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This page was generated on 2025-08-07 12:05 -0400 (Thu, 07 Aug 2025).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 24.04.2 LTS)x86_644.5.1 (2025-06-13) -- "Great Square Root" 4815
palomino8Windows Server 2022 Datacenterx644.5.1 (2025-06-13 ucrt) -- "Great Square Root" 4550
lconwaymacOS 12.7.1 Montereyx86_644.5.1 (2025-06-13) -- "Great Square Root" 4592
kjohnson3macOS 13.7.1 Venturaarm644.5.1 Patched (2025-06-14 r88325) -- "Great Square Root" 4534
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 1993/2315HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.19.1  (landing page)
Joshua David Campbell
Snapshot Date: 2025-08-06 14:04 -0400 (Wed, 06 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 24.04.2 LTS) / x86_64  OK    OK    OK  NO, package depends on 'MAST' which is not available
palomino8Windows Server 2022 Datacenter / x64  OK    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 13.7.1 Ventura / arm64  OK    OK    OK    OK  NO, package depends on 'MAST' which is not available


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: 2025-08-07 02:56:30 -0400 (Thu, 07 Aug 2025)
EndedAt: 2025-08-07 03:13:08 -0400 (Thu, 07 Aug 2025)
EllapsedTime: 997.9 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 24.04.2 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
importGeneSetsFromMSigDB 45.623  0.632  46.257
plotDoubletFinderResults 36.057  0.246  36.384
runDoubletFinder         35.800  0.143  35.947
runSeuratSCTransform     29.521  1.708  31.234
plotScDblFinderResults   30.181  0.615  30.517
runScDblFinder           20.098  0.419  20.183
plotBatchCorrCompare     13.150  0.015  13.350
importExampleData        10.559  0.372  11.308
plotScdsHybridResults     9.747  0.120   9.153
plotDecontXResults        8.772  0.093   8.866
plotBcdsResults           8.565  0.101   7.956
runUMAP                   7.729  0.498   8.308
runDecontX                7.707  0.059   7.766
plotUMAP                  7.493  0.103   7.676
plotCxdsResults           6.915  0.089   7.081
plotEmptyDropsResults     6.561  0.012   6.574
plotEmptyDropsScatter     6.431  0.017   6.448
runEmptyDrops             6.263  0.014   6.276
* 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)
NULL
> 
> proc.time()
   user  system elapsed 
  0.145   0.034   0.169 

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 
304.780   5.114 314.122 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0010.0000.002
SEG0.0020.0010.002
calcEffectSizes0.1750.0140.191
combineSCE0.6960.0010.698
computeZScore0.2260.0100.236
convertSCEToSeurat4.1400.0554.197
convertSeuratToSCE0.3060.0000.306
dedupRowNames0.0570.0000.056
detectCellOutlier4.8000.0644.866
diffAbundanceFET0.0530.0000.053
discreteColorPalette0.0060.0000.005
distinctColors0.0020.0000.002
downSampleCells0.4460.0550.501
downSampleDepth0.3800.0050.385
expData-ANY-character-method0.1160.0020.118
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.1570.0000.157
expData-set0.1450.0000.145
expData0.120.000.12
expDataNames-ANY-method0.1140.0000.114
expDataNames0.1130.0000.113
expDeleteDataTag0.0320.0000.032
expSetDataTag0.0230.0000.024
expTaggedData0.0240.0000.024
exportSCE0.0200.0010.021
exportSCEtoAnnData0.0950.0020.096
exportSCEtoFlatFile0.0930.0040.097
featureIndex0.0360.0000.035
generateSimulatedData0.0480.0000.049
getBiomarker0.0550.0020.057
getDEGTopTable0.6540.0670.721
getDiffAbundanceResults0.0480.0000.048
getEnrichRResult0.5760.0402.952
getFindMarkerTopTable1.4560.0171.472
getMSigDBTable0.0040.0000.003
getPathwayResultNames0.0220.0000.021
getSampleSummaryStatsTable0.1750.0000.176
getSoupX000
getTSCANResults1.0150.0121.027
getTopHVG0.7630.0000.764
importAnnData0.0010.0000.002
importBUStools0.1560.0010.157
importCellRanger0.7140.0130.728
importCellRangerV2Sample0.140.000.14
importCellRangerV3Sample0.2990.0020.302
importDropEst0.1970.0010.199
importExampleData10.559 0.37211.308
importGeneSetsFromCollection0.7400.0330.772
importGeneSetsFromGMT0.0640.0020.066
importGeneSetsFromList0.1230.0020.125
importGeneSetsFromMSigDB45.623 0.63246.257
importMitoGeneSet0.0530.0010.054
importOptimus0.0000.0010.002
importSEQC0.1460.0120.159
importSTARsolo0.1410.0300.171
iterateSimulations0.1720.0260.198
listSampleSummaryStatsTables0.2510.0260.277
mergeSCEColData0.3700.0490.420
mouseBrainSubsetSCE0.0360.0010.037
msigdb_table0.0010.0000.001
plotBarcodeRankDropsResults0.5660.0030.569
plotBarcodeRankScatter0.640.000.64
plotBatchCorrCompare13.150 0.01513.350
plotBatchVariance0.3260.0010.327
plotBcdsResults8.5650.1017.956
plotBubble0.7420.0190.761
plotClusterAbundance0.8280.0110.839
plotCxdsResults6.9150.0897.081
plotDEGHeatmap2.0490.0132.063
plotDEGRegression3.2050.0053.205
plotDEGViolin3.8940.0823.971
plotDEGVolcano0.8390.0070.846
plotDecontXResults8.7720.0938.866
plotDimRed0.2400.0010.241
plotDoubletFinderResults36.057 0.24636.384
plotEmptyDropsResults6.5610.0126.574
plotEmptyDropsScatter6.4310.0176.448
plotFindMarkerHeatmap3.7640.0333.798
plotMASTThresholdGenes1.2640.0021.265
plotPCA0.3440.0010.346
plotPathway0.4940.0050.499
plotRunPerCellQCResults1.9390.0531.992
plotSCEBarAssayData0.1900.0000.191
plotSCEBarColData0.1430.0000.144
plotSCEBatchFeatureMean0.2780.0010.279
plotSCEDensity0.2250.0010.226
plotSCEDensityAssayData0.1740.0010.175
plotSCEDensityColData0.2150.0010.216
plotSCEDimReduceColData0.5280.0020.530
plotSCEDimReduceFeatures0.2650.0020.266
plotSCEHeatmap0.4760.0000.476
plotSCEScatter0.2540.0020.256
plotSCEViolin0.2430.0010.245
plotSCEViolinAssayData0.2610.0020.263
plotSCEViolinColData0.2540.0000.254
plotScDblFinderResults30.181 0.61530.517
plotScanpyDotPlot0.0220.0000.022
plotScanpyEmbedding0.0210.0000.020
plotScanpyHVG0.0200.0010.021
plotScanpyHeatmap0.0210.0000.021
plotScanpyMarkerGenes0.0210.0000.021
plotScanpyMarkerGenesDotPlot0.0220.0000.022
plotScanpyMarkerGenesHeatmap0.0200.0010.021
plotScanpyMarkerGenesMatrixPlot0.0200.0010.021
plotScanpyMarkerGenesViolin0.0210.0010.021
plotScanpyMatrixPlot0.0210.0000.021
plotScanpyPCA0.0220.0000.021
plotScanpyPCAGeneRanking0.0220.0000.022
plotScanpyPCAVariance0.0210.0000.022
plotScanpyViolin0.0200.0010.022
plotScdsHybridResults9.7470.1209.153
plotScrubletResults0.0220.0000.022
plotSeuratElbow0.0200.0010.022
plotSeuratHVG0.0220.0000.021
plotSeuratJackStraw0.0210.0010.022
plotSeuratReduction0.0210.0000.022
plotSoupXResults000
plotTSCANClusterDEG3.5890.0513.641
plotTSCANClusterPseudo1.1600.0041.164
plotTSCANDimReduceFeatures1.1890.0041.193
plotTSCANPseudotimeGenes1.3620.0051.367
plotTSCANPseudotimeHeatmap1.3710.0031.374
plotTSCANResults1.1860.0031.190
plotTSNE0.3090.0010.309
plotTopHVG0.5290.0000.529
plotUMAP7.4930.1037.676
readSingleCellMatrix0.0040.0010.005
reportCellQC0.0760.0010.076
reportDropletQC0.0220.0000.021
reportQCTool0.0760.0000.076
retrieveSCEIndex0.0260.0010.028
runBBKNN000
runBarcodeRankDrops0.2100.0000.209
runBcds1.9230.0071.142
runCellQC0.0760.0010.077
runClusterSummaryMetrics0.3580.0000.358
runComBatSeq0.4170.0010.418
runCxds0.3000.0020.302
runCxdsBcdsHybrid2.0540.0761.311
runDEAnalysis0.3560.0010.357
runDecontX7.7070.0597.766
runDimReduce0.2750.0010.276
runDoubletFinder35.800 0.14335.947
runDropletQC0.0210.0010.022
runEmptyDrops6.2630.0146.276
runEnrichR0.5480.0242.673
runFastMNN1.6310.0111.643
runFeatureSelection0.2060.0000.206
runFindMarker1.3910.0021.393
runGSVA0.7010.0150.717
runHarmony0.0370.0000.037
runKMeans0.1680.0010.169
runLimmaBC0.0720.0100.082
runMNNCorrect0.3940.0030.397
runModelGeneVar0.2950.0060.301
runNormalization2.4570.0882.547
runPerCellQC0.3250.0010.326
runSCANORAMA000
runSCMerge0.0030.0010.004
runScDblFinder20.098 0.41920.183
runScanpyFindClusters0.0230.0000.024
runScanpyFindHVG0.0220.0000.023
runScanpyFindMarkers0.0220.0000.022
runScanpyNormalizeData0.0930.0010.094
runScanpyPCA0.0220.0000.022
runScanpyScaleData0.0210.0000.022
runScanpyTSNE0.0220.0000.022
runScanpyUMAP0.0210.0000.022
runScranSNN0.2930.0000.293
runScrublet0.0230.0000.023
runSeuratFindClusters0.0230.0000.023
runSeuratFindHVG0.5120.0020.515
runSeuratHeatmap0.0240.0000.024
runSeuratICA0.0240.0000.023
runSeuratJackStraw0.0240.0000.024
runSeuratNormalizeData0.0230.0000.023
runSeuratPCA0.0240.0000.023
runSeuratSCTransform29.521 1.70831.234
runSeuratScaleData0.0240.0000.023
runSeuratUMAP0.0240.0000.024
runSingleR0.0390.0000.038
runSoupX000
runTSCAN0.7230.0310.753
runTSCANClusterDEAnalysis0.7990.0370.836
runTSCANDEG0.8040.0500.854
runTSNE0.7240.0300.754
runUMAP7.7290.4988.308
runVAM0.3060.0130.318
runZINBWaVE0.0050.0000.004
sampleSummaryStats0.1600.0020.162
scaterCPM0.1330.0130.147
scaterPCA0.4610.0130.473
scaterlogNormCounts0.2400.0200.261
sce0.0240.0000.024
sctkListGeneSetCollections0.0860.0030.088
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0910.0010.092
setSCTKDisplayRow0.3250.0180.343
singleCellTK000
subDiffEx0.3450.0050.350
subsetSCECols0.0860.0090.095
subsetSCERows0.2890.0280.316
summarizeSCE0.0730.0000.073
trimCounts0.2120.0120.224