Back to Multiple platform build/check report for BioC 3.16 |
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This page was generated on 2022-08-16 11:05:55 -0400 (Tue, 16 Aug 2022).
Hostname | OS | Arch (*) | R version | Installed pkgs |
---|---|---|---|---|
nebbiolo2 | Linux (Ubuntu 20.04.4 LTS) | x86_64 | 4.2.1 (2022-06-23) -- "Funny-Looking Kid" | 4378 |
palomino4 | Windows Server 2022 Datacenter | x64 | 4.2.1 (2022-06-23 ucrt) -- "Funny-Looking Kid" | 4161 |
lconway | macOS 12.2.1 Monterey | x86_64 | 4.2.1 Patched (2022-07-09 r82577) -- "Funny-Looking Kid" | 4169 |
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 |
To the developers/maintainers of the singleCellTK package: - Please 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 How and When does the builder pull? When will my changes propagate? for more information. - Make sure to use the following settings in order to reproduce any error or warning you see on this page. |
Package 1854/2140 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
singleCellTK 2.7.1 (landing page) Yichen Wang
| nebbiolo2 | Linux (Ubuntu 20.04.4 LTS) / x86_64 | OK | OK | ERROR | |||||||||
palomino4 | Windows Server 2022 Datacenter / x64 | OK | OK | ERROR | OK | |||||||||
lconway | macOS 12.2.1 Monterey / x86_64 | OK | OK | ERROR | OK | |||||||||
Package: singleCellTK |
Version: 2.7.1 |
Command: /home/biocbuild/bbs-3.16-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.16-bioc/R/library --no-vignettes --timings singleCellTK_2.7.1.tar.gz |
StartedAt: 2022-08-15 21:56:37 -0400 (Mon, 15 Aug 2022) |
EndedAt: 2022-08-15 22:09:46 -0400 (Mon, 15 Aug 2022) |
EllapsedTime: 788.6 seconds |
RetCode: 1 |
Status: ERROR |
CheckDir: singleCellTK.Rcheck |
Warnings: NA |
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.16-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.16-bioc/R/library --no-vignettes --timings singleCellTK_2.7.1.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/home/biocbuild/bbs-3.16-bioc/meat/singleCellTK.Rcheck’ * using R version 4.2.1 (2022-06-23) * using platform: x86_64-pc-linux-gnu (64-bit) * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘singleCellTK/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘singleCellTK’ version ‘2.7.1’ * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * 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 ... NOTE installed size is 6.6Mb sub-directories of 1Mb or more: extdata 1.6Mb 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 R 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 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 ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of ‘data’ directory ... OK * checking data for non-ASCII characters ... OK * checking data for ASCII and uncompressed saves ... OK * checking R/sysdata.rda ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK Examples with CPU (user + system) or elapsed time > 5s user system elapsed plotScDblFinderResults 27.017 0.556 27.302 plotDoubletFinderResults 26.549 0.527 27.068 runDoubletFinder 20.797 0.107 20.905 runScDblFinder 17.649 0.144 17.528 importExampleData 15.698 1.851 18.221 plotBatchCorrCompare 12.407 0.470 12.859 plotScdsHybridResults 10.096 0.176 9.374 plotBcdsResults 9.012 0.221 8.268 plotDecontXResults 7.754 0.340 8.093 plotUMAP 7.587 0.092 7.671 runDecontX 7.432 0.084 7.516 plotEmptyDropsResults 6.776 0.068 6.845 plotEmptyDropsScatter 6.675 0.048 6.724 plotCxdsResults 6.533 0.113 6.635 detectCellOutlier 6.167 0.351 6.518 runEmptyDrops 6.437 0.004 6.441 getUMAP 4.730 0.508 5.230 * 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: 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| [ FAIL 1 | WARN 18 | SKIP 0 | PASS 225 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure (test-cellTypeLabeling.R:14:3): Testing SingleR ───────────────────── "SingleR_hpca_main_first.labels" %in% names(colData(sce)) is not TRUE `actual`: FALSE `expected`: TRUE [ FAIL 1 | WARN 18 | SKIP 0 | PASS 225 ] Error: Test failures Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes in ‘inst/doc’ ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 1 ERROR, 1 NOTE See ‘/home/biocbuild/bbs-3.16-bioc/meat/singleCellTK.Rcheck/00check.log’ for details.
singleCellTK.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.16-bioc/R/bin/R CMD INSTALL singleCellTK ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/bbs-3.16-bioc/R/library’ * installing *source* package ‘singleCellTK’ ... ** 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 4.2.1 (2022-06-23) -- "Funny-Looking Kid" Copyright (C) 2022 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > if (requireNamespace('spelling', quietly = TRUE)) + spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE) NULL > > proc.time() user system elapsed 0.146 0.034 0.165
singleCellTK.Rcheck/tests/testthat.Rout.fail
R version 4.2.1 (2022-06-23) -- "Funny-Looking Kid" Copyright (C) 2022 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 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, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which.max, which.min Loading required package: S4Vectors Attaching package: 'S4Vectors' The following objects are masked from 'package:base': I, expand.grid, unname Loading required package: IRanges Loading required package: GenomeInfoDb 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 Attaching package: 'DelayedArray' The following objects are masked from 'package:base': aperm, apply, rowsum, 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% [----|----|----|----|----|----|----|----|----|----| **************************************************| Estimating GSVA scores for 34 gene sets. Estimating ECDFs with Gaussian kernels | | | 0% | |== | 3% | |==== | 6% | |====== | 9% | |======== | 12% | |========== | 15% | |============ | 18% | |============== | 21% | |================ | 24% | |=================== | 26% | |===================== | 29% | |======================= | 32% | |========================= | 35% | |=========================== | 38% | |============================= | 41% | |=============================== | 44% | |================================= | 47% | |=================================== | 50% | |===================================== | 53% | |======================================= | 56% | |========================================= | 59% | |=========================================== | 62% | |============================================= | 65% | |=============================================== | 68% | |================================================= | 71% | |=================================================== | 74% | |====================================================== | 76% | |======================================================== | 79% | |========================================================== | 82% | |============================================================ | 85% | |============================================================== | 88% | |================================================================ | 91% | |================================================================== | 94% | |==================================================================== | 97% | |======================================================================| 100% Estimating GSVA scores for 2 gene sets. Estimating ECDFs with Gaussian kernels | | | 0% | |=================================== | 50% | |======================================================================| 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% 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: 9590 Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Maximum modularity in 10 random starts: 0.8042 Number of communities: 6 Elapsed time: 0 seconds 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| | | | 0% | |======================================================================| 100% | | | 0% | |======================================================================| 100% | | | 0% | |======================================================================| 100% | | | 0% | |======================================================================| 100% 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| | | | 0% | |======================================================================| 100% | | | 0% | |======================================================================| 100% | | | 0% | |======================================================================| 100% | | | 0% | |======================================================================| 100% | | | 0% | |======================================================================| 100% Performing log-normalization 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| [ FAIL 1 | WARN 18 | SKIP 0 | PASS 225 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure (test-cellTypeLabeling.R:14:3): Testing SingleR ───────────────────── "SingleR_hpca_main_first.labels" %in% names(colData(sce)) is not TRUE `actual`: FALSE `expected`: TRUE [ FAIL 1 | WARN 18 | SKIP 0 | PASS 225 ] Error: Test failures Execution halted
singleCellTK.Rcheck/singleCellTK-Ex.timings
name | user | system | elapsed | |
MitoGenes | 0.003 | 0.001 | 0.003 | |
SEG | 0.002 | 0.000 | 0.002 | |
calcEffectSizes | 0.180 | 0.012 | 0.192 | |
combineSCE | 1.632 | 0.079 | 1.712 | |
computeZScore | 0.378 | 0.073 | 0.450 | |
convertSCEToSeurat | 3.665 | 0.907 | 4.572 | |
convertSeuratToSCE | 0.566 | 0.024 | 0.590 | |
dedupRowNames | 0.057 | 0.000 | 0.056 | |
detectCellOutlier | 6.167 | 0.351 | 6.518 | |
diffAbundanceFET | 0.089 | 0.004 | 0.092 | |
discreteColorPalette | 0.007 | 0.000 | 0.006 | |
distinctColors | 0.002 | 0.000 | 0.002 | |
downSampleCells | 0.650 | 0.044 | 0.694 | |
downSampleDepth | 0.490 | 0.012 | 0.502 | |
expData-ANY-character-method | 0.324 | 0.000 | 0.324 | |
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method | 0.349 | 0.008 | 0.357 | |
expData-set | 0.327 | 0.024 | 0.351 | |
expData | 0.330 | 0.008 | 0.338 | |
expDataNames-ANY-method | 0.316 | 0.008 | 0.323 | |
expDataNames | 0.309 | 0.004 | 0.312 | |
expDeleteDataTag | 0.041 | 0.000 | 0.041 | |
expSetDataTag | 0.026 | 0.000 | 0.026 | |
expTaggedData | 0.027 | 0.000 | 0.028 | |
exportSCE | 0.023 | 0.000 | 0.023 | |
exportSCEtoAnnData | 0.090 | 0.008 | 0.099 | |
exportSCEtoFlatFile | 0.087 | 0.008 | 0.095 | |
featureIndex | 0.037 | 0.000 | 0.037 | |
findMarkerDiffExp | 3.849 | 0.108 | 3.956 | |
findMarkerTopTable | 3.360 | 0.076 | 3.437 | |
generateSimulatedData | 0.043 | 0.000 | 0.043 | |
getBiomarker | 0.051 | 0.000 | 0.051 | |
getDEGTopTable | 0.559 | 0.000 | 0.559 | |
getDiffAbundanceResults | 0.033 | 0.004 | 0.036 | |
getEnrichRResult | 0.303 | 0.012 | 2.783 | |
getMSigDBTable | 0.003 | 0.000 | 0.003 | |
getPathwayResultNames | 0.023 | 0.000 | 0.024 | |
getSampleSummaryStatsTable | 0.316 | 0.000 | 0.316 | |
getSoupX | 0.384 | 0.000 | 0.385 | |
getTSCANResults | 1.823 | 0.004 | 1.827 | |
getTSNE | 0.901 | 0.000 | 0.902 | |
getTopHVG | 0.843 | 0.007 | 0.852 | |
getUMAP | 4.730 | 0.508 | 5.230 | |
importAnnData | 0.001 | 0.000 | 0.001 | |
importBUStools | 0.258 | 0.000 | 0.258 | |
importCellRanger | 0.988 | 0.092 | 1.080 | |
importCellRangerV2Sample | 0.263 | 0.024 | 0.286 | |
importCellRangerV3Sample | 0.353 | 0.032 | 0.385 | |
importDropEst | 0.326 | 0.020 | 0.346 | |
importExampleData | 15.698 | 1.851 | 18.221 | |
importGeneSetsFromCollection | 0.726 | 0.036 | 0.762 | |
importGeneSetsFromGMT | 0.061 | 0.012 | 0.074 | |
importGeneSetsFromList | 0.117 | 0.004 | 0.121 | |
importGeneSetsFromMSigDB | 3.898 | 0.260 | 4.158 | |
importMitoGeneSet | 0.051 | 0.000 | 0.051 | |
importOptimus | 0.001 | 0.000 | 0.001 | |
importSEQC | 0.299 | 0.036 | 0.335 | |
importSTARsolo | 0.255 | 0.004 | 0.260 | |
iterateSimulations | 0.304 | 0.012 | 0.316 | |
listSampleSummaryStatsTables | 0.411 | 0.036 | 0.446 | |
mergeSCEColData | 0.492 | 0.012 | 0.504 | |
mouseBrainSubsetSCE | 0.025 | 0.000 | 0.026 | |
msigdb_table | 0.001 | 0.000 | 0.002 | |
plotBarcodeRankDropsResults | 0.913 | 0.012 | 0.925 | |
plotBarcodeRankScatter | 0.668 | 0.012 | 0.681 | |
plotBatchCorrCompare | 12.407 | 0.470 | 12.859 | |
plotBatchVariance | 0.261 | 0.008 | 0.268 | |
plotBcdsResults | 9.012 | 0.221 | 8.268 | |
plotClusterAbundance | 0.906 | 0.024 | 0.931 | |
plotCxdsResults | 6.533 | 0.113 | 6.635 | |
plotDEGHeatmap | 3.020 | 0.036 | 3.056 | |
plotDEGRegression | 3.530 | 0.100 | 3.617 | |
plotDEGViolin | 4.191 | 0.080 | 4.257 | |
plotDEGVolcano | 1.024 | 0.004 | 1.028 | |
plotDecontXResults | 7.754 | 0.340 | 8.093 | |
plotDimRed | 0.291 | 0.040 | 0.331 | |
plotDoubletFinderResults | 26.549 | 0.527 | 27.068 | |
plotEmptyDropsResults | 6.776 | 0.068 | 6.845 | |
plotEmptyDropsScatter | 6.675 | 0.048 | 6.724 | |
plotMASTThresholdGenes | 1.562 | 0.020 | 1.582 | |
plotMarkerDiffExp | 4.486 | 0.040 | 4.526 | |
plotPCA | 0.454 | 0.004 | 0.457 | |
plotPathway | 0.771 | 0.020 | 0.792 | |
plotRunPerCellQCResults | 0.026 | 0.000 | 0.025 | |
plotSCEBarAssayData | 0.140 | 0.008 | 0.149 | |
plotSCEBarColData | 0.108 | 0.020 | 0.127 | |
plotSCEBatchFeatureMean | 0.274 | 0.004 | 0.279 | |
plotSCEDensity | 0.23 | 0.00 | 0.23 | |
plotSCEDensityAssayData | 0.154 | 0.000 | 0.157 | |
plotSCEDensityColData | 0.191 | 0.012 | 0.204 | |
plotSCEDimReduceColData | 0.740 | 0.012 | 0.751 | |
plotSCEDimReduceFeatures | 0.343 | 0.000 | 0.342 | |
plotSCEHeatmap | 0.753 | 0.000 | 0.753 | |
plotSCEScatter | 0.322 | 0.004 | 0.325 | |
plotSCEViolin | 0.200 | 0.004 | 0.204 | |
plotSCEViolinAssayData | 0.228 | 0.004 | 0.232 | |
plotSCEViolinColData | 0.199 | 0.004 | 0.203 | |
plotScDblFinderResults | 27.017 | 0.556 | 27.302 | |
plotScdsHybridResults | 10.096 | 0.176 | 9.374 | |
plotScrubletResults | 0.022 | 0.004 | 0.026 | |
plotSeuratElbow | 0.023 | 0.000 | 0.023 | |
plotSeuratHVG | 0.024 | 0.000 | 0.024 | |
plotSeuratJackStraw | 0.023 | 0.000 | 0.023 | |
plotSeuratReduction | 0.023 | 0.000 | 0.023 | |
plotSoupXResults | 0.187 | 0.008 | 0.195 | |
plotTSCANClusterDEG | 4.943 | 0.052 | 4.995 | |
plotTSCANClusterPseudo | 2.193 | 0.020 | 2.213 | |
plotTSCANDimReduceFeatures | 2.173 | 0.004 | 2.177 | |
plotTSCANPseudotimeGenes | 1.977 | 0.004 | 1.980 | |
plotTSCANPseudotimeHeatmap | 2.348 | 0.000 | 2.348 | |
plotTSCANResults | 2.038 | 0.004 | 2.042 | |
plotTSNE | 0.499 | 0.000 | 0.499 | |
plotTopHVG | 0.423 | 0.000 | 0.423 | |
plotUMAP | 7.587 | 0.092 | 7.671 | |
readSingleCellMatrix | 0.004 | 0.000 | 0.004 | |
reportCellQC | 0.186 | 0.000 | 0.186 | |
reportDropletQC | 0.025 | 0.000 | 0.024 | |
reportQCTool | 0.188 | 0.000 | 0.187 | |
retrieveSCEIndex | 0.033 | 0.000 | 0.033 | |
runBBKNN | 0 | 0 | 0 | |
runBarcodeRankDrops | 0.522 | 0.000 | 0.522 | |
runBcds | 2.535 | 0.036 | 1.587 | |
runCellQC | 0.263 | 0.012 | 0.274 | |
runComBatSeq | 0.467 | 0.004 | 0.471 | |
runCxds | 0.548 | 0.000 | 0.549 | |
runCxdsBcdsHybrid | 2.563 | 0.020 | 1.641 | |
runDEAnalysis | 0.790 | 0.000 | 0.789 | |
runDecontX | 7.432 | 0.084 | 7.516 | |
runDimReduce | 0.563 | 0.000 | 0.563 | |
runDoubletFinder | 20.797 | 0.107 | 20.905 | |
runDropletQC | 0.025 | 0.000 | 0.025 | |
runEmptyDrops | 6.437 | 0.004 | 6.441 | |
runEnrichR | 0.261 | 0.004 | 1.237 | |
runFastMNN | 1.626 | 0.020 | 1.647 | |
runFeatureSelection | 0.196 | 0.004 | 0.200 | |
runGSVA | 0.830 | 0.000 | 0.829 | |
runKMeans | 0.415 | 0.000 | 0.416 | |
runLimmaBC | 0.069 | 0.000 | 0.069 | |
runMNNCorrect | 0.521 | 0.000 | 0.521 | |
runModelGeneVar | 0.501 | 0.000 | 0.500 | |
runNormalization | 0.581 | 0.008 | 0.589 | |
runPerCellQC | 0.496 | 0.004 | 0.500 | |
runSCANORAMA | 0.000 | 0.000 | 0.001 | |
runSCMerge | 0.003 | 0.000 | 0.004 | |
runScDblFinder | 17.649 | 0.144 | 17.528 | |
runScranSNN | 0.727 | 0.004 | 0.731 | |
runScrublet | 0.025 | 0.000 | 0.025 | |
runSeuratFindClusters | 0.024 | 0.000 | 0.024 | |
runSeuratFindHVG | 0.636 | 0.020 | 0.657 | |
runSeuratHeatmap | 0.025 | 0.000 | 0.025 | |
runSeuratICA | 0.024 | 0.000 | 0.024 | |
runSeuratJackStraw | 0.023 | 0.000 | 0.023 | |
runSeuratNormalizeData | 0.023 | 0.000 | 0.024 | |
runSeuratPCA | 0.024 | 0.000 | 0.024 | |
runSeuratSCTransform | 3.152 | 0.140 | 3.294 | |
runSeuratScaleData | 0.025 | 0.000 | 0.026 | |
runSeuratUMAP | 0.024 | 0.000 | 0.024 | |
runSingleR | 0.037 | 0.000 | 0.037 | |
runSoupX | 0.190 | 0.000 | 0.189 | |
runTSCAN | 1.532 | 0.004 | 1.536 | |
runTSCANClusterDEAnalysis | 1.669 | 0.000 | 1.669 | |
runTSCANDEG | 1.542 | 0.032 | 1.574 | |
runVAM | 0.58 | 0.02 | 0.60 | |
runZINBWaVE | 0.004 | 0.000 | 0.004 | |
sampleSummaryStats | 0.31 | 0.00 | 0.31 | |
scaterCPM | 0.133 | 0.000 | 0.133 | |
scaterPCA | 0.569 | 0.000 | 0.569 | |
scaterlogNormCounts | 0.248 | 0.000 | 0.247 | |
sce | 0.024 | 0.000 | 0.024 | |
sctkListGeneSetCollections | 0.075 | 0.000 | 0.076 | |
sctkPythonInstallConda | 0 | 0 | 0 | |
sctkPythonInstallVirtualEnv | 0 | 0 | 0 | |
selectSCTKConda | 0 | 0 | 0 | |
selectSCTKVirtualEnvironment | 0 | 0 | 0 | |
setRowNames | 0.086 | 0.000 | 0.086 | |
setSCTKDisplayRow | 0.377 | 0.004 | 0.381 | |
singleCellTK | 0 | 0 | 0 | |
subDiffEx | 0.459 | 0.000 | 0.459 | |
subsetSCECols | 0.181 | 0.000 | 0.181 | |
subsetSCERows | 0.468 | 0.004 | 0.472 | |
summarizeSCE | 0.06 | 0.00 | 0.06 | |
trimCounts | 0.307 | 0.000 | 0.307 | |