Back to Multiple platform build/check report for BioC 3.22: simplified long |
|
This page was generated on 2025-10-04 12:06 -0400 (Sat, 04 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" | 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 2015/2341 | 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 | OK | ||||||||||
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. |
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: 2025-10-03 21:46:14 -0400 (Fri, 03 Oct 2025) |
EndedAt: 2025-10-03 21:52:39 -0400 (Fri, 03 Oct 2025) |
EllapsedTime: 385.4 seconds |
RetCode: 1 |
Status: ERROR |
CheckDir: singleCellTK.Rcheck |
Warnings: NA |
############################################################################## ############################################################################## ### ### 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 6.8Mb 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") Fri Oct 3 21:50:20 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 17.918 0.079 18.006 plotDoubletFinderResults 17.805 0.072 17.936 runDoubletFinder 15.893 0.051 15.994 plotScDblFinderResults 13.635 0.266 14.149 plotBatchCorrCompare 6.062 0.047 6.130 importExampleData 5.094 0.456 5.953 * 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.
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)
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 0.065 0.018 0.080
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
singleCellTK.Rcheck/singleCellTK-Ex.timings
name | user | system | elapsed | |
MitoGenes | 0.001 | 0.001 | 0.002 | |
SEG | 0.001 | 0.002 | 0.002 | |
calcEffectSizes | 0.070 | 0.004 | 0.075 | |
combineSCE | 0.236 | 0.005 | 0.245 | |
computeZScore | 0.093 | 0.003 | 0.096 | |
convertSCEToSeurat | 1.667 | 0.066 | 1.744 | |
convertSeuratToSCE | 0.122 | 0.003 | 0.124 | |
dedupRowNames | 0.023 | 0.001 | 0.024 | |
detectCellOutlier | 2.715 | 0.032 | 2.747 | |
diffAbundanceFET | 0.026 | 0.002 | 0.027 | |
discreteColorPalette | 0.002 | 0.000 | 0.002 | |
distinctColors | 0 | 0 | 0 | |
downSampleCells | 0.208 | 0.023 | 0.230 | |
downSampleDepth | 0.157 | 0.017 | 0.174 | |
expData-ANY-character-method | 0.044 | 0.002 | 0.046 | |
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method | 0.056 | 0.002 | 0.058 | |
expData-set | 0.050 | 0.002 | 0.052 | |
expData | 0.044 | 0.002 | 0.049 | |
expDataNames-ANY-method | 0.040 | 0.002 | 0.043 | |
expDataNames | 0.040 | 0.004 | 0.045 | |
expDeleteDataTag | 0.016 | 0.002 | 0.018 | |
expSetDataTag | 0.011 | 0.001 | 0.013 | |
expTaggedData | 0.012 | 0.001 | 0.014 | |
exportSCE | 0.011 | 0.002 | 0.012 | |
exportSCEtoAnnData | 0.042 | 0.002 | 0.043 | |
exportSCEtoFlatFile | 0.042 | 0.001 | 0.042 | |
featureIndex | 0.016 | 0.002 | 0.018 | |
generateSimulatedData | 0.023 | 0.002 | 0.025 | |
getBiomarker | 0.025 | 0.002 | 0.027 | |
getDEGTopTable | 0.233 | 0.015 | 0.249 | |
getDiffAbundanceResults | 0.022 | 0.001 | 0.023 | |
getEnrichRResult | 0.125 | 0.019 | 2.703 | |
getFindMarkerTopTable | 0.457 | 0.018 | 0.476 | |
getMSigDBTable | 0.001 | 0.001 | 0.003 | |
getPathwayResultNames | 0.012 | 0.002 | 0.013 | |
getSampleSummaryStatsTable | 0.061 | 0.003 | 0.063 | |
getSoupX | 0 | 0 | 0 | |
getTSCANResults | 0.361 | 0.014 | 0.376 | |
getTopHVG | 0.305 | 0.007 | 0.313 | |
importAnnData | 0.000 | 0.000 | 0.001 | |
importBUStools | 0.049 | 0.002 | 0.051 | |
importCellRanger | 0.224 | 0.010 | 0.234 | |
importCellRangerV2Sample | 0.041 | 0.001 | 0.042 | |
importCellRangerV3Sample | 0.081 | 0.004 | 0.085 | |
importDropEst | 0.063 | 0.001 | 0.064 | |
importExampleData | 5.094 | 0.456 | 5.953 | |
importGeneSetsFromCollection | 0.743 | 0.032 | 0.776 | |
importGeneSetsFromGMT | 0.027 | 0.002 | 0.029 | |
importGeneSetsFromList | 0.043 | 0.002 | 0.045 | |
importGeneSetsFromMSigDB | 17.918 | 0.079 | 18.006 | |
importMitoGeneSet | 0.021 | 0.003 | 0.024 | |
importOptimus | 0.001 | 0.000 | 0.000 | |
importSEQC | 0.049 | 0.003 | 0.053 | |
importSTARsolo | 0.046 | 0.004 | 0.050 | |
iterateSimulations | 0.070 | 0.007 | 0.076 | |
listSampleSummaryStatsTables | 0.119 | 0.005 | 0.124 | |
mergeSCEColData | 0.111 | 0.008 | 0.118 | |
mouseBrainSubsetSCE | 0.018 | 0.002 | 0.020 | |
msigdb_table | 0.000 | 0.001 | 0.002 | |
plotBarcodeRankDropsResults | 0.287 | 0.009 | 0.296 | |
plotBarcodeRankScatter | 0.280 | 0.004 | 0.283 | |
plotBatchCorrCompare | 6.062 | 0.047 | 6.130 | |
plotBatchVariance | 0.143 | 0.002 | 0.145 | |
plotBcdsResults | 3.882 | 0.060 | 3.973 | |
plotBubble | 0.260 | 0.007 | 0.270 | |
plotClusterAbundance | 0.461 | 0.004 | 0.472 | |
plotCxdsResults | 3.127 | 0.028 | 3.231 | |
plotDEGHeatmap | 0.686 | 0.010 | 0.696 | |
plotDEGRegression | 1.362 | 0.016 | 1.377 | |
plotDEGViolin | 2.423 | 0.060 | 2.484 | |
plotDEGVolcano | 0.335 | 0.004 | 0.340 | |
plotDecontXResults | 3.636 | 0.022 | 3.659 | |
plotDimRed | 0.119 | 0.003 | 0.121 | |
plotDoubletFinderResults | 17.805 | 0.072 | 17.936 | |
plotEmptyDropsResults | 2.155 | 0.005 | 2.160 | |
plotEmptyDropsScatter | 2.126 | 0.008 | 2.138 | |
plotFindMarkerHeatmap | 1.262 | 0.009 | 1.276 | |
plotMASTThresholdGenes | 0.438 | 0.014 | 0.456 | |
plotPCA | 0.124 | 0.003 | 0.127 | |
plotPathway | 0.207 | 0.004 | 0.213 | |
plotRunPerCellQCResults | 1.007 | 0.006 | 1.041 | |
plotSCEBarAssayData | 0.129 | 0.004 | 0.133 | |
plotSCEBarColData | 0.084 | 0.003 | 0.089 | |
plotSCEBatchFeatureMean | 0.122 | 0.001 | 0.124 | |
plotSCEDensity | 0.105 | 0.003 | 0.109 | |
plotSCEDensityAssayData | 0.119 | 0.003 | 0.131 | |
plotSCEDensityColData | 0.102 | 0.003 | 0.105 | |
plotSCEDimReduceColData | 0.256 | 0.004 | 0.261 | |
plotSCEDimReduceFeatures | 0.122 | 0.003 | 0.125 | |
plotSCEHeatmap | 0.135 | 0.002 | 0.137 | |
plotSCEScatter | 0.138 | 0.004 | 0.142 | |
plotSCEViolin | 0.118 | 0.004 | 0.121 | |
plotSCEViolinAssayData | 0.131 | 0.005 | 0.136 | |
plotSCEViolinColData | 0.208 | 0.006 | 0.215 | |
plotScDblFinderResults | 13.635 | 0.266 | 14.149 | |
plotScanpyDotPlot | 0.012 | 0.001 | 0.014 | |
plotScanpyEmbedding | 0.011 | 0.000 | 0.012 | |
plotScanpyHVG | 0.011 | 0.000 | 0.012 | |
plotScanpyHeatmap | 0.011 | 0.000 | 0.012 | |
plotScanpyMarkerGenes | 0.011 | 0.001 | 0.012 | |
plotScanpyMarkerGenesDotPlot | 0.011 | 0.001 | 0.012 | |
plotScanpyMarkerGenesHeatmap | 0.011 | 0.001 | 0.011 | |
plotScanpyMarkerGenesMatrixPlot | 0.012 | 0.001 | 0.012 | |
plotScanpyMarkerGenesViolin | 0.012 | 0.002 | 0.013 | |
plotScanpyMatrixPlot | 0.013 | 0.003 | 0.016 | |
plotScanpyPCA | 0.012 | 0.003 | 0.015 | |
plotScanpyPCAGeneRanking | 0.011 | 0.003 | 0.014 | |
plotScanpyPCAVariance | 0.012 | 0.003 | 0.014 | |
plotScanpyViolin | 0.011 | 0.003 | 0.013 | |
plotScdsHybridResults | 4.429 | 0.092 | 4.558 | |
plotScrubletResults | 0.011 | 0.002 | 0.013 | |
plotSeuratElbow | 0.011 | 0.001 | 0.013 | |
plotSeuratHVG | 0.012 | 0.002 | 0.017 | |
plotSeuratJackStraw | 0.012 | 0.002 | 0.013 | |
plotSeuratReduction | 0.011 | 0.001 | 0.012 | |
plotSoupXResults | 0 | 0 | 0 | |
plotTSCANClusterDEG | 1.538 | 0.020 | 1.559 | |
plotTSCANClusterPseudo | 0.444 | 0.013 | 0.479 | |
plotTSCANDimReduceFeatures | 0.446 | 0.010 | 0.460 | |
plotTSCANPseudotimeGenes | 0.534 | 0.009 | 0.551 | |
plotTSCANPseudotimeHeatmap | 0.419 | 0.007 | 0.426 | |
plotTSCANResults | 0.409 | 0.011 | 0.427 | |
plotTSNE | 0.127 | 0.006 | 0.137 | |
plotTopHVG | 0.202 | 0.006 | 0.213 | |
plotUMAP | 3.280 | 0.033 | 3.517 | |
readSingleCellMatrix | 0.002 | 0.000 | 0.002 | |
reportCellQC | 0.029 | 0.004 | 0.033 | |
reportDropletQC | 0.011 | 0.004 | 0.016 | |
reportQCTool | 0.029 | 0.004 | 0.033 | |
retrieveSCEIndex | 0.014 | 0.004 | 0.018 | |
runBBKNN | 0 | 0 | 0 | |
runBarcodeRankDrops | 0.078 | 0.004 | 0.081 | |
runBcds | 0.605 | 0.038 | 0.674 | |
runCellQC | 0.033 | 0.003 | 0.036 | |
runClusterSummaryMetrics | 0.119 | 0.010 | 0.138 | |
runComBatSeq | 0.160 | 0.010 | 0.169 | |
runCxds | 0.116 | 0.009 | 0.130 | |
runCxdsBcdsHybrid | 0.629 | 0.036 | 0.691 | |
runDEAnalysis | 0.167 | 0.015 | 0.182 | |
runDecontX | 3.293 | 0.024 | 3.394 | |
runDimReduce | 0.090 | 0.002 | 0.094 | |
runDoubletFinder | 15.893 | 0.051 | 15.994 | |
runDropletQC | 0.013 | 0.003 | 0.015 | |
runEmptyDrops | 2.015 | 0.004 | 2.019 | |
runEnrichR | 0.119 | 0.015 | 2.053 | |
runFastMNN | 0.563 | 0.042 | 0.606 | |
runFeatureSelection | 0.080 | 0.002 | 0.083 | |
runFindMarker | 0.444 | 0.012 | 0.457 | |