| Back to Multiple platform build/check report for BioC 3.22: simplified long |
|
This page was generated on 2025-10-07 12:08 -0400 (Tue, 07 Oct 2025).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) | x86_64 | 4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" | 4853 |
| lconway | macOS 12.7.1 Monterey | x86_64 | 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" | 4640 |
| kjohnson3 | macOS 13.7.7 Ventura | arm64 | 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" | 4585 |
| taishan | Linux (openEuler 24.03 LTS) | aarch64 | 4.5.0 (2025-04-11) -- "How About a Twenty-Six" | 4584 |
| 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 2015/2341 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| singleCellTK 2.19.2 (landing page) Joshua David Campbell
| nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) / x86_64 | OK | OK | ERROR | |||||||||
| lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | ERROR | OK | |||||||||
| kjohnson3 | macOS 13.7.7 Ventura / arm64 | OK | OK | ERROR | OK | |||||||||
| taishan | Linux (openEuler 24.03 LTS) / aarch64 | OK | OK | ERROR | ||||||||||
|
To the developers/maintainers of the singleCellTK package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/singleCellTK.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. - See Martin Grigorov's blog post for how to debug Linux ARM64 related issues on a x86_64 host. |
| Package: singleCellTK |
| Version: 2.19.2 |
| Command: /home/biocbuild/R/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings singleCellTK_2.19.2.tar.gz |
| StartedAt: 2025-10-07 12:13:44 -0000 (Tue, 07 Oct 2025) |
| EndedAt: 2025-10-07 12:34:24 -0000 (Tue, 07 Oct 2025) |
| EllapsedTime: 1239.5 seconds |
| RetCode: 1 |
| Status: ERROR |
| CheckDir: singleCellTK.Rcheck |
| Warnings: NA |
##############################################################################
##############################################################################
###
### Running command:
###
### /home/biocbuild/R/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings singleCellTK_2.19.2.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.0 (2025-04-11)
* using platform: aarch64-unknown-linux-gnu
* R was compiled by
aarch64-unknown-linux-gnu-gcc (GCC) 14.2.0
GNU Fortran (GCC) 14.2.0
* running under: openEuler 24.03 (LTS)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.19.2’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 80 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable. Move as many as possible to Suggests and
use conditionally.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... INFO
installed size is 7.0Mb
sub-directories of 1Mb or more:
R 1.0Mb
extdata 1.6Mb
shiny 3.0Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
dedupRowNames.Rd: SingleCellExperiment-class
detectCellOutlier.Rd: colData
diffAbundanceFET.Rd: colData
downSampleCells.Rd: SingleCellExperiment-class
downSampleDepth.Rd: SingleCellExperiment-class
featureIndex.Rd: SummarizedExperiment-class,
SingleCellExperiment-class
getBiomarker.Rd: SingleCellExperiment-class
getDEGTopTable.Rd: SingleCellExperiment-class
getEnrichRResult.Rd: SingleCellExperiment-class
getFindMarkerTopTable.Rd: SingleCellExperiment-class
getGenesetNamesFromCollection.Rd: SingleCellExperiment-class
getPathwayResultNames.Rd: SingleCellExperiment-class
getSampleSummaryStatsTable.Rd: SingleCellExperiment-class, assay,
colData
getSoupX.Rd: SingleCellExperiment-class
getTSCANResults.Rd: SingleCellExperiment-class
getTopHVG.Rd: SingleCellExperiment-class
importAlevin.Rd: DelayedArray, readMM
importAnnData.Rd: DelayedArray, readMM
importBUStools.Rd: readMM
importCellRanger.Rd: readMM, DelayedArray
importCellRangerV2Sample.Rd: readMM, DelayedArray
importCellRangerV3Sample.Rd: readMM, DelayedArray
importDropEst.Rd: DelayedArray, readMM
importExampleData.Rd: scRNAseq, Matrix, DelayedArray,
ReprocessedFluidigmData, ReprocessedAllenData, NestorowaHSCData
importFromFiles.Rd: readMM, DelayedArray, SingleCellExperiment-class
importGeneSetsFromCollection.Rd: GeneSetCollection-class,
SingleCellExperiment-class, GeneSetCollection, GSEABase, metadata
importGeneSetsFromGMT.Rd: GeneSetCollection-class,
SingleCellExperiment-class, getGmt, GSEABase, metadata
importGeneSetsFromList.Rd: GeneSetCollection-class,
SingleCellExperiment-class, GSEABase, metadata
importGeneSetsFromMSigDB.Rd: SingleCellExperiment-class, msigdbr,
GeneSetCollection-class, GSEABase, metadata
importMitoGeneSet.Rd: SingleCellExperiment-class,
GeneSetCollection-class, GSEABase, metadata
importMultipleSources.Rd: DelayedArray
importOptimus.Rd: readMM, DelayedArray
importSEQC.Rd: readMM, DelayedArray
importSTARsolo.Rd: readMM, DelayedArray
iterateSimulations.Rd: SingleCellExperiment-class
listSampleSummaryStatsTables.Rd: SingleCellExperiment-class, metadata
plotBarcodeRankDropsResults.Rd: SingleCellExperiment-class
plotBarcodeRankScatter.Rd: SingleCellExperiment-class
plotBatchCorrCompare.Rd: SingleCellExperiment-class
plotBatchVariance.Rd: SingleCellExperiment-class
plotBcdsResults.Rd: SingleCellExperiment-class
plotClusterAbundance.Rd: colData
plotCxdsResults.Rd: SingleCellExperiment-class
plotDEGHeatmap.Rd: SingleCellExperiment-class
plotDEGRegression.Rd: SingleCellExperiment-class
plotDEGViolin.Rd: SingleCellExperiment-class
plotDEGVolcano.Rd: SingleCellExperiment-class
plotDecontXResults.Rd: SingleCellExperiment-class
plotDoubletFinderResults.Rd: SingleCellExperiment-class
plotEmptyDropsResults.Rd: SingleCellExperiment-class
plotEmptyDropsScatter.Rd: SingleCellExperiment-class
plotEnrichR.Rd: SingleCellExperiment-class
plotFindMarkerHeatmap.Rd: SingleCellExperiment-class
plotPCA.Rd: SingleCellExperiment-class
plotPathway.Rd: SingleCellExperiment-class
plotRunPerCellQCResults.Rd: SingleCellExperiment-class
plotSCEBarAssayData.Rd: SingleCellExperiment-class
plotSCEBarColData.Rd: SingleCellExperiment-class
plotSCEBatchFeatureMean.Rd: SingleCellExperiment-class
plotSCEDensity.Rd: SingleCellExperiment-class
plotSCEDensityAssayData.Rd: SingleCellExperiment-class
plotSCEDensityColData.Rd: SingleCellExperiment-class
plotSCEDimReduceColData.Rd: SingleCellExperiment-class
plotSCEDimReduceFeatures.Rd: SingleCellExperiment-class
plotSCEHeatmap.Rd: SingleCellExperiment-class
plotSCEScatter.Rd: SingleCellExperiment-class
plotSCEViolin.Rd: SingleCellExperiment-class
plotSCEViolinAssayData.Rd: SingleCellExperiment-class
plotSCEViolinColData.Rd: SingleCellExperiment-class
plotScDblFinderResults.Rd: SingleCellExperiment-class
plotScdsHybridResults.Rd: SingleCellExperiment-class
plotScrubletResults.Rd: SingleCellExperiment-class
plotSoupXResults.Rd: SingleCellExperiment-class
plotTSCANClusterDEG.Rd: SingleCellExperiment-class
plotTSCANClusterPseudo.Rd: SingleCellExperiment-class
plotTSCANDimReduceFeatures.Rd: SingleCellExperiment-class
plotTSCANPseudotimeGenes.Rd: SingleCellExperiment-class
plotTSCANPseudotimeHeatmap.Rd: SingleCellExperiment-class
plotTSCANResults.Rd: SingleCellExperiment-class
plotTSNE.Rd: SingleCellExperiment-class
plotUMAP.Rd: SingleCellExperiment-class
readSingleCellMatrix.Rd: DelayedArray
reportCellQC.Rd: SingleCellExperiment-class
reportClusterAbundance.Rd: colData
reportDiffAbundanceFET.Rd: colData
retrieveSCEIndex.Rd: SingleCellExperiment-class
runBBKNN.Rd: SingleCellExperiment-class
runBarcodeRankDrops.Rd: SingleCellExperiment-class, colData
runBcds.Rd: SingleCellExperiment-class, colData
runCellQC.Rd: colData
runComBatSeq.Rd: SingleCellExperiment-class
runCxds.Rd: SingleCellExperiment-class, colData
runCxdsBcdsHybrid.Rd: colData
runDEAnalysis.Rd: SingleCellExperiment-class
runDecontX.Rd: colData
runDimReduce.Rd: SingleCellExperiment-class
runDoubletFinder.Rd: SingleCellExperiment-class
runDropletQC.Rd: colData
runEmptyDrops.Rd: SingleCellExperiment-class, colData
runEnrichR.Rd: SingleCellExperiment-class
runFastMNN.Rd: SingleCellExperiment-class, BiocParallelParam-class
runFeatureSelection.Rd: SingleCellExperiment-class
runFindMarker.Rd: SingleCellExperiment-class
runGSVA.Rd: SingleCellExperiment-class
runHarmony.Rd: SingleCellExperiment-class
runKMeans.Rd: SingleCellExperiment-class, colData
runLimmaBC.Rd: SingleCellExperiment-class, assay
runMNNCorrect.Rd: SingleCellExperiment-class, assay,
BiocParallelParam-class
runModelGeneVar.Rd: SingleCellExperiment-class
runPerCellQC.Rd: SingleCellExperiment-class, BiocParallelParam,
colData
runSCANORAMA.Rd: SingleCellExperiment-class, assay
runSCMerge.Rd: SingleCellExperiment-class, colData, assay,
BiocParallelParam-class
runScDblFinder.Rd: SingleCellExperiment-class, colData
runScranSNN.Rd: SingleCellExperiment-class, reducedDim, assay,
altExp, colData, igraph
runScrublet.Rd: SingleCellExperiment-class, colData
runSingleR.Rd: SingleCellExperiment-class
runSoupX.Rd: SingleCellExperiment-class
runTSCAN.Rd: SingleCellExperiment-class
runTSCANClusterDEAnalysis.Rd: SingleCellExperiment-class
runTSCANDEG.Rd: SingleCellExperiment-class
runTSNE.Rd: SingleCellExperiment-class
runUMAP.Rd: SingleCellExperiment-class, BiocParallelParam-class
runVAM.Rd: SingleCellExperiment-class
runZINBWaVE.Rd: SingleCellExperiment-class, colData,
BiocParallelParam-class
sampleSummaryStats.Rd: SingleCellExperiment-class, assay, colData
scaterPCA.Rd: SingleCellExperiment-class, BiocParallelParam-class
scaterlogNormCounts.Rd: logNormCounts
sctkListGeneSetCollections.Rd: GeneSetCollection-class
sctkPythonInstallConda.Rd: conda_install, reticulate, conda_create
sctkPythonInstallVirtualEnv.Rd: virtualenv_install, reticulate,
virtualenv_create
selectSCTKConda.Rd: reticulate
selectSCTKVirtualEnvironment.Rd: reticulate
setRowNames.Rd: SingleCellExperiment-class
setSCTKDisplayRow.Rd: SingleCellExperiment-class
singleCellTK.Rd: SingleCellExperiment-class
subsetSCECols.Rd: SingleCellExperiment-class
subsetSCERows.Rd: SingleCellExperiment-class, altExp
summarizeSCE.Rd: SingleCellExperiment-class
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... 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")
Tue Oct 7 12:27:01 2025 ... Running GSVA
ℹ GSVA version 2.3.3
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 51.512 0.789 52.402
plotDoubletFinderResults 45.465 2.136 47.854
runDoubletFinder 36.649 0.289 37.042
plotScDblFinderResults 34.831 0.619 35.802
importExampleData 15.789 1.509 23.006
plotBatchCorrCompare 15.004 0.180 15.545
plotScdsHybridResults 12.500 0.117 12.673
plotBcdsResults 11.264 0.379 11.025
plotDecontXResults 10.698 0.319 11.138
plotCxdsResults 8.749 0.669 9.563
plotDEGViolin 8.656 0.271 8.954
plotUMAP 7.943 0.151 8.127
runDecontX 7.765 0.041 7.825
plotTSCANClusterDEG 7.230 0.044 7.298
plotDEGRegression 6.881 0.259 7.314
detectCellOutlier 6.237 0.020 6.279
plotEmptyDropsResults 5.889 0.093 6.001
plotFindMarkerHeatmap 5.668 0.180 5.861
plotEmptyDropsScatter 5.710 0.072 5.790
convertSCEToSeurat 5.632 0.092 5.752
runEmptyDrops 5.247 0.008 5.264
getEnrichRResult 0.441 0.212 9.673
runEnrichR 0.391 0.016 27.043
* 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
‘/home/biocbuild/bbs-3.22-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.
singleCellTK.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/R/R/bin/R CMD INSTALL singleCellTK ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/R/R-4.5.0/site-library’ * installing *source* package ‘singleCellTK’ ... ** this is package ‘singleCellTK’ version ‘2.19.2’ ** using staged installation ** R ** data ** exec ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (singleCellTK)
singleCellTK.Rcheck/tests/spelling.Rout
R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> if (requireNamespace('spelling', quietly = TRUE))
+ spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
All Done!
>
> proc.time()
user system elapsed
0.184 0.034 0.204
singleCellTK.Rcheck/tests/testthat.Rout
R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(testthat)
> library(singleCellTK)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats
Attaching package: 'MatrixGenerics'
The following objects are masked from 'package:matrixStats':
colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
colWeightedMeans, colWeightedMedians, colWeightedSds,
colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
rowWeightedSds, rowWeightedVars
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: generics
Attaching package: 'generics'
The following objects are masked from 'package:base':
as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
setequal, union
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
as.data.frame, basename, cbind, colnames, dirname, do.call,
duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
unsplit, which.max, which.min
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:utils':
findMatches
The following objects are masked from 'package:base':
I, expand.grid, unname
Loading required package: IRanges
Loading required package: Seqinfo
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: 'Biobase'
The following object is masked from 'package:MatrixGenerics':
rowMedians
The following objects are masked from 'package:matrixStats':
anyMissing, rowMedians
Loading required package: SingleCellExperiment
Loading required package: DelayedArray
Loading required package: Matrix
Attaching package: 'Matrix'
The following object is masked from 'package:S4Vectors':
expand
Loading required package: S4Arrays
Loading required package: abind
Attaching package: 'S4Arrays'
The following object is masked from 'package:abind':
abind
The following object is masked from 'package:base':
rowsum
Loading required package: SparseArray
Attaching package: 'DelayedArray'
The following objects are masked from 'package:base':
apply, scale, sweep
Attaching package: 'singleCellTK'
The following object is masked from 'package:BiocGenerics':
plotPCA
>
> test_check("singleCellTK")
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 0 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 1 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Uploading data to Enrichr... Done.
Querying HDSigDB_Human_2021... Done.
Parsing results... Done.
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
[1] train-logloss:0.452573
Will train until train_logloss hasn't improved in 2 rounds.
[2] train-logloss:0.320290
[3] train-logloss:0.237363
[4] train-logloss:0.182378
[5] train-logloss:0.144113
[6] train-logloss:0.117560
[7] train-logloss:0.098812
[8] train-logloss:0.084977
[9] train-logloss:0.075059
[10] train-logloss:0.067480
[11] train-logloss:0.061855
[12] train-logloss:0.057358
[13] train-logloss:0.053969
[14] train-logloss:0.050909
[15] train-logloss:0.047615
[16] train-logloss:0.045564
[17] train-logloss:0.043868
[1] train-logloss:0.453064
Will train until train_logloss hasn't improved in 2 rounds.
[2] train-logloss:0.321072
[3] train-logloss:0.238210
[4] train-logloss:0.183469
[5] train-logloss:0.145239
[6] train-logloss:0.118860
[7] train-logloss:0.100304
[8] train-logloss:0.086606
[9] train-logloss:0.076012
[10] train-logloss:0.068021
[11] train-logloss:0.062325
[12] train-logloss:0.057942
[13] train-logloss:0.054289
[14] train-logloss:0.051302
[15] train-logloss:0.048796
[1] train-logloss:0.453064
Will train until train_logloss hasn't improved in 2 rounds.
[2] train-logloss:0.321072
[3] train-logloss:0.238210
[4] train-logloss:0.183469
[5] train-logloss:0.145239
[6] train-logloss:0.118860
[7] train-logloss:0.100304
[8] train-logloss:0.086606
[9] train-logloss:0.076012
[10] train-logloss:0.068021
[11] train-logloss:0.062325
[12] train-logloss:0.057942
[13] train-logloss:0.054289
[14] train-logloss:0.051302
[15] train-logloss:0.048796
[16] train-logloss:0.046452
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 390
Number of edges: 9849
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]
[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]
>
> proc.time()
user system elapsed
385.049 7.799 427.707
singleCellTK.Rcheck/tests/testthat.Rout.fail
R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(testthat)
> library(singleCellTK)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats
Attaching package: 'MatrixGenerics'
The following objects are masked from 'package:matrixStats':
colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
colWeightedMeans, colWeightedMedians, colWeightedSds,
colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
rowWeightedSds, rowWeightedVars
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: generics
Attaching package: 'generics'
The following objects are masked from 'package:base':
as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
setequal, union
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
as.data.frame, basename, cbind, colnames, dirname, do.call,
duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
unsplit, which.max, which.min
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:utils':
findMatches
The following objects are masked from 'package:base':
I, expand.grid, unname
Loading required package: IRanges
Loading required package: Seqinfo
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: 'Biobase'
The following object is masked from 'package:MatrixGenerics':
rowMedians
The following objects are masked from 'package:matrixStats':
anyMissing, rowMedians
Loading required package: SingleCellExperiment
Loading required package: DelayedArray
Loading required package: Matrix
Attaching package: 'Matrix'
The following object is masked from 'package:S4Vectors':
expand
Loading required package: S4Arrays
Loading required package: abind
Attaching package: 'S4Arrays'
The following object is masked from 'package:abind':
abind
The following object is masked from 'package:base':
rowsum
Loading required package: SparseArray
Attaching package: 'DelayedArray'
The following objects are masked from 'package:base':
apply, scale, sweep
Attaching package: 'singleCellTK'
The following object is masked from 'package:BiocGenerics':
plotPCA
>
> test_check("singleCellTK")
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 0 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 1 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Uploading data to Enrichr... Done.
Querying HDSigDB_Human_2021... Done.
Parsing results... Done.
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
[1] train-logloss:0.452573
Will train until train_logloss hasn't improved in 2 rounds.
[2] train-logloss:0.320290
[3] train-logloss:0.237363
[4] train-logloss:0.182378
[5] train-logloss:0.144113
[6] train-logloss:0.117560
[7] train-logloss:0.098812
[8] train-logloss:0.084977
[9] train-logloss:0.075059
[10] train-logloss:0.067480
[11] train-logloss:0.061855
[12] train-logloss:0.057358
[13] train-logloss:0.053969
[14] train-logloss:0.050909
[15] train-logloss:0.047615
[16] train-logloss:0.045564
[17] train-logloss:0.043868
[1] train-logloss:0.453064
Will train until train_logloss hasn't improved in 2 rounds.
[2] train-logloss:0.321072
[3] train-logloss:0.238210
[4] train-logloss:0.183469
[5] train-logloss:0.145239
[6] train-logloss:0.118860
[7] train-logloss:0.100304
[8] train-logloss:0.086606
[9] train-logloss:0.076012
[10] train-logloss:0.068021
[11] train-logloss:0.062325
[12] train-logloss:0.057942
[13] train-logloss:0.054289
[14] train-logloss:0.051302
[15] train-logloss:0.048796
[1] train-logloss:0.453064
Will train until train_logloss hasn't improved in 2 rounds.
[2] train-logloss:0.321072
[3] train-logloss:0.238210
[4] train-logloss:0.183469
[5] train-logloss:0.145239
[6] train-logloss:0.118860
[7] train-logloss:0.100304
[8] train-logloss:0.086606
[9] train-logloss:0.076012
[10] train-logloss:0.068021
[11] train-logloss:0.062325
[12] train-logloss:0.057942
[13] train-logloss:0.054289
[14] train-logloss:0.051302
[15] train-logloss:0.048796
[16] train-logloss:0.046452
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 390
Number of edges: 9849
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 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
singleCellTK.Rcheck/singleCellTK-Ex.timings
| name | user | system | elapsed | |
| MitoGenes | 0.003 | 0.000 | 0.003 | |
| SEG | 0.002 | 0.000 | 0.003 | |
| calcEffectSizes | 0.283 | 0.000 | 0.284 | |
| combineSCE | 1.027 | 0.008 | 1.039 | |
| computeZScore | 0.267 | 0.000 | 0.268 | |
| convertSCEToSeurat | 5.632 | 0.092 | 5.752 | |
| convertSeuratToSCE | 0.476 | 0.004 | 0.481 | |
| dedupRowNames | 0.080 | 0.000 | 0.082 | |
| detectCellOutlier | 6.237 | 0.020 | 6.279 | |
| diffAbundanceFET | 0.063 | 0.000 | 0.064 | |
| discreteColorPalette | 0.008 | 0.000 | 0.008 | |
| distinctColors | 0.003 | 0.000 | 0.003 | |
| downSampleCells | 0.713 | 0.067 | 0.783 | |
| downSampleDepth | 0.587 | 0.005 | 0.592 | |
| expData-ANY-character-method | 0.168 | 0.000 | 0.168 | |
| expData-set-ANY-character-CharacterOrNullOrMissing-logical-method | 0.214 | 0.003 | 0.218 | |
| expData-set | 0.204 | 0.000 | 0.204 | |
| expData | 0.166 | 0.004 | 0.170 | |
| expDataNames-ANY-method | 0.156 | 0.000 | 0.156 | |
| expDataNames | 0.153 | 0.003 | 0.158 | |
| expDeleteDataTag | 0.039 | 0.000 | 0.039 | |
| expSetDataTag | 0.024 | 0.004 | 0.028 | |
| expTaggedData | 0.028 | 0.000 | 0.028 | |
| exportSCE | 0.025 | 0.000 | 0.026 | |
| exportSCEtoAnnData | 0.078 | 0.000 | 0.079 | |
| exportSCEtoFlatFile | 0.074 | 0.004 | 0.079 | |
| featureIndex | 0.039 | 0.004 | 0.043 | |
| generateSimulatedData | 0.061 | 0.004 | 0.065 | |
| getBiomarker | 0.070 | 0.004 | 0.074 | |
| getDEGTopTable | 0.983 | 0.020 | 1.010 | |
| getDiffAbundanceResults | 0.057 | 0.000 | 0.058 | |
| getEnrichRResult | 0.441 | 0.212 | 9.673 | |
| getFindMarkerTopTable | 2.123 | 0.767 | 2.899 | |
| getMSigDBTable | 0.004 | 0.000 | 0.004 | |
| getPathwayResultNames | 0.027 | 0.000 | 0.028 | |
| getSampleSummaryStatsTable | 0.242 | 0.020 | 0.264 | |
| getSoupX | 0.000 | 0.000 | 0.001 | |
| getTSCANResults | 1.447 | 0.184 | 1.637 | |
| getTopHVG | 1.131 | 0.107 | 1.242 | |
| importAnnData | 0.002 | 0.000 | 0.002 | |
| importBUStools | 0.189 | 0.032 | 0.224 | |
| importCellRanger | 1.073 | 0.059 | 1.141 | |
| importCellRangerV2Sample | 0.226 | 0.001 | 0.228 | |
| importCellRangerV3Sample | 0.388 | 0.027 | 0.416 | |
| importDropEst | 0.277 | 0.016 | 0.295 | |
| importExampleData | 15.789 | 1.509 | 23.006 | |
| importGeneSetsFromCollection | 2.140 | 0.092 | 2.237 | |
| importGeneSetsFromGMT | 0.089 | 0.004 | 0.093 | |
| importGeneSetsFromList | 0.196 | 0.000 | 0.196 | |
| importGeneSetsFromMSigDB | 51.512 | 0.789 | 52.402 | |
| importMitoGeneSet | 0.074 | 0.000 | 0.075 | |
| importOptimus | 0.002 | 0.000 | 0.002 | |
| importSEQC | 0.211 | 0.012 | 0.226 | |
| importSTARsolo | 0.196 | 0.028 | 0.226 | |
| iterateSimulations | 0.254 | 0.016 | 0.271 | |
| listSampleSummaryStatsTables | 0.428 | 0.020 | 0.451 | |
| mergeSCEColData | 0.504 | 0.016 | 0.522 | |
| mouseBrainSubsetSCE | 0.041 | 0.000 | 0.040 | |
| msigdb_table | 0.000 | 0.001 | 0.002 | |
| plotBarcodeRankDropsResults | 1.273 | 0.025 | 1.302 | |
| plotBarcodeRankScatter | 1.283 | 0.000 | 1.287 | |
| plotBatchCorrCompare | 15.004 | 0.180 | 15.545 | |
| plotBatchVariance | 0.680 | 0.004 | 0.685 | |
| plotBcdsResults | 11.264 | 0.379 | 11.025 | |
| plotBubble | 1.236 | 0.084 | 1.324 | |
| plotClusterAbundance | 2.295 | 0.175 | 2.477 | |
| plotCxdsResults | 8.749 | 0.669 | 9.563 | |
| plotDEGHeatmap | 3.150 | 0.076 | 3.301 | |
| plotDEGRegression | 6.881 | 0.259 | 7.314 | |
| plotDEGViolin | 8.656 | 0.271 | 8.954 | |
| plotDEGVolcano | 1.296 | 0.040 | 1.338 | |
| plotDecontXResults | 10.698 | 0.319 | 11.138 | |
| plotDimRed | 0.486 | 0.015 | 0.504 | |
| plotDoubletFinderResults | 45.465 | 2.136 | 47.854 | |
| plotEmptyDropsResults | 5.889 | 0.093 | 6.001 | |
| plotEmptyDropsScatter | 5.710 | 0.072 | 5.790 | |
| plotFindMarkerHeatmap | 5.668 | 0.180 | 5.861 | |
| plotMASTThresholdGenes | 1.910 | 0.040 | 1.954 | |
| plotPCA | 0.521 | 0.012 | 0.535 | |
| plotPathway | 0.954 | 0.008 | 0.966 | |
| plotRunPerCellQCResults | 4.822 | 0.115 | 4.949 | |
| plotSCEBarAssayData | 0.502 | 0.031 | 0.535 | |
| plotSCEBarColData | 0.334 | 0.000 | 0.334 | |
| plotSCEBatchFeatureMean | 0.609 | 0.020 | 0.631 | |
| plotSCEDensity | 0.485 | 0.004 | 0.489 | |
| plotSCEDensityAssayData | 0.487 | 0.012 | 0.500 | |
| plotSCEDensityColData | 0.444 | 0.008 | 0.453 | |
| plotSCEDimReduceColData | 1.186 | 0.004 | 1.197 | |
| plotSCEDimReduceFeatures | 0.580 | 0.004 | 0.585 | |
| plotSCEHeatmap | 0.616 | 0.008 | 0.626 | |
| plotSCEScatter | 2.240 | 0.188 | 2.433 | |
| plotSCEViolin | 0.549 | 0.004 | 0.553 | |
| plotSCEViolinAssayData | 0.614 | 0.008 | 0.625 | |
| plotSCEViolinColData | 0.624 | 0.020 | 0.645 | |
| plotScDblFinderResults | 34.831 | 0.619 | 35.802 | |
| plotScanpyDotPlot | 0.022 | 0.003 | 0.025 | |
| plotScanpyEmbedding | 0.025 | 0.000 | 0.025 | |
| plotScanpyHVG | 0.022 | 0.003 | 0.026 | |
| plotScanpyHeatmap | 0.025 | 0.000 | 0.026 | |
| plotScanpyMarkerGenes | 0.027 | 0.000 | 0.027 | |
| plotScanpyMarkerGenesDotPlot | 0.029 | 0.000 | 0.029 | |
| plotScanpyMarkerGenesHeatmap | 0.027 | 0.000 | 0.026 | |
| plotScanpyMarkerGenesMatrixPlot | 0.026 | 0.000 | 0.027 | |
| plotScanpyMarkerGenesViolin | 0.028 | 0.000 | 0.028 | |
| plotScanpyMatrixPlot | 0.028 | 0.000 | 0.028 | |
| plotScanpyPCA | 0.024 | 0.004 | 0.028 | |
| plotScanpyPCAGeneRanking | 0.024 | 0.000 | 0.024 | |
| plotScanpyPCAVariance | 0.024 | 0.000 | 0.025 | |
| plotScanpyViolin | 0.025 | 0.000 | 0.025 | |
| plotScdsHybridResults | 12.500 | 0.117 | 12.673 | |
| plotScrubletResults | 0.024 | 0.000 | 0.024 | |
| plotSeuratElbow | 0.023 | 0.000 | 0.022 | |
| plotSeuratHVG | 0.022 | 0.000 | 0.023 | |
| plotSeuratJackStraw | 0.023 | 0.000 | 0.022 | |
| plotSeuratReduction | 0.022 | 0.000 | 0.023 | |
| plotSoupXResults | 0 | 0 | 0 | |
| plotTSCANClusterDEG | 7.230 | 0.044 | 7.298 | |
| plotTSCANClusterPseudo | 1.946 | 0.008 | 1.960 | |
| plotTSCANDimReduceFeatures | 2.079 | 0.028 | 2.114 | |
| plotTSCANPseudotimeGenes | 2.502 | 0.012 | 2.524 | |
| plotTSCANPseudotimeHeatmap | 1.947 | 0.000 | 1.953 | |
| plotTSCANResults | 1.943 | 0.021 | 1.970 | |
| plotTSNE | 0.569 | 0.008 | 0.579 | |
| plotTopHVG | 0.903 | 0.000 | 0.906 | |
| plotUMAP | 7.943 | 0.151 | 8.127 | |
| readSingleCellMatrix | 0.007 | 0.000 | 0.007 | |
| reportCellQC | 0.108 | 0.001 | 0.108 | |
| reportDropletQC | 0.024 | 0.000 | 0.025 | |
| reportQCTool | 0.111 | 0.000 | 0.111 | |
| retrieveSCEIndex | 0.031 | 0.004 | 0.034 | |
| runBBKNN | 0 | 0 | 0 | |
| runBarcodeRankDrops | 0.317 | 0.000 | 0.317 | |
| runBcds | 3.166 | 0.087 | 3.216 | |
| runCellQC | 0.105 | 0.000 | 0.106 | |
| runClusterSummaryMetrics | 0.511 | 0.004 | 0.515 | |
| runComBatSeq | 0.643 | 0.012 | 0.658 | |
| runCxds | 0.414 | 0.004 | 0.419 | |
| runCxdsBcdsHybrid | 2.774 | 0.105 | 2.466 | |
| runDEAnalysis | 0.624 | 0.020 | 0.646 | |
| runDecontX | 7.765 | 0.041 | 7.825 | |
| runDimReduce | 0.380 | 0.004 | 0.386 | |
| runDoubletFinder | 36.649 | 0.289 | 37.042 | |
| runDropletQC | 0.027 | 0.000 | 0.028 | |
| runEmptyDrops | 5.247 | 0.008 | 5.264 | |
| runEnrichR | 0.391 | 0.016 | 27.043 | |
| runFastMNN | 2.451 | 0.008 | 2.487 | |
| runFeatureSelection | 0.326 | 0.000 | 0.327 | |
| runFindMarker | 2.091 | 0.016 | 2.113 | |