Rd: SingleCellExperiment-class
  getGenesetNamesFromCollection.Rd: SingleCellExperiment-class
  getPathwayResultNames.Rd: SingleCellExperiment-class
  getSampleSummaryStatsTable.Rd: SingleCellExperiment-class, assay,
    colData
  getSoupX.Rd: SingleCellExperiment-class
  getTSCANResults.Rd: SingleCellExperiment-class
  getTopHVG.Rd: SingleCellExperiment-class
  importAlevin.Rd: DelayedArray, readMM
  importAnnData.Rd: DelayedArray, readMM
  importBUStools.Rd: readMM
  importCellRanger.Rd: readMM, DelayedArray
  importCellRangerV2Sample.Rd: readMM, DelayedArray
  importCellRangerV3Sample.Rd: readMM, DelayedArray
  importDropEst.Rd: DelayedArray, readMM
  importExampleData.Rd: scRNAseq, Matrix, DelayedArray,
    ReprocessedFluidigmData, ReprocessedAllenData, NestorowaHSCData
  importFromFiles.Rd: readMM, DelayedArray, SingleCellExperiment-class
  importGeneSetsFromCollection.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GeneSetCollection, GSEABase, metadata
  importGeneSetsFromGMT.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, getGmt, GSEABase, metadata
  importGeneSetsFromList.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GSEABase, metadata
  importGeneSetsFromMSigDB.Rd: SingleCellExperiment-class, msigdbr,
    GeneSetCollection-class, GSEABase, metadata
  importMitoGeneSet.Rd: SingleCellExperiment-class,
    GeneSetCollection-class, GSEABase, metadata
  importMultipleSources.Rd: DelayedArray
  importOptimus.Rd: readMM, DelayedArray
  importSEQC.Rd: readMM, DelayedArray
  importSTARsolo.Rd: readMM, DelayedArray
  iterateSimulations.Rd: SingleCellExperiment-class
  listSampleSummaryStatsTables.Rd: SingleCellExperiment-class, metadata
  plotBarcodeRankDropsResults.Rd: SingleCellExperiment-class
  plotBarcodeRankScatter.Rd: SingleCellExperiment-class
  plotBatchCorrCompare.Rd: SingleCellExperiment-class
  plotBatchVariance.Rd: SingleCellExperiment-class
  plotBcdsResults.Rd: SingleCellExperiment-class
  plotClusterAbundance.Rd: colData
  plotCxdsResults.Rd: SingleCellExperiment-class
  plotDEGHeatmap.Rd: SingleCellExperiment-class
  plotDEGRegression.Rd: SingleCellExperiment-class
  plotDEGViolin.Rd: SingleCellExperiment-class
  plotDEGVolcano.Rd: SingleCellExperiment-class
  plotDecontXResults.Rd: SingleCellExperiment-class
  plotDoubletFinderResults.Rd: SingleCellExperiment-class
  plotEmptyDropsResults.Rd: SingleCellExperiment-class
  plotEmptyDropsScatter.Rd: SingleCellExperiment-class
  plotEnrichR.Rd: SingleCellExperiment-class
  plotFindMarkerHeatmap.Rd: SingleCellExperiment-class
  plotPCA.Rd: SingleCellExperiment-class
  plotPathway.Rd: SingleCellExperiment-class
  plotRunPerCellQCResults.Rd: SingleCellExperiment-class
  plotSCEBarAssayData.Rd: SingleCellExperiment-class
  plotSCEBarColData.Rd: SingleCellExperiment-class
  plotSCEBatchFeatureMean.Rd: SingleCellExperiment-class
  plotSCEDensity.Rd: SingleCellExperiment-class
  plotSCEDensityAssayData.Rd: SingleCellExperiment-class
  plotSCEDensityColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceFeatures.Rd: SingleCellExperiment-class
  plotSCEHeatmap.Rd: SingleCellExperiment-class
  plotSCEScatter.Rd: SingleCellExperiment-class
  plotSCEViolin.Rd: SingleCellExperiment-class
  plotSCEViolinAssayData.Rd: SingleCellExperiment-class
  plotSCEViolinColData.Rd: SingleCellExperiment-class
  plotScDblFinderResults.Rd: SingleCellExperiment-class
  plotScdsHybridResults.Rd: SingleCellExperiment-class
  plotScrubletResults.Rd: SingleCellExperiment-class
  plotSoupXResults.Rd: SingleCellExperiment-class
  plotTSCANClusterDEG.Rd: SingleCellExperiment-class
  plotTSCANClusterPseudo.Rd: SingleCellExperiment-class
  plotTSCANDimReduceFeatures.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeGenes.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeHeatmap.Rd: SingleCellExperiment-class
  plotTSCANResults.Rd: SingleCellExperiment-class
  plotTSNE.Rd: SingleCellExperiment-class
  plotUMAP.Rd: SingleCellExperiment-class
  readSingleCellMatrix.Rd: DelayedArray
  reportCellQC.Rd: SingleCellExperiment-class
  reportClusterAbundance.Rd: colData
  reportDiffAbundanceFET.Rd: colData
  retrieveSCEIndex.Rd: SingleCellExperiment-class
  runBBKNN.Rd: SingleCellExperiment-class
  runBarcodeRankDrops.Rd: SingleCellExperiment-class, colData
  runBcds.Rd: SingleCellExperiment-class, colData
  runCellQC.Rd: colData
  runComBatSeq.Rd: SingleCellExperiment-class
  runCxds.Rd: SingleCellExperiment-class, colData
  runCxdsBcdsHybrid.Rd: colData
  runDEAnalysis.Rd: SingleCellExperiment-class
  runDecontX.Rd: colData
  runDimReduce.Rd: SingleCellExperiment-class
  runDoubletFinder.Rd: SingleCellExperiment-class
  runDropletQC.Rd: colData
  runEmptyDrops.Rd: SingleCellExperiment-class, colData
  runEnrichR.Rd: SingleCellExperiment-class
  runFastMNN.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runFeatureSelection.Rd: SingleCellExperiment-class
  runFindMarker.Rd: SingleCellExperiment-class
  runGSVA.Rd: SingleCellExperiment-class
  runHarmony.Rd: SingleCellExperiment-class
  runKMeans.Rd: SingleCellExperiment-class, colData
  runLimmaBC.Rd: SingleCellExperiment-class, assay
  runMNNCorrect.Rd: SingleCellExperiment-class, assay,
    BiocParallelParam-class
  runModelGeneVar.Rd: SingleCellExperiment-class
  runPerCellQC.Rd: SingleCellExperiment-class, BiocParallelParam,
    colData
  runSCANORAMA.Rd: SingleCellExperiment-class, assay
  runSCMerge.Rd: SingleCellExperiment-class, colData, assay,
    BiocParallelParam-class
  runScDblFinder.Rd: SingleCellExperiment-class, colData
  runScranSNN.Rd: SingleCellExperiment-class, reducedDim, assay,
    altExp, colData, igraph
  runScrublet.Rd: SingleCellExperiment-class, colData
  runSingleR.Rd: SingleCellExperiment-class
  runSoupX.Rd: SingleCellExperiment-class
  runTSCAN.Rd: SingleCellExperiment-class
  runTSCANClusterDEAnalysis.Rd: SingleCellExperiment-class
  runTSCANDEG.Rd: SingleCellExperiment-class
  runTSNE.Rd: SingleCellExperiment-class
  runUMAP.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runVAM.Rd: SingleCellExperiment-class
  runZINBWaVE.Rd: SingleCellExperiment-class, colData,
    BiocParallelParam-class
  sampleSummaryStats.Rd: SingleCellExperiment-class, assay, colData
  scaterPCA.Rd: SingleCellExperiment-class, BiocParallelParam-class
  scaterlogNormCounts.Rd: logNormCounts
  sctkListGeneSetCollections.Rd: GeneSetCollection-class
  sctkPythonInstallConda.Rd: conda_install, reticulate, conda_create
  sctkPythonInstallVirtualEnv.Rd: virtualenv_install, reticulate,
    virtualenv_create
  selectSCTKConda.Rd: reticulate
  selectSCTKVirtualEnvironment.Rd: reticulate
  setRowNames.Rd: SingleCellExperiment-class
  setSCTKDisplayRow.Rd: SingleCellExperiment-class
  singleCellTK.Rd: SingleCellExperiment-class
  subsetSCECols.Rd: SingleCellExperiment-class
  subsetSCERows.Rd: SingleCellExperiment-class, altExp
  summarizeSCE.Rd: SingleCellExperiment-class
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
importGeneSetsFromMSigDB 50.756  1.126  52.008
plotDoubletFinderResults 44.787  2.221  49.845
runSeuratSCTransform     45.351  0.812  48.177
runDoubletFinder         37.133  0.079  39.486
plotScDblFinderResults   35.396  0.522  36.959
runScDblFinder           22.818  0.635  23.509
importExampleData        16.060  1.282  23.318
plotBatchCorrCompare     15.159  0.252  16.074
plotBcdsResults          12.053  0.624  14.556
plotScdsHybridResults    12.356  0.111  13.789
plotDecontXResults       10.365  0.251  11.467
plotCxdsResults           8.975  0.467  10.475
plotDEGViolin             8.431  0.266   8.720
runUMAP                   8.450  0.247   9.120
runDecontX                7.681  0.000   7.964
plotUMAP                  7.564  0.038   8.188
plotTSCANClusterDEG       7.399  0.060   8.179
plotDEGRegression         6.938  0.171   7.265
detectCellOutlier         6.324  0.287   6.645
convertSCEToSeurat        6.059  0.422   6.526
plotFindMarkerHeatmap     5.991  0.123   6.173
plotEmptyDropsResults     5.819  0.056   5.883
plotEmptyDropsScatter     5.694  0.060   5.761
runEmptyDrops             5.230  0.020   5.259
plotRunPerCellQCResults   4.965  0.127   5.113
getEnrichRResult          0.468  0.044   9.579
runEnrichR                0.387  0.020   8.673
* 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
  ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/R/R-4.5.0/site-library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.19.2’
** 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 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-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 
  0.185   0.038   0.210 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-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:0.452573 
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%
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 
385.049   7.799 427.707 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0000.002
SEG0.0030.0000.003
calcEffectSizes0.2740.0040.279
combineSCE1.0990.0441.147
computeZScore0.2950.0430.340
convertSCEToSeurat6.0590.4226.526
convertSeuratToSCE0.4870.0040.494
dedupRowNames0.0640.0080.073
detectCellOutlier6.3240.2876.645
diffAbundanceFET0.0630.0000.062
discreteColorPalette0.0080.0000.008
distinctColors0.0030.0000.003
downSampleCells0.7270.0790.809
downSampleDepth0.6120.0050.618
expData-ANY-character-method0.1760.0070.184
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.2220.0040.228
expData-set0.2060.0040.212
expData0.1940.0080.203
expDataNames-ANY-method0.1720.0040.176
expDataNames0.1580.0080.167
expDeleteDataTag0.0340.0080.043
expSetDataTag0.0310.0000.031
expTaggedData0.0320.0000.031
exportSCE0.0270.0000.028
exportSCEtoAnnData0.0740.0040.078
exportSCEtoFlatFile0.0740.0040.078
featureIndex0.0460.0000.046
generateSimulatedData0.0580.0040.062
getBiomarker0.0760.0040.081
getDEGTopTable0.9320.1041.039
getDiffAbundanceResults0.0550.0000.056
getEnrichRResult0.4680.0449.579
getFindMarkerTopTable2.2720.1512.771
getMSigDBTable0.0050.0000.004
getPathwayResultNames0.0250.0040.029
getSampleSummaryStatsTable0.2700.0280.299
getSoupX000
getTSCANResults1.4680.1601.633
getTopHVG1.2460.1031.354
importAnnData0.0020.0000.002
importBUStools0.2540.0200.276
importCellRanger1.1380.0721.219
importCellRangerV2Sample0.2010.0000.202
importCellRangerV3Sample0.4410.0120.455
importDropEst0.3160.0040.358
importExampleData16.060 1.28223.318
importGeneSetsFromCollection1.9650.1442.113
importGeneSetsFromGMT0.0890.0040.092
importGeneSetsFromList0.2040.0080.212
importGeneSetsFromMSigDB50.756 1.12652.008
importMitoGeneSet0.0780.0000.079
importOptimus0.0020.0000.002
importSEQC0.2120.0120.226
importSTARsolo0.2250.0080.235
iterateSimulations0.2290.0240.253
listSampleSummaryStatsTables0.4350.0120.448
mergeSCEColData0.4850.0110.498
mouseBrainSubsetSCE0.0390.0000.038
msigdb_table0.0010.0010.002
plotBarcodeRankDropsResults1.3210.0281.353
plotBarcodeRankScatter1.2610.0031.269
plotBatchCorrCompare15.159 0.25216.074
plotBatchVariance0.6500.0040.656
plotBcdsResults12.053 0.62414.556
plotBubble1.2170.0761.296
plotClusterAbundance2.2300.2272.465
plotCxdsResults 8.975 0.46710.475
plotDEGHeatmap3.2110.1723.392
plotDEGRegression6.9380.1717.265
plotDEGViolin8.4310.2668.720
plotDEGVolcano1.2570.0161.357
plotDecontXResults10.365 0.25111.467
plotDimRed0.4750.0200.496
plotDoubletFinderResults44.787 2.22149.845
plotEmptyDropsResults5.8190.0565.883
plotEmptyDropsScatter5.6940.0605.761
plotFindMarkerHeatmap5.9910.1236.173
plotMASTThresholdGenes2.0130.0322.063
plotPCA0.5500.0120.563
plotPathway1.0290.0161.048
plotRunPerCellQCResults4.9650.1275.113
plotSCEBarAssayData0.5240.0080.533
plotSCEBarColData0.3370.0000.339
plotSCEBatchFeatureMean0.6400.0080.650
plotSCEDensity0.4940.0040.502
plotSCEDensityAssayData0.4680.0040.476
plotSCEDensityColData0.4430.0000.446
plotSCEDimReduceColData1.1790.0041.186
plotSCEDimReduceFeatures0.5400.0000.541
plotSCEHeatmap0.6160.0040.622
plotSCEScatter2.1630.1002.269
plotSCEViolin0.5190.0000.522
plotSCEViolinAssayData0.5790.0000.581
plotSCEViolinColData0.5990.0000.601
plotScDblFinderResults35.396 0.52236.959
plotScanpyDotPlot0.0290.0000.029
plotScanpyEmbedding0.0260.0000.026
plotScanpyHVG0.0250.0000.025
plotScanpyHeatmap0.0260.0000.026
plotScanpyMarkerGenes0.0270.0000.027
plotScanpyMarkerGenesDotPlot0.0260.0000.027
plotScanpyMarkerGenesHeatmap0.0250.0000.025
plotScanpyMarkerGenesMatrixPlot0.0250.0000.025
plotScanpyMarkerGenesViolin0.0210.0040.025
plotScanpyMatrixPlot0.0280.0000.028
plotScanpyPCA0.0290.0000.029
plotScanpyPCAGeneRanking0.0220.0040.025
plotScanpyPCAVariance0.0250.0000.026
plotScanpyViolin0.0250.0000.025
plotScdsHybridResults12.356 0.11113.789
plotScrubletResults0.0200.0040.024
plotSeuratElbow0.0230.0000.024
plotSeuratHVG0.0240.0000.024
plotSeuratJackStraw0.0250.0000.025
plotSeuratReduction0.0240.0000.023
plotSoupXResults000
plotTSCANClusterDEG7.3990.0608.179
plotTSCANClusterPseudo2.0600.0122.078
plotTSCANDimReduceFeatures2.1610.0443.665
plotTSCANPseudotimeGenes2.5300.0243.058
plotTSCANPseudotimeHeatmap2.0060.0282.040
plotTSCANResults1.9240.0201.953
plotTSNE0.5410.0000.543
plotTopHVG0.9210.0201.087
plotUMAP7.5640.0388.188
readSingleCellMatrix0.0070.0000.006
reportCellQC0.1060.0040.111
reportDropletQC0.0240.0000.023
reportQCTool0.1070.0000.107
retrieveSCEIndex0.0290.0000.029
runBBKNN000
runBarcodeRankDrops0.2790.0000.280
runBcds3.2000.0333.237
runCellQC0.1020.0000.103
runClusterSummaryMetrics0.5050.0040.509
runComBatSeq0.6370.0110.859
runCxds0.4130.0040.417
runCxdsBcdsHybrid3.2570.0523.331
runDEAnalysis0.5830.0040.588
runDecontX7.6810.0007.964
runDimReduce0.4050.0000.407
runDoubletFinder37.133 0.07939.486
runDropletQC0.0230.0000.023
runEmptyDrops5.2300.0205.259
runEnrichR0.3870.0208.673
runFastMNN2.4680.1872.665
runFeatureSelection0.2940.0080.304
runFindMarker2.1080.1282.242
runGSVA1.0320.0631.098
runHarmony0.0640.0000.063
runKMeans0.2460.0040.251
runLimmaBC0.2120.0560.269
runMNNCorrect0.5640.0480.614
runModelGeneVar0.4130.0360.451
runNormalization2.7210.3313.059
runPerCellQC0.4780.0240.503
runSCANORAMA0.0000.0000.001
runSCMerge0.0050.0000.005
runScDblFinder22.818 0.63523.509
runScanpyFindClusters0.0240.0000.023
runScanpyFindHVG0.0230.0000.023
runScanpyFindMarkers0.0230.0000.023
runScanpyNormalizeData0.130.000.13
runScanpyPCA0.0230.0000.023
runScanpyScaleData0.0250.0000.025
runScanpyTSNE0.0240.0000.024
runScanpyUMAP0.0230.0000.024
runScranSNN0.3980.0000.398
runScrublet0.0230.0000.023
runSeuratFindClusters0.0230.0000.024
runSeuratFindHVG0.6920.0000.693
runSeuratHeatmap0.0220.0030.025
runSeuratICA0.0210.0040.026
runSeuratJackStraw0.0250.0000.025
runSeuratNormalizeData0.0260.0080.037
runSeuratPCA0.0260.0000.026
runSeuratSCTransform45.351 0.81248.177
runSeuratScaleData0.0270.0000.028
runSeuratUMAP0.0260.0000.026
runSingleR0.0560.0000.056
runSoupX000
runTSCAN0.9790.0080.990
runTSCANClusterDEAnalysis1.0750.0081.086
runTSCANDEG1.1490.0161.169
runTSNE1.0150.0111.031
runUMAP8.4500.2479.120
runVAM0.4610.0200.551
runZINBWaVE0.0010.0040.005
sampleSummaryStats0.2390.0080.248
scaterCPM0.1330.0160.150
scaterPCA0.7030.0440.751
scaterlogNormCounts0.2850.0120.310
sce0.0250.0000.024
sctkListGeneSetCollections0.1170.0000.117
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment0.0000.0000.001
setRowNames0.1200.0080.129
setSCTKDisplayRow0.6290.0280.658
singleCellTK000
subDiffEx0.4370.0190.458
subsetSCECols0.1090.0080.117
subsetSCERows0.4130.0320.447
summarizeSCE0.0920.0000.092
trimCounts0.2350.0040.240