Back to Multiple platform build/check report for BioC 3.16: simplified long |
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This page was generated on 2023-04-12 11:06:27 -0400 (Wed, 12 Apr 2023).
Hostname | OS | Arch (*) | R version | Installed pkgs |
---|---|---|---|---|
nebbiolo2 | Linux (Ubuntu 20.04.5 LTS) | x86_64 | 4.2.3 (2023-03-15) -- "Shortstop Beagle" | 4502 |
palomino4 | Windows Server 2022 Datacenter | x64 | 4.2.3 (2023-03-15 ucrt) -- "Shortstop Beagle" | 4282 |
lconway | macOS 12.5.1 Monterey | x86_64 | 4.2.3 (2023-03-15) -- "Shortstop Beagle" | 4310 |
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 1889/2183 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
singleCellTK 2.8.0 (landing page) Yichen Wang
| nebbiolo2 | Linux (Ubuntu 20.04.5 LTS) / x86_64 | OK | OK | TIMEOUT | |||||||||
palomino4 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | ![]() | ||||||||
lconway | macOS 12.5.1 Monterey / x86_64 | OK | OK | OK | OK | ![]() | ||||||||
Package: singleCellTK |
Version: 2.8.0 |
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_2.8.0.tar.gz |
StartedAt: 2023-04-10 22:44:13 -0400 (Mon, 10 Apr 2023) |
EndedAt: 2023-04-10 22:56:28 -0400 (Mon, 10 Apr 2023) |
EllapsedTime: 735.3 seconds |
RetCode: 0 |
Status: OK |
CheckDir: singleCellTK.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### 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_2.8.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/Users/biocbuild/bbs-3.16-bioc/meat/singleCellTK.Rcheck’ * using R version 4.2.3 (2023-03-15) * using platform: x86_64-apple-darwin17.0 (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 ‘2.8.0’ * 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 1.5Mb shiny 2.8Mb * 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 22.847 0.721 23.614 plotDoubletFinderResults 21.231 0.180 21.447 importExampleData 16.778 1.690 19.078 runDoubletFinder 16.502 0.107 16.630 runScDblFinder 15.302 0.377 15.724 plotBatchCorrCompare 9.561 0.107 9.677 plotScdsHybridResults 7.928 0.163 8.103 plotDecontXResults 6.874 0.065 6.953 plotBcdsResults 6.654 0.128 6.790 runDecontX 6.113 0.088 6.211 plotEmptyDropsScatter 5.781 0.023 5.827 runUMAP 5.651 0.077 5.727 plotUMAP 5.606 0.079 5.691 plotCxdsResults 5.475 0.102 5.586 plotEmptyDropsResults 5.546 0.019 5.575 runEmptyDrops 5.370 0.017 5.400 detectCellOutlier 5.036 0.135 5.188 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘spelling.R’ Running ‘testthat.R’ OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes 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 NOTE See ‘/Users/biocbuild/bbs-3.16-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/4.2/Resources/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 4.2.3 (2023-03-15) -- "Shortstop Beagle" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin17.0 (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 0.174 0.063 0.228
singleCellTK.Rcheck/tests/testthat.Rout
R version 4.2.3 (2023-03-15) -- "Shortstop Beagle" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin17.0 (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, aperm, 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': 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% [----|----|----|----|----|----|----|----|----|----| **************************************************| Error in fitdistr(mahalanobis.sq.null[nonzero.values], "gamma", lower = 0.01) : optimization failed 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 Using method 'umap' 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 0 | WARN 21 | SKIP 0 | PASS 221 ] [ FAIL 0 | WARN 21 | SKIP 0 | PASS 221 ] > > proc.time() user system elapsed 206.647 5.047 215.683
singleCellTK.Rcheck/singleCellTK-Ex.timings
name | user | system | elapsed | |
MitoGenes | 0.002 | 0.002 | 0.004 | |
SEG | 0.002 | 0.002 | 0.004 | |
calcEffectSizes | 0.129 | 0.005 | 0.133 | |
combineSCE | 1.266 | 0.020 | 1.292 | |
computeZScore | 0.260 | 0.010 | 0.271 | |
convertSCEToSeurat | 3.181 | 0.201 | 3.393 | |
convertSeuratToSCE | 0.386 | 0.006 | 0.391 | |
dedupRowNames | 0.054 | 0.003 | 0.057 | |
detectCellOutlier | 5.036 | 0.135 | 5.188 | |
diffAbundanceFET | 0.037 | 0.001 | 0.039 | |
discreteColorPalette | 0.005 | 0.000 | 0.006 | |
distinctColors | 0.002 | 0.000 | 0.002 | |
downSampleCells | 0.600 | 0.057 | 0.659 | |
downSampleDepth | 0.540 | 0.050 | 0.593 | |
expData-ANY-character-method | 0.361 | 0.009 | 0.370 | |
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method | 0.345 | 0.007 | 0.352 | |
expData-set | 0.356 | 0.008 | 0.365 | |
expData | 0.319 | 0.005 | 0.324 | |
expDataNames-ANY-method | 0.299 | 0.004 | 0.305 | |
expDataNames | 0.264 | 0.006 | 0.271 | |
expDeleteDataTag | 0.041 | 0.005 | 0.047 | |
expSetDataTag | 0.026 | 0.002 | 0.028 | |
expTaggedData | 0.030 | 0.002 | 0.033 | |
exportSCE | 0.028 | 0.005 | 0.034 | |
exportSCEtoAnnData | 0.084 | 0.002 | 0.085 | |
exportSCEtoFlatFile | 0.091 | 0.003 | 0.094 | |
featureIndex | 0.035 | 0.004 | 0.039 | |
generateSimulatedData | 0.043 | 0.003 | 0.045 | |
getBiomarker | 0.043 | 0.003 | 0.046 | |
getDEGTopTable | 0.807 | 0.034 | 0.843 | |
getDiffAbundanceResults | 0.039 | 0.001 | 0.041 | |
getEnrichRResult | 0.313 | 0.040 | 1.930 | |
getFindMarkerTopTable | 3.016 | 0.038 | 3.062 | |
getMSigDBTable | 0.003 | 0.002 | 0.005 | |
getPathwayResultNames | 0.023 | 0.004 | 0.027 | |
getSampleSummaryStatsTable | 0.346 | 0.005 | 0.352 | |
getSoupX | 0.395 | 0.013 | 0.409 | |
getTSCANResults | 1.610 | 0.032 | 1.646 | |
getTopHVG | 0.662 | 0.009 | 0.673 | |
importAnnData | 0.001 | 0.001 | 0.001 | |
importBUStools | 0.250 | 0.003 | 0.254 | |
importCellRanger | 0.876 | 0.026 | 0.910 | |
importCellRangerV2Sample | 0.246 | 0.002 | 0.250 | |
importCellRangerV3Sample | 0.335 | 0.012 | 0.349 | |
importDropEst | 0.296 | 0.004 | 0.301 | |
importExampleData | 16.778 | 1.690 | 19.078 | |
importGeneSetsFromCollection | 0.748 | 0.121 | 0.878 | |
importGeneSetsFromGMT | 0.069 | 0.005 | 0.075 | |
importGeneSetsFromList | 0.112 | 0.005 | 0.117 | |
importGeneSetsFromMSigDB | 3.034 | 0.122 | 3.167 | |
importMitoGeneSet | 0.045 | 0.007 | 0.052 | |
importOptimus | 0.001 | 0.000 | 0.002 | |
importSEQC | 0.257 | 0.007 | 0.267 | |
importSTARsolo | 0.276 | 0.009 | 0.287 | |
iterateSimulations | 0.330 | 0.017 | 0.348 | |
listSampleSummaryStatsTables | 0.406 | 0.021 | 0.428 | |
mergeSCEColData | 0.450 | 0.019 | 0.477 | |
mouseBrainSubsetSCE | 0.026 | 0.002 | 0.028 | |
msigdb_table | 0.001 | 0.002 | 0.004 | |
plotBarcodeRankDropsResults | 0.725 | 0.011 | 0.738 | |
plotBarcodeRankScatter | 0.713 | 0.015 | 0.731 | |
plotBatchCorrCompare | 9.561 | 0.107 | 9.677 | |
plotBatchVariance | 0.289 | 0.006 | 0.297 | |
plotBcdsResults | 6.654 | 0.128 | 6.790 | |
plotClusterAbundance | 0.886 | 0.038 | 0.926 | |
plotCxdsResults | 5.475 | 0.102 | 5.586 | |
plotDEGHeatmap | 2.514 | 0.077 | 2.602 | |
plotDEGRegression | 3.136 | 0.046 | 3.192 | |
plotDEGViolin | 3.499 | 0.072 | 3.575 | |
plotDEGVolcano | 0.956 | 0.012 | 0.969 | |
plotDecontXResults | 6.874 | 0.065 | 6.953 | |
plotDimRed | 0.224 | 0.003 | 0.228 | |
plotDoubletFinderResults | 21.231 | 0.180 | 21.447 | |
plotEmptyDropsResults | 5.546 | 0.019 | 5.575 | |
plotEmptyDropsScatter | 5.781 | 0.023 | 5.827 | |
plotFindMarkerHeatmap | 4.258 | 0.029 | 4.311 | |
plotMASTThresholdGenes | 1.377 | 0.020 | 1.409 | |
plotPCA | 0.576 | 0.009 | 0.612 | |
plotPathway | 0.795 | 0.013 | 0.814 | |
plotRunPerCellQCResults | 1.216 | 0.015 | 1.236 | |
plotSCEBarAssayData | 0.177 | 0.005 | 0.183 | |
plotSCEBarColData | 0.132 | 0.003 | 0.136 | |
plotSCEBatchFeatureMean | 0.248 | 0.003 | 0.251 | |
plotSCEDensity | 0.208 | 0.004 | 0.213 | |
plotSCEDensityAssayData | 0.161 | 0.004 | 0.172 | |
plotSCEDensityColData | 0.212 | 0.005 | 0.218 | |
plotSCEDimReduceColData | 0.837 | 0.009 | 0.852 | |
plotSCEDimReduceFeatures | 0.301 | 0.004 | 0.305 | |
plotSCEHeatmap | 0.657 | 0.008 | 0.667 | |
plotSCEScatter | 0.419 | 0.009 | 0.430 | |
plotSCEViolin | 0.188 | 0.004 | 0.193 | |
plotSCEViolinAssayData | 0.252 | 0.004 | 0.257 | |
plotSCEViolinColData | 0.200 | 0.004 | 0.206 | |
plotScDblFinderResults | 22.847 | 0.721 | 23.614 | |
plotScdsHybridResults | 7.928 | 0.163 | 8.103 | |
plotScrubletResults | 0.020 | 0.002 | 0.023 | |
plotSeuratElbow | 0.020 | 0.002 | 0.022 | |
plotSeuratHVG | 0.022 | 0.002 | 0.025 | |
plotSeuratJackStraw | 0.021 | 0.002 | 0.023 | |
plotSeuratReduction | 0.019 | 0.002 | 0.022 | |
plotSoupXResults | 0.160 | 0.004 | 0.166 | |
plotTSCANClusterDEG | 4.789 | 0.075 | 4.871 | |
plotTSCANClusterPseudo | 2.042 | 0.023 | 2.070 | |
plotTSCANDimReduceFeatures | 1.897 | 0.015 | 1.914 | |
plotTSCANPseudotimeGenes | 1.874 | 0.018 | 1.896 | |
plotTSCANPseudotimeHeatmap | 1.922 | 0.016 | 1.941 | |
plotTSCANResults | 1.886 | 0.019 | 1.910 | |
plotTSNE | 0.408 | 0.005 | 0.414 | |
plotTopHVG | 0.345 | 0.006 | 0.353 | |
plotUMAP | 5.606 | 0.079 | 5.691 | |
readSingleCellMatrix | 0.003 | 0.000 | 0.003 | |
reportCellQC | 0.176 | 0.005 | 0.181 | |
reportDropletQC | 0.023 | 0.004 | 0.028 | |
reportQCTool | 0.164 | 0.006 | 0.171 | |
retrieveSCEIndex | 0.028 | 0.003 | 0.031 | |
runBBKNN | 0 | 0 | 0 | |
runBarcodeRankDrops | 0.391 | 0.005 | 0.396 | |
runBcds | 1.541 | 0.038 | 1.581 | |
runCellQC | 0.152 | 0.005 | 0.155 | |
runComBatSeq | 0.423 | 0.017 | 0.442 | |
runCxds | 0.606 | 0.039 | 0.650 | |
runCxdsBcdsHybrid | 1.673 | 0.033 | 1.711 | |
runDEAnalysis | 0.590 | 0.007 | 0.598 | |
runDecontX | 6.113 | 0.088 | 6.211 | |
runDimReduce | 0.431 | 0.006 | 0.439 | |
runDoubletFinder | 16.502 | 0.107 | 16.630 | |
runDropletQC | 0.021 | 0.004 | 0.024 | |
runEmptyDrops | 5.370 | 0.017 | 5.400 | |
runEnrichR | 0.226 | 0.020 | 1.787 | |
runFastMNN | 1.501 | 0.036 | 1.548 | |
runFeatureSelection | 0.210 | 0.002 | 0.213 | |
runFindMarker | 2.832 | 0.032 | 2.873 | |
runGSVA | 0.672 | 0.016 | 0.690 | |
runHarmony | 0.028 | 0.001 | 0.028 | |
runKMeans | 0.412 | 0.007 | 0.421 | |
runLimmaBC | 0.070 | 0.001 | 0.071 | |
runMNNCorrect | 0.481 | 0.004 | 0.487 | |
runModelGeneVar | 0.399 | 0.006 | 0.407 | |
runNormalization | 0.534 | 0.008 | 0.546 | |
runPerCellQC | 0.506 | 0.023 | 0.530 | |
runSCANORAMA | 0 | 0 | 0 | |
runSCMerge | 0.003 | 0.001 | 0.004 | |
runScDblFinder | 15.302 | 0.377 | 15.724 | |
runScranSNN | 0.654 | 0.009 | 0.665 | |
runScrublet | 0.020 | 0.002 | 0.022 | |
runSeuratFindClusters | 0.020 | 0.002 | 0.021 | |
runSeuratFindHVG | 0.539 | 0.113 | 0.655 | |
runSeuratHeatmap | 0.023 | 0.008 | 0.032 | |
runSeuratICA | 0.022 | 0.002 | 0.024 | |
runSeuratJackStraw | 0.021 | 0.002 | 0.024 | |
runSeuratNormalizeData | 0.028 | 0.004 | 0.033 | |
runSeuratPCA | 0.027 | 0.004 | 0.031 | |
runSeuratSCTransform | 3.404 | 0.098 | 3.513 | |
runSeuratScaleData | 0.030 | 0.003 | 0.033 | |
runSeuratUMAP | 0.030 | 0.007 | 0.037 | |
runSingleR | 0.035 | 0.002 | 0.037 | |
runSoupX | 0.174 | 0.004 | 0.178 | |
runTSCAN | 1.427 | 0.019 | 1.449 | |
runTSCANClusterDEAnalysis | 1.423 | 0.014 | 1.439 | |
runTSCANDEG | 1.473 | 0.017 | 1.493 | |
runTSNE | 0.806 | 0.012 | 0.819 | |
runUMAP | 5.651 | 0.077 | 5.727 | |
runVAM | 0.481 | 0.004 | 0.486 | |
runZINBWaVE | 0.003 | 0.000 | 0.004 | |
sampleSummaryStats | 0.282 | 0.006 | 0.289 | |
scaterCPM | 0.112 | 0.007 | 0.118 | |
scaterPCA | 0.405 | 0.008 | 0.413 | |
scaterlogNormCounts | 0.242 | 0.003 | 0.246 | |
sce | 0.023 | 0.006 | 0.028 | |
sctkListGeneSetCollections | 0.061 | 0.003 | 0.064 | |
sctkPythonInstallConda | 0 | 0 | 0 | |
sctkPythonInstallVirtualEnv | 0 | 0 | 0 | |
selectSCTKConda | 0.000 | 0.001 | 0.000 | |
selectSCTKVirtualEnvironment | 0 | 0 | 0 | |
setRowNames | 0.066 | 0.002 | 0.068 | |
setSCTKDisplayRow | 0.344 | 0.006 | 0.350 | |
singleCellTK | 0 | 0 | 0 | |
subDiffEx | 0.479 | 0.025 | 0.505 | |
subsetSCECols | 0.148 | 0.006 | 0.155 | |
subsetSCERows | 0.366 | 0.005 | 0.372 | |
summarizeSCE | 0.055 | 0.001 | 0.057 | |
trimCounts | 0.244 | 0.006 | 0.251 | |