Back to Multiple platform build/check report for BioC 3.22:   simplified   long
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This page was generated on 2025-10-06 12:06 -0400 (Mon, 06 Oct 2025).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 24.04.3 LTS)x86_644.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" 4853
lconwaymacOS 12.7.1 Montereyx86_644.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" 4640
kjohnson3macOS 13.7.7 Venturaarm644.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" 4585
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 2015/2341HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.19.2  (landing page)
Joshua David Campbell
Snapshot Date: 2025-10-05 13:45 -0400 (Sun, 05 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    ERROR  
lconwaymacOS 12.7.1 Monterey / x86_64  OK    OK    ERROR    OK  
kjohnson3macOS 13.7.7 Ventura / arm64  OK    OK    ERROR    OK  
taishanLinux (openEuler 24.03 LTS) / aarch64  OK    OK    OK  


CHECK results for singleCellTK on kjohnson3

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: 2.19.2
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_2.19.2.tar.gz
StartedAt: 2025-10-05 21:50:04 -0400 (Sun, 05 Oct 2025)
EndedAt: 2025-10-05 22:01:30 -0400 (Sun, 05 Oct 2025)
EllapsedTime: 685.8 seconds
RetCode: 1
Status:   ERROR  
CheckDir: singleCellTK.Rcheck
Warnings: NA

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_2.19.2.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.1 Patched (2025-09-10 r88807)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 16.0.0 (clang-1600.0.26.6)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Ventura 13.7.7
* 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  6.9Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    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
  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 ... ERROR
Running examples in ‘singleCellTK-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: runGSVA
> ### Title: Run GSVA analysis on a SingleCellExperiment object
> ### Aliases: runGSVA
> 
> ### ** Examples
> 
> data(scExample, package = "singleCellTK")
> sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
> sce <- scaterlogNormCounts(sce, assayName = "logcounts")
> gs1 <- rownames(sce)[seq(10)]
> gs2 <- rownames(sce)[seq(11,20)]
> gs <- list("geneset1" = gs1, "geneset2" = gs2)
> 
> sce <- importGeneSetsFromList(inSCE = sce,geneSetList = gs,
+                                            by = "rownames")
> sce <- runGSVA(inSCE = sce, 
+                geneSetCollectionName = "GeneSetCollection", 
+                useAssay = "logcounts")
Sun Oct  5 21:56:41 2025 ... Running GSVA
ℹ GSVA version 2.3.2
ℹ Calculating GSVA ranks
ℹ kcdf='auto' (default)
ℹ GSVA dense (classical) algorithm
ℹ Row-wise ECDF estimation with Gaussian kernels
ℹ Calculating GSVA column ranks
Error in (function (cond)  : 
  error in evaluating the argument 'x' in selecting a method for function 't': ✖ No identifiers in the gene sets could be matched to the identifiers in
  the expression data.
Calls: runGSVA ... <Anonymous> -> signal_abort -> signalCondition -> <Anonymous>
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
importGeneSetsFromMSigDB 17.899  0.075  17.979
plotDoubletFinderResults 17.488  0.085  17.595
runDoubletFinder         15.950  0.044  16.005
plotScDblFinderResults   13.413  0.250  13.860
plotBatchCorrCompare      5.870  0.043   5.968
importExampleData         5.166  0.434   5.983
getEnrichRResult          0.140  0.034 152.968
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 ERROR
Running the tests in ‘tests/testthat.R’ failed.
Last 13 lines of output:
    5. │   └─GSVA (local) .local(param, ...)
    6. │     ├─GSVA::gsvaScores(param = rankspar, verbose = verbose, BPPARAM = BPPARAM)
    7. │     └─GSVA::gsvaScores(param = rankspar, verbose = verbose, BPPARAM = BPPARAM)
    8. │       └─GSVA (local) .local(param, ...)
    9. │         └─GSVA:::.filterAndMapGeneSets(...)
   10. │           └─GSVA:::.mapGeneSetsToFeatures(geneSets, rownames(filteredDataMatrix))
   11. │             └─cli::cli_abort(c(x = msg))
   12. │               └─rlang::abort(...)
   13. │                 └─rlang:::signal_abort(cnd, .file)
   14. │                   └─base::signalCondition(cnd)
   15. └─base (local) `<fn>`(`<rlng_rrr>`)
  
  [ FAIL 2 | WARN 22 | SKIP 0 | PASS 222 ]
  Error: Test failures
  Execution halted
* 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: 2 ERRORs, 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-arm64/Resources/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 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-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)
All Done!
> 
> proc.time()
   user  system elapsed 
  0.067   0.018   0.083 

singleCellTK.Rcheck/tests/testthat.Rout.fail


R version 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-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:0.452573 
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 2 | WARN 22 | SKIP 0 | PASS 222 ]

══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-misc.R:64:3'): Testing runGSVA ─────────────────────────────────
Error in `(function (cond) 
.Internal(C_tryCatchHelper(addr, 1L, cond)))(structure(list(message = c(x = "No identifiers in the gene sets could be matched to the identifiers in the expression data."), 
    trace = structure(list(call = list(runGSVA(inSCE = sce, geneSetCollectionName = "H", 
        useAssay = "logcounts"), t(GSVA::gsva(gsvaPar)), GSVA::gsva(gsvaPar), 
        GSVA::gsva(gsvaPar), .local(param, ...), gsvaScores(param = rankspar, 
            verbose = verbose, BPPARAM = BPPARAM), gsvaScores(param = rankspar, 
            verbose = verbose, BPPARAM = BPPARAM), .local(param, 
            ...), .filterAndMapGeneSets(param = param, filteredDataMatrix = filteredDataMatrix, 
            verbose = verbose), .mapGeneSetsToFeatures(geneSets, 
            rownames(filteredDataMatrix)), cli_abort(c(x = msg)), 
        rlang::abort(message, ..., call = call, use_cli_format = TRUE, 
            .frame = .frame)), parent = c(0L, 1L, 1L, 1L, 4L, 
    5L, 5L, 7L, 8L, 9L, 10L, 11L), visible = c(TRUE, TRUE, TRUE, 
    TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE), 
        namespace = c("singleCellTK", "base", "GSVA", "GSVA", 
        "GSVA", "GSVA", "GSVA", "GSVA", "GSVA", "GSVA", "cli", 
        "rlang"), scope = c("::", "::", "::", "::", "local", 
        "::", "::", "local", ":::", ":::", "::", "::"), error_frame = c(FALSE, 
        FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
        TRUE, FALSE, FALSE)), row.names = c(NA, -12L), version = 2L, class = c("rlang_trace", 
    "rlib_trace", "tbl", "data.frame")), parent = NULL, rlang = list(
        inherit = TRUE), call = .mapGeneSetsToFeatures(geneSets, 
        rownames(filteredDataMatrix)), use_cli_format = TRUE), class = c("rlang_error", 
"error", "condition")))`: error in evaluating the argument 'x' in selecting a method for function 't': ✖ No identifiers in the gene sets could be matched to the identifiers in
  the expression data.
Backtrace:
     ▆
  1. ├─singleCellTK::runGSVA(...) at test-misc.R:64:3
  2. │ ├─base::t(GSVA::gsva(gsvaPar))
  3. │ ├─GSVA::gsva(gsvaPar)
  4. │ └─GSVA::gsva(gsvaPar)
  5. │   └─GSVA (local) .local(param, ...)
  6. │     ├─GSVA::gsvaScores(param = rankspar, verbose = verbose, BPPARAM = BPPARAM)
  7. │     └─GSVA::gsvaScores(param = rankspar, verbose = verbose, BPPARAM = BPPARAM)
  8. │       └─GSVA (local) .local(param, ...)
  9. │         └─GSVA:::.filterAndMapGeneSets(...)
 10. │           └─GSVA:::.mapGeneSetsToFeatures(geneSets, rownames(filteredDataMatrix))
 11. │             └─cli::cli_abort(c(x = msg))
 12. │               └─rlang::abort(...)
 13. │                 └─rlang:::signal_abort(cnd, .file)
 14. │                   └─base::signalCondition(cnd)
 15. └─base (local) `<fn>`(`<rlng_rrr>`)
── Error ('test-pathway.R:36:5'): Testing GSVA ─────────────────────────────────
Error in `(function (cond) 
.Internal(C_tryCatchHelper(addr, 1L, cond)))(structure(list(message = c(x = "No identifiers in the gene sets could be matched to the identifiers in the expression data."), 
    trace = structure(list(call = list(runGSVA(sce, geneSetCollectionName = "GeneSetCollection", 
        useAssay = "logcounts"), t(GSVA::gsva(gsvaPar)), GSVA::gsva(gsvaPar), 
        GSVA::gsva(gsvaPar), .local(param, ...), gsvaScores(param = rankspar, 
            verbose = verbose, BPPARAM = BPPARAM), gsvaScores(param = rankspar, 
            verbose = verbose, BPPARAM = BPPARAM), .local(param, 
            ...), .filterAndMapGeneSets(param = param, filteredDataMatrix = filteredDataMatrix, 
            verbose = verbose), .mapGeneSetsToFeatures(geneSets, 
            rownames(filteredDataMatrix)), cli_abort(c(x = msg)), 
        rlang::abort(message, ..., call = call, use_cli_format = TRUE, 
            .frame = .frame)), parent = c(0L, 1L, 1L, 1L, 4L, 
    5L, 5L, 7L, 8L, 9L, 10L, 11L), visible = c(TRUE, TRUE, TRUE, 
    TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE), 
        namespace = c("singleCellTK", "base", "GSVA", "GSVA", 
        "GSVA", "GSVA", "GSVA", "GSVA", "GSVA", "GSVA", "cli", 
        "rlang"), scope = c("::", "::", "::", "::", "local", 
        "::", "::", "local", ":::", ":::", "::", "::"), error_frame = c(FALSE, 
        FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
        TRUE, FALSE, FALSE)), row.names = c(NA, -12L), version = 2L, class = c("rlang_trace", 
    "rlib_trace", "tbl", "data.frame")), parent = NULL, rlang = list(
        inherit = TRUE), call = .mapGeneSetsToFeatures(geneSets, 
        rownames(filteredDataMatrix)), use_cli_format = TRUE), class = c("rlang_error", 
"error", "condition")))`: error in evaluating the argument 'x' in selecting a method for function 't': ✖ No identifiers in the gene sets could be matched to the identifiers in
  the expression data.
Backtrace:
     ▆
  1. ├─singleCellTK::runGSVA(...) at test-pathway.R:36:5
  2. │ ├─base::t(GSVA::gsva(gsvaPar))
  3. │ ├─GSVA::gsva(gsvaPar)
  4. │ └─GSVA::gsva(gsvaPar)
  5. │   └─GSVA (local) .local(param, ...)
  6. │     ├─GSVA::gsvaScores(param = rankspar, verbose = verbose, BPPARAM = BPPARAM)
  7. │     └─GSVA::gsvaScores(param = rankspar, verbose = verbose, BPPARAM = BPPARAM)
  8. │       └─GSVA (local) .local(param, ...)
  9. │         └─GSVA:::.filterAndMapGeneSets(...)
 10. │           └─GSVA:::.mapGeneSetsToFeatures(geneSets, rownames(filteredDataMatrix))
 11. │             └─cli::cli_abort(c(x = msg))
 12. │               └─rlang::abort(...)
 13. │                 └─rlang:::signal_abort(cnd, .file)
 14. │                   └─base::signalCondition(cnd)
 15. └─base (local) `<fn>`(`<rlng_rrr>`)

[ FAIL 2 | WARN 22 | SKIP 0 | PASS 222 ]
Error: Test failures
Execution halted

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0010.0020.002
SEG0.0010.0010.003
calcEffectSizes0.0720.0040.080
combineSCE0.2340.0050.240
computeZScore0.0910.0020.094
convertSCEToSeurat1.6540.0671.725
convertSeuratToSCE0.1190.0030.121
dedupRowNames0.0210.0010.022
detectCellOutlier2.6580.0302.711
diffAbundanceFET0.0250.0010.026
discreteColorPalette0.0020.0000.002
distinctColors0.0000.0000.001
downSampleCells0.2050.0190.224
downSampleDepth0.1530.0100.162
expData-ANY-character-method0.0410.0020.042
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.0540.0010.056
expData-set0.0490.0010.050
expData0.0410.0020.043
expDataNames-ANY-method0.0400.0030.042
expDataNames0.0400.0040.043
expDeleteDataTag0.0160.0020.018
expSetDataTag0.0130.0010.013
expTaggedData0.0120.0010.013
exportSCE0.0110.0020.013
exportSCEtoAnnData0.0420.0010.043
exportSCEtoFlatFile0.0410.0010.042
featureIndex0.0150.0020.018
generateSimulatedData0.0220.0030.025
getBiomarker0.0240.0020.027
getDEGTopTable0.2240.0140.238
getDiffAbundanceResults0.0210.0020.023
getEnrichRResult 0.140 0.034152.968
getFindMarkerTopTable0.4700.0180.488
getMSigDBTable0.0010.0020.003
getPathwayResultNames0.0120.0020.015
getSampleSummaryStatsTable0.0610.0020.064
getSoupX000
getTSCANResults0.3590.0140.376
getTopHVG0.2900.0040.294
importAnnData0.0010.0000.001
importBUStools0.0420.0010.044
importCellRanger0.220.010.23
importCellRangerV2Sample0.0410.0010.045
importCellRangerV3Sample0.0790.0040.084
importDropEst0.0630.0000.064
importExampleData5.1660.4345.983
importGeneSetsFromCollection0.8130.0290.843
importGeneSetsFromGMT0.0270.0030.030
importGeneSetsFromList0.0440.0020.046
importGeneSetsFromMSigDB17.899 0.07517.979
importMitoGeneSet0.0210.0040.025
importOptimus0.0010.0000.001
importSEQC0.0480.0040.054
importSTARsolo0.0440.0050.050
iterateSimulations0.0680.0070.076
listSampleSummaryStatsTables0.1250.0070.132
mergeSCEColData0.1070.0060.115
mouseBrainSubsetSCE0.0180.0020.021
msigdb_table0.0000.0010.002
plotBarcodeRankDropsResults0.2910.0100.302
plotBarcodeRankScatter0.2950.0060.301
plotBatchCorrCompare5.8700.0435.968
plotBatchVariance0.1420.0020.144
plotBcdsResults3.7070.0573.796
plotBubble0.2550.0050.260
plotClusterAbundance0.4490.0030.453
plotCxdsResults3.2520.0383.306
plotDEGHeatmap0.7110.0120.723
plotDEGRegression1.3950.0181.413
plotDEGViolin2.4510.0572.509
plotDEGVolcano0.3340.0050.339
plotDecontXResults3.8150.0263.848
plotDimRed0.1180.0010.120
plotDoubletFinderResults17.488 0.08517.595
plotEmptyDropsResults2.1470.0072.154
plotEmptyDropsScatter2.0970.0042.101
plotFindMarkerHeatmap1.2350.0091.251
plotMASTThresholdGenes0.4410.0100.451
plotPCA0.1230.0030.126
plotPathway0.2050.0040.208
plotRunPerCellQCResults1.0150.0111.073
plotSCEBarAssayData0.1340.0040.150
plotSCEBarColData0.0780.0040.506
plotSCEBatchFeatureMean0.1200.0030.125
plotSCEDensity0.1100.0040.123
plotSCEDensityAssayData0.1170.0030.133
plotSCEDensityColData0.1010.0030.114
plotSCEDimReduceColData0.2630.0060.294
plotSCEDimReduceFeatures0.1270.0040.150
plotSCEHeatmap0.1360.0030.151
plotSCEScatter0.1430.0040.160
plotSCEViolin0.1220.0050.140
plotSCEViolinAssayData0.1330.0050.154
plotSCEViolinColData0.2060.0100.230
plotScDblFinderResults13.413 0.25013.860
plotScanpyDotPlot0.0110.0020.013
plotScanpyEmbedding0.0110.0010.013
plotScanpyHVG0.0100.0010.012
plotScanpyHeatmap0.0100.0010.011
plotScanpyMarkerGenes0.0110.0010.012
plotScanpyMarkerGenesDotPlot0.0110.0010.012
plotScanpyMarkerGenesHeatmap0.0110.0010.012
plotScanpyMarkerGenesMatrixPlot0.0110.0010.012
plotScanpyMarkerGenesViolin0.0110.0010.012
plotScanpyMatrixPlot0.0100.0010.012
plotScanpyPCA0.0100.0010.012
plotScanpyPCAGeneRanking0.0100.0010.012
plotScanpyPCAVariance0.0100.0000.012
plotScanpyViolin0.0110.0010.012
plotScdsHybridResults4.2560.0814.361
plotScrubletResults0.0120.0020.014
plotSeuratElbow0.0120.0000.012
plotSeuratHVG0.0110.0010.012
plotSeuratJackStraw0.0110.0000.012
plotSeuratReduction0.0110.0000.011
plotSoupXResults000
plotTSCANClusterDEG1.5620.0231.587
plotTSCANClusterPseudo0.4190.0080.426
plotTSCANDimReduceFeatures0.4560.0130.469
plotTSCANPseudotimeGenes0.5270.0090.544
plotTSCANPseudotimeHeatmap0.4100.0070.418
plotTSCANResults0.4120.0070.421
plotTSNE0.1260.0040.133
plotTopHVG0.2090.0040.214
plotUMAP3.4260.0343.563
readSingleCellMatrix0.0020.0000.002
reportCellQC0.0300.0020.033
reportDropletQC0.0110.0010.013
reportQCTool0.0280.0010.029
retrieveSCEIndex0.0140.0010.015
runBBKNN000
runBarcodeRankDrops0.0760.0020.079
runBcds0.5920.0250.618
runCellQC0.0290.0010.030
runClusterSummaryMetrics0.1140.0110.126
runComBatSeq0.1560.0110.167
runCxds0.1160.0090.133
runCxdsBcdsHybrid0.6190.0230.642
runDEAnalysis0.1700.0120.183
runDecontX3.4050.0223.438
runDimReduce0.0930.0040.096
runDoubletFinder15.950 0.04416.005
runDropletQC0.0120.0020.014
runEmptyDrops2.0070.0032.011
runEnrichR0.1280.0182.338
runFastMNN0.5570.0500.607
runFeatureSelection0.0800.0020.081
runFindMarker0.4400.0100.451