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This page was generated on 2025-08-08 12:09 -0400 (Fri, 08 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: 2025-08-07 13:45 -0400 (Thu, 07 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 lconway

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

raw results


Summary

Package: singleCellTK
Version: 2.19.1
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.19.1.tar.gz
StartedAt: 2025-08-08 00:09:24 -0400 (Fri, 08 Aug 2025)
EndedAt: 2025-08-08 00:27:27 -0400 (Fri, 08 Aug 2025)
EllapsedTime: 1082.9 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

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


* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.1 (2025-06-13)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.19.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 79 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable.  Move as many as possible to Suggests and
use conditionally.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... INFO
  installed size is  6.8Mb
  sub-directories of 1Mb or more:
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
  dedupRowNames.Rd: SingleCellExperiment-class
  detectCellOutlier.Rd: colData
  diffAbundanceFET.Rd: colData
  downSampleCells.Rd: SingleCellExperiment-class
  downSampleDepth.Rd: SingleCellExperiment-class
  featureIndex.Rd: SummarizedExperiment-class,
    SingleCellExperiment-class
  getBiomarker.Rd: SingleCellExperiment-class
  getDEGTopTable.Rd: SingleCellExperiment-class
  getEnrichRResult.Rd: SingleCellExperiment-class
  getFindMarkerTopTable.Rd: SingleCellExperiment-class
  getGenesetNamesFromCollection.Rd: SingleCellExperiment-class
  getPathwayResultNames.Rd: SingleCellExperiment-class
  getSampleSummaryStatsTable.Rd: SingleCellExperiment-class, assay,
    colData
  getSoupX.Rd: SingleCellExperiment-class
  getTSCANResults.Rd: SingleCellExperiment-class
  getTopHVG.Rd: SingleCellExperiment-class
  importAlevin.Rd: DelayedArray, readMM
  importAnnData.Rd: DelayedArray, readMM
  importBUStools.Rd: readMM
  importCellRanger.Rd: readMM, DelayedArray
  importCellRangerV2Sample.Rd: readMM, DelayedArray
  importCellRangerV3Sample.Rd: readMM, DelayedArray
  importDropEst.Rd: DelayedArray, readMM
  importExampleData.Rd: scRNAseq, Matrix, DelayedArray,
    ReprocessedFluidigmData, ReprocessedAllenData, NestorowaHSCData
  importFromFiles.Rd: readMM, DelayedArray, SingleCellExperiment-class
  importGeneSetsFromCollection.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GeneSetCollection, GSEABase, metadata
  importGeneSetsFromGMT.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, getGmt, GSEABase, metadata
  importGeneSetsFromList.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GSEABase, metadata
  importGeneSetsFromMSigDB.Rd: SingleCellExperiment-class, msigdbr,
    GeneSetCollection-class, GSEABase, metadata
  importMitoGeneSet.Rd: SingleCellExperiment-class,
    GeneSetCollection-class, GSEABase, metadata
  importMultipleSources.Rd: DelayedArray
  importOptimus.Rd: readMM, DelayedArray
  importSEQC.Rd: readMM, DelayedArray
  importSTARsolo.Rd: readMM, DelayedArray
  iterateSimulations.Rd: SingleCellExperiment-class
  listSampleSummaryStatsTables.Rd: SingleCellExperiment-class, metadata
  plotBarcodeRankDropsResults.Rd: SingleCellExperiment-class
  plotBarcodeRankScatter.Rd: SingleCellExperiment-class
  plotBatchCorrCompare.Rd: SingleCellExperiment-class
  plotBatchVariance.Rd: SingleCellExperiment-class
  plotBcdsResults.Rd: SingleCellExperiment-class
  plotClusterAbundance.Rd: colData
  plotCxdsResults.Rd: SingleCellExperiment-class
  plotDEGHeatmap.Rd: SingleCellExperiment-class
  plotDEGRegression.Rd: SingleCellExperiment-class
  plotDEGViolin.Rd: SingleCellExperiment-class
  plotDEGVolcano.Rd: SingleCellExperiment-class
  plotDecontXResults.Rd: SingleCellExperiment-class
  plotDoubletFinderResults.Rd: SingleCellExperiment-class
  plotEmptyDropsResults.Rd: SingleCellExperiment-class
  plotEmptyDropsScatter.Rd: SingleCellExperiment-class
  plotFindMarkerHeatmap.Rd: SingleCellExperiment-class
  plotPCA.Rd: SingleCellExperiment-class
  plotPathway.Rd: SingleCellExperiment-class
  plotRunPerCellQCResults.Rd: SingleCellExperiment-class
  plotSCEBarAssayData.Rd: SingleCellExperiment-class
  plotSCEBarColData.Rd: SingleCellExperiment-class
  plotSCEBatchFeatureMean.Rd: SingleCellExperiment-class
  plotSCEDensity.Rd: SingleCellExperiment-class
  plotSCEDensityAssayData.Rd: SingleCellExperiment-class
  plotSCEDensityColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceFeatures.Rd: SingleCellExperiment-class
  plotSCEHeatmap.Rd: SingleCellExperiment-class
  plotSCEScatter.Rd: SingleCellExperiment-class
  plotSCEViolin.Rd: SingleCellExperiment-class
  plotSCEViolinAssayData.Rd: SingleCellExperiment-class
  plotSCEViolinColData.Rd: SingleCellExperiment-class
  plotScDblFinderResults.Rd: SingleCellExperiment-class
  plotScdsHybridResults.Rd: SingleCellExperiment-class
  plotScrubletResults.Rd: SingleCellExperiment-class
  plotSoupXResults.Rd: SingleCellExperiment-class
  plotTSCANClusterDEG.Rd: SingleCellExperiment-class
  plotTSCANClusterPseudo.Rd: SingleCellExperiment-class
  plotTSCANDimReduceFeatures.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeGenes.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeHeatmap.Rd: SingleCellExperiment-class
  plotTSCANResults.Rd: SingleCellExperiment-class
  plotTSNE.Rd: SingleCellExperiment-class
  plotUMAP.Rd: SingleCellExperiment-class
  readSingleCellMatrix.Rd: DelayedArray
  reportCellQC.Rd: SingleCellExperiment-class
  reportClusterAbundance.Rd: colData
  reportDiffAbundanceFET.Rd: colData
  retrieveSCEIndex.Rd: SingleCellExperiment-class
  runBBKNN.Rd: SingleCellExperiment-class
  runBarcodeRankDrops.Rd: SingleCellExperiment-class, colData
  runBcds.Rd: SingleCellExperiment-class, colData
  runCellQC.Rd: colData
  runComBatSeq.Rd: SingleCellExperiment-class
  runCxds.Rd: SingleCellExperiment-class, colData
  runCxdsBcdsHybrid.Rd: colData
  runDEAnalysis.Rd: SingleCellExperiment-class
  runDecontX.Rd: colData
  runDimReduce.Rd: SingleCellExperiment-class
  runDoubletFinder.Rd: SingleCellExperiment-class
  runDropletQC.Rd: colData
  runEmptyDrops.Rd: SingleCellExperiment-class, colData
  runEnrichR.Rd: SingleCellExperiment-class
  runFastMNN.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runFeatureSelection.Rd: SingleCellExperiment-class
  runFindMarker.Rd: SingleCellExperiment-class
  runGSVA.Rd: SingleCellExperiment-class
  runHarmony.Rd: SingleCellExperiment-class
  runKMeans.Rd: SingleCellExperiment-class, colData
  runLimmaBC.Rd: SingleCellExperiment-class, assay
  runMNNCorrect.Rd: SingleCellExperiment-class, assay,
    BiocParallelParam-class
  runModelGeneVar.Rd: SingleCellExperiment-class
  runPerCellQC.Rd: SingleCellExperiment-class, BiocParallelParam,
    colData
  runSCANORAMA.Rd: SingleCellExperiment-class, assay
  runSCMerge.Rd: SingleCellExperiment-class, colData, assay,
    BiocParallelParam-class
  runScDblFinder.Rd: SingleCellExperiment-class, colData
  runScranSNN.Rd: SingleCellExperiment-class, reducedDim, assay,
    altExp, colData, igraph
  runScrublet.Rd: SingleCellExperiment-class, colData
  runSingleR.Rd: SingleCellExperiment-class
  runSoupX.Rd: SingleCellExperiment-class
  runTSCAN.Rd: SingleCellExperiment-class
  runTSCANClusterDEAnalysis.Rd: SingleCellExperiment-class
  runTSCANDEG.Rd: SingleCellExperiment-class
  runTSNE.Rd: SingleCellExperiment-class
  runUMAP.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runVAM.Rd: SingleCellExperiment-class
  runZINBWaVE.Rd: SingleCellExperiment-class, colData,
    BiocParallelParam-class
  sampleSummaryStats.Rd: SingleCellExperiment-class, assay, colData
  scaterPCA.Rd: SingleCellExperiment-class, BiocParallelParam-class
  scaterlogNormCounts.Rd: logNormCounts
  sctkListGeneSetCollections.Rd: GeneSetCollection-class
  sctkPythonInstallConda.Rd: conda_install, reticulate, conda_create
  sctkPythonInstallVirtualEnv.Rd: virtualenv_install, reticulate,
    virtualenv_create
  selectSCTKConda.Rd: reticulate
  selectSCTKVirtualEnvironment.Rd: reticulate
  setRowNames.Rd: SingleCellExperiment-class
  setSCTKDisplayRow.Rd: SingleCellExperiment-class
  singleCellTK.Rd: SingleCellExperiment-class
  subsetSCECols.Rd: SingleCellExperiment-class
  subsetSCERows.Rd: SingleCellExperiment-class, altExp
  summarizeSCE.Rd: SingleCellExperiment-class
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
importGeneSetsFromMSigDB 51.459  0.556  52.321
plotDoubletFinderResults 42.221  0.252  42.653
runDoubletFinder         37.109  0.230  37.499
plotScDblFinderResults   32.126  0.882  33.165
runScDblFinder           21.444  0.500  22.038
importExampleData        15.141  1.811  17.430
plotBatchCorrCompare     13.694  0.131  13.883
plotScdsHybridResults    10.046  0.123  10.217
plotBcdsResults           9.391  0.169   9.611
plotDecontXResults        9.165  0.100   9.301
runDecontX                7.842  0.057   7.936
plotUMAP                  7.665  0.078   7.783
plotCxdsResults           7.565  0.070   7.659
runUMAP                   7.341  0.070   7.444
plotEmptyDropsScatter     6.188  0.032   6.255
plotEmptyDropsResults     6.131  0.044   6.207
runEmptyDrops             5.802  0.035   5.866
detectCellOutlier         5.522  0.165   5.719
plotDEGViolin             4.965  0.087   5.078
getEnrichRResult          0.325  0.045   6.103
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

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


Installation output

singleCellTK.Rcheck/00install.out

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


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.19.1’
** using staged installation
** R
** data
** exec
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (singleCellTK)

Tests output

singleCellTK.Rcheck/tests/spelling.Rout


R version 4.5.1 (2025-06-13) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
NULL
> 
> proc.time()
   user  system elapsed 
  0.224   0.083   0.324 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.1 (2025-06-13) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(singleCellTK)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

The following objects are masked from 'package:matrixStats':

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: generics

Attaching package: 'generics'

The following objects are masked from 'package:base':

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


Attaching package: 'BiocGenerics'

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:utils':

    findMatches

The following objects are masked from 'package:base':

    I, expand.grid, unname

Loading required package: IRanges
Loading required package: Seqinfo
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

The following objects are masked from 'package:matrixStats':

    anyMissing, rowMedians

Loading required package: SingleCellExperiment
Loading required package: DelayedArray
Loading required package: Matrix

Attaching package: 'Matrix'

The following object is masked from 'package:S4Vectors':

    expand

Loading required package: S4Arrays
Loading required package: abind

Attaching package: 'S4Arrays'

The following object is masked from 'package:abind':

    abind

The following object is masked from 'package:base':

    rowsum

Loading required package: SparseArray

Attaching package: 'DelayedArray'

The following objects are masked from 'package:base':

    apply, scale, sweep


Attaching package: 'singleCellTK'

The following object is masked from 'package:BiocGenerics':

    plotPCA

> 
> test_check("singleCellTK")
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 0 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 1 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Uploading data to Enrichr... Done.
  Querying HDSigDB_Human_2021... Done.
Parsing results... Done.
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
[1]	train-logloss:0.452540 
Will train until train_logloss hasn't improved in 2 rounds.

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

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

[2]	train-logloss:0.321019 
[3]	train-logloss:0.238344 
[4]	train-logloss:0.183572 
[5]	train-logloss:0.145515 
[6]	train-logloss:0.118784 
[7]	train-logloss:0.100283 
[8]	train-logloss:0.086178 
[9]	train-logloss:0.076766 
[10]	train-logloss:0.069198 
[11]	train-logloss:0.063614 
[12]	train-logloss:0.059085 
[13]	train-logloss:0.055346 
[14]	train-logloss:0.052474 
[15]	train-logloss:0.049706 
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 390
Number of edges: 9849

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

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

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

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

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

[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]
> 
> proc.time()
   user  system elapsed 
318.639   7.999 333.779 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0030.005
SEG0.0030.0030.005
calcEffectSizes0.2070.0160.223
combineSCE0.8480.0180.870
computeZScore0.2330.0090.245
convertSCEToSeurat4.7240.2324.989
convertSeuratToSCE0.3770.0090.388
dedupRowNames0.0690.0050.074
detectCellOutlier5.5220.1655.719
diffAbundanceFET0.0620.0050.068
discreteColorPalette0.0070.0000.007
distinctColors0.0030.0000.003
downSampleCells0.5470.0710.621
downSampleDepth0.4280.0420.472
expData-ANY-character-method0.1440.0080.152
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.1890.0060.196
expData-set0.1570.0070.164
expData0.1510.0050.158
expDataNames-ANY-method0.1540.0060.161
expDataNames0.1370.0110.152
expDeleteDataTag0.0370.0030.040
expSetDataTag0.0260.0030.028
expTaggedData0.0320.0060.038
exportSCE0.0310.0050.036
exportSCEtoAnnData0.0640.0020.067
exportSCEtoFlatFile0.0620.0020.065
featureIndex0.0450.0040.050
generateSimulatedData0.0630.0050.069
getBiomarker0.0710.0060.078
getDEGTopTable0.8540.0860.945
getDiffAbundanceResults0.0590.0040.064
getEnrichRResult0.3250.0456.103
getFindMarkerTopTable1.6940.0551.757
getMSigDBTable0.0040.0020.007
getPathwayResultNames0.0280.0050.033
getSampleSummaryStatsTable0.2120.0070.220
getSoupX000
getTSCANResults1.2280.0601.298
getTopHVG0.9600.0190.986
importAnnData0.0010.0010.002
importBUStools0.1760.0050.184
importCellRanger0.8780.0420.939
importCellRangerV2Sample0.1700.0030.174
importCellRangerV3Sample0.3590.0160.383
importDropEst0.2130.0040.219
importExampleData15.141 1.81117.430
importGeneSetsFromCollection0.8740.1451.028
importGeneSetsFromGMT0.0760.0050.081
importGeneSetsFromList0.2040.0110.221
importGeneSetsFromMSigDB51.459 0.55652.321
importMitoGeneSet0.0610.0080.069
importOptimus0.0020.0010.001
importSEQC0.1650.0100.176
importSTARsolo0.1630.0150.179
iterateSimulations0.2120.0190.233
listSampleSummaryStatsTables0.3030.0240.328
mergeSCEColData0.4820.0620.553
mouseBrainSubsetSCE0.0440.0060.051
msigdb_table0.0010.0020.003
plotBarcodeRankDropsResults0.6840.0340.723
plotBarcodeRankScatter0.6890.0110.702
plotBatchCorrCompare13.694 0.13113.883
plotBatchVariance0.3670.0140.383
plotBcdsResults9.3910.1699.611
plotBubble0.8330.0560.894
plotClusterAbundance0.9740.0130.993
plotCxdsResults7.5650.0707.659
plotDEGHeatmap2.4100.0492.473
plotDEGRegression3.8130.0583.890
plotDEGViolin4.9650.0875.078
plotDEGVolcano1.0690.0171.092
plotDecontXResults9.1650.1009.301
plotDimRed0.3400.0250.373
plotDoubletFinderResults42.221 0.25242.653
plotEmptyDropsResults6.1310.0446.207
plotEmptyDropsScatter6.1880.0326.255
plotFindMarkerHeatmap4.4590.0384.517
plotMASTThresholdGenes1.6080.0351.654
plotPCA0.4370.0110.451
plotPathway0.6310.0120.646
plotRunPerCellQCResults2.4240.0272.466
plotSCEBarAssayData0.2480.0080.258
plotSCEBarColData0.1850.0060.192
plotSCEBatchFeatureMean0.3410.0040.347
plotSCEDensity0.2470.0060.254
plotSCEDensityAssayData0.1940.0060.202
plotSCEDensityColData0.2710.0060.278
plotSCEDimReduceColData0.6360.0150.656
plotSCEDimReduceFeatures0.3330.0080.342
plotSCEHeatmap0.5720.0110.588
plotSCEScatter0.2960.0090.307
plotSCEViolin0.3140.0080.326
plotSCEViolinAssayData0.3230.0080.333
plotSCEViolinColData0.3250.0100.338
plotScDblFinderResults32.126 0.88233.165
plotScanpyDotPlot0.0350.0040.039
plotScanpyEmbedding0.0280.0050.033
plotScanpyHVG0.0290.0030.032
plotScanpyHeatmap0.0260.0030.030
plotScanpyMarkerGenes0.0270.0030.029
plotScanpyMarkerGenesDotPlot0.0270.0040.033
plotScanpyMarkerGenesHeatmap0.0270.0040.031
plotScanpyMarkerGenesMatrixPlot0.0290.0040.034
plotScanpyMarkerGenesViolin0.0300.0050.036
plotScanpyMatrixPlot0.0300.0040.036
plotScanpyPCA0.0300.0040.034
plotScanpyPCAGeneRanking0.0280.0030.032
plotScanpyPCAVariance0.0270.0040.033
plotScanpyViolin0.0330.0060.042
plotScdsHybridResults10.046 0.12310.217
plotScrubletResults0.0270.0050.033
plotSeuratElbow0.0260.0040.029
plotSeuratHVG0.0300.0050.035
plotSeuratJackStraw0.0210.0020.024
plotSeuratReduction0.0230.0020.026
plotSoupXResults000
plotTSCANClusterDEG4.3120.0894.423
plotTSCANClusterPseudo1.3720.0271.405
plotTSCANDimReduceFeatures1.4320.0271.466
plotTSCANPseudotimeGenes1.6740.0321.716
plotTSCANPseudotimeHeatmap1.6520.0341.699
plotTSCANResults1.4300.0281.468
plotTSNE0.3710.0100.383
plotTopHVG0.6100.0150.627
plotUMAP7.6650.0787.783
readSingleCellMatrix0.0070.0010.007
reportCellQC0.1020.0080.112
reportDropletQC0.0290.0040.033
reportQCTool0.1050.0060.112
retrieveSCEIndex0.0420.0060.049
runBBKNN0.0000.0010.001
runBarcodeRankDrops0.2710.0070.280
runBcds1.9540.0672.035
runCellQC0.0940.0060.100
runClusterSummaryMetrics0.4600.0140.475
runComBatSeq0.5040.0180.526
runCxds0.3730.0080.383
runCxdsBcdsHybrid1.9670.1602.140
runDEAnalysis0.4510.0090.462
runDecontX7.8420.0577.936
runDimReduce0.3500.0080.361
runDoubletFinder37.109 0.23037.499
runDropletQC0.0330.0080.042
runEmptyDrops5.8020.0355.866
runEnrichR0.2940.0332.673
runFastMNN1.9500.0512.012
runFeatureSelection0.2370.0060.242
runFindMarker1.6490.0401.693
runGSVA0.9150.0540.971
runHarmony0.0480.0010.049
runKMeans0.2130.0100.224
runLimmaBC0.0920.0020.094
runMNNCorrect0.4560.0060.463
runModelGeneVar0.3590.0070.368
runNormalization2.6370.0402.685
runPerCellQC0.3920.0100.405
runSCANORAMA0.0000.0010.001
runSCMerge0.0040.0020.006
runScDblFinder21.444 0.50022.038
runScanpyFindClusters0.0320.0060.038
runScanpyFindHVG0.0270.0050.032
runScanpyFindMarkers0.0290.0050.034
runScanpyNormalizeData0.1170.0070.125
runScanpyPCA0.0330.0050.039
runScanpyScaleData0.0300.0060.038
runScanpyTSNE0.0300.0040.035
runScanpyUMAP0.0290.0070.037
runScranSNN0.3560.0130.372
runScrublet0.0260.0030.029
runSeuratFindClusters0.0280.0050.032
runSeuratFindHVG0.5840.0090.596
runSeuratHeatmap0.0300.0040.035
runSeuratICA0.0320.0030.035
runSeuratJackStraw0.0300.0040.034
runSeuratNormalizeData0.0290.0070.035
runSeuratPCA0.0310.0070.038
runSeuratSCTransform4.2240.0884.339
runSeuratScaleData0.0280.0040.032
runSeuratUMAP0.0300.0050.036
runSingleR0.0430.0020.046
runSoupX000
runTSCAN0.7930.0210.819
runTSCANClusterDEAnalysis0.8540.0320.891
runTSCANDEG0.8840.0280.921
runTSNE0.6990.0140.718
runUMAP7.3410.0707.444
runVAM0.3120.0060.320
runZINBWaVE0.0040.0010.006
sampleSummaryStats0.1550.0060.163
scaterCPM0.1210.0070.128
scaterPCA0.4990.0090.510
scaterlogNormCounts0.2370.0060.245
sce0.0270.0050.032
sctkListGeneSetCollections0.0870.0050.093
sctkPythonInstallConda0.0000.0000.001
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0980.0030.101
setSCTKDisplayRow0.3580.0090.369
singleCellTK0.0000.0000.001
subDiffEx0.3880.0270.417
subsetSCECols0.1000.0070.107
subsetSCERows0.3520.0250.379
summarizeSCE0.0830.0070.090
trimCounts0.2010.0110.215