Back to Multiple platform build/check report for BioC 3.18: simplified long |
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This page was generated on 2024-04-17 11:36:24 -0400 (Wed, 17 Apr 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
---|---|---|---|---|
nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.3.3 (2024-02-29) -- "Angel Food Cake" | 4676 |
palomino4 | Windows Server 2022 Datacenter | x64 | 4.3.3 (2024-02-29 ucrt) -- "Angel Food Cake" | 4414 |
merida1 | macOS 12.7.1 Monterey | x86_64 | 4.3.3 (2024-02-29) -- "Angel Food Cake" | 4437 |
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 1971/2266 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
singleCellTK 2.12.2 (landing page) Joshua David Campbell
| nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) / x86_64 | OK | OK | OK | |||||||||
palomino4 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | |||||||||
merida1 | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | |||||||||
kjohnson1 | macOS 13.6.1 Ventura / arm64 | see weekly results here | ||||||||||||
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: 2.12.2 |
Command: /home/biocbuild/bbs-3.18-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.18-bioc/R/site-library --timings singleCellTK_2.12.2.tar.gz |
StartedAt: 2024-04-16 04:01:27 -0400 (Tue, 16 Apr 2024) |
EndedAt: 2024-04-16 04:17:01 -0400 (Tue, 16 Apr 2024) |
EllapsedTime: 933.9 seconds |
RetCode: 0 |
Status: OK |
CheckDir: singleCellTK.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.18-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.18-bioc/R/site-library --timings singleCellTK_2.12.2.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/home/biocbuild/bbs-3.18-bioc/meat/singleCellTK.Rcheck’ * using R version 4.3.3 (2024-02-29) * using platform: x86_64-pc-linux-gnu (64-bit) * R was compiled by gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 GNU Fortran (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 * running under: Ubuntu 22.04.4 LTS * using session charset: UTF-8 * checking for file ‘singleCellTK/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘singleCellTK’ version ‘2.12.2’ * 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 6.9Mb sub-directories of 1Mb or more: extdata 1.6Mb shiny 3.0Mb * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking 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 loading without being on the library search path ... OK * checking 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 ... 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 plotDoubletFinderResults 34.606 0.456 35.059 runSeuratSCTransform 29.793 0.180 29.976 plotScDblFinderResults 28.735 0.644 29.377 runDoubletFinder 28.700 0.196 28.896 runScDblFinder 20.776 0.140 20.916 importExampleData 14.866 1.572 16.960 plotBatchCorrCompare 10.011 0.335 10.340 plotScdsHybridResults 9.242 0.108 8.463 plotBcdsResults 7.876 0.264 7.272 plotDecontXResults 7.494 0.168 7.661 plotEmptyDropsResults 6.654 0.012 6.666 plotEmptyDropsScatter 6.596 0.044 6.640 runDecontX 6.490 0.104 6.594 runUMAP 6.308 0.084 6.389 runEmptyDrops 6.331 0.000 6.331 plotCxdsResults 6.181 0.100 6.278 plotUMAP 5.996 0.028 6.021 detectCellOutlier 5.193 0.168 5.362 plotTSCANClusterDEG 5.216 0.032 5.248 getEnrichRResult 0.607 0.028 12.251 * 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 ... ‘singleCellTK.Rmd’ using ‘UTF-8’... OK OK * checking re-building of vignette outputs ... OK * checking PDF version of manual ... OK * DONE Status: 1 NOTE See ‘/home/biocbuild/bbs-3.18-bioc/meat/singleCellTK.Rcheck/00check.log’ for details.
singleCellTK.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.18-bioc/R/bin/R CMD INSTALL singleCellTK ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/bbs-3.18-bioc/R/site-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 4.3.3 (2024-02-29) -- "Angel Food Cake" Copyright (C) 2024 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 0.138 0.042 0.169
singleCellTK.Rcheck/tests/testthat.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake" Copyright (C) 2024 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, 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 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: 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 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% [----|----|----|----|----|----|----|----|----|----| **************************************************| 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: 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% | | | 0% | |======================================================================| 100% | | | 0% | |======================================================================| 100% | | | 0% | |======================================================================| 100% Performing log-normalization 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| [ FAIL 0 | WARN 22 | SKIP 0 | PASS 223 ] [ FAIL 0 | WARN 22 | SKIP 0 | PASS 223 ] > > proc.time() user system elapsed 274.741 8.153 286.919
singleCellTK.Rcheck/singleCellTK-Ex.timings
name | user | system | elapsed | |
MitoGenes | 0.002 | 0.000 | 0.002 | |
SEG | 0.002 | 0.000 | 0.002 | |
calcEffectSizes | 0.251 | 0.000 | 0.251 | |
combineSCE | 1.899 | 0.055 | 1.955 | |
computeZScore | 0.249 | 0.013 | 0.261 | |
convertSCEToSeurat | 3.946 | 0.119 | 4.067 | |
convertSeuratToSCE | 0.464 | 0.008 | 0.472 | |
dedupRowNames | 0.052 | 0.000 | 0.052 | |
detectCellOutlier | 5.193 | 0.168 | 5.362 | |
diffAbundanceFET | 0.056 | 0.000 | 0.056 | |
discreteColorPalette | 0.005 | 0.000 | 0.006 | |
distinctColors | 0.002 | 0.000 | 0.002 | |
downSampleCells | 0.651 | 0.052 | 0.702 | |
downSampleDepth | 0.514 | 0.004 | 0.518 | |
expData-ANY-character-method | 0.280 | 0.000 | 0.281 | |
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method | 0.315 | 0.000 | 0.315 | |
expData-set | 0.313 | 0.012 | 0.324 | |
expData | 0.320 | 0.016 | 0.336 | |
expDataNames-ANY-method | 0.266 | 0.004 | 0.271 | |
expDataNames | 0.273 | 0.000 | 0.274 | |
expDeleteDataTag | 0.034 | 0.000 | 0.034 | |
expSetDataTag | 0.024 | 0.000 | 0.025 | |
expTaggedData | 0.026 | 0.000 | 0.026 | |
exportSCE | 0.023 | 0.000 | 0.022 | |
exportSCEtoAnnData | 0.098 | 0.000 | 0.097 | |
exportSCEtoFlatFile | 0.097 | 0.004 | 0.101 | |
featureIndex | 0.034 | 0.004 | 0.039 | |
generateSimulatedData | 0.051 | 0.000 | 0.052 | |
getBiomarker | 0.052 | 0.004 | 0.056 | |
getDEGTopTable | 0.807 | 0.012 | 0.820 | |
getDiffAbundanceResults | 0.044 | 0.004 | 0.049 | |
getEnrichRResult | 0.607 | 0.028 | 12.251 | |
getFindMarkerTopTable | 3.400 | 0.052 | 3.452 | |
getMSigDBTable | 0.003 | 0.000 | 0.003 | |
getPathwayResultNames | 0.023 | 0.000 | 0.022 | |
getSampleSummaryStatsTable | 0.300 | 0.012 | 0.311 | |
getSoupX | 0 | 0 | 0 | |
getTSCANResults | 1.73 | 0.04 | 1.77 | |
getTopHVG | 0.999 | 0.020 | 1.019 | |
importAnnData | 0.002 | 0.000 | 0.001 | |
importBUStools | 0.258 | 0.000 | 0.257 | |
importCellRanger | 1.087 | 0.028 | 1.115 | |
importCellRangerV2Sample | 0.237 | 0.000 | 0.237 | |
importCellRangerV3Sample | 0.370 | 0.000 | 0.371 | |
importDropEst | 0.291 | 0.000 | 0.291 | |
importExampleData | 14.866 | 1.572 | 16.960 | |
importGeneSetsFromCollection | 0.685 | 0.076 | 0.761 | |
importGeneSetsFromGMT | 0.062 | 0.004 | 0.066 | |
importGeneSetsFromList | 0.116 | 0.000 | 0.117 | |
importGeneSetsFromMSigDB | 3.468 | 0.264 | 3.732 | |
importMitoGeneSet | 0.048 | 0.004 | 0.052 | |
importOptimus | 0.001 | 0.000 | 0.002 | |
importSEQC | 0.229 | 0.020 | 0.249 | |
importSTARsolo | 0.250 | 0.016 | 0.266 | |
iterateSimulations | 0.375 | 0.036 | 0.412 | |
listSampleSummaryStatsTables | 0.376 | 0.012 | 0.388 | |
mergeSCEColData | 0.444 | 0.020 | 0.465 | |
mouseBrainSubsetSCE | 0.037 | 0.000 | 0.037 | |
msigdb_table | 0.001 | 0.000 | 0.001 | |
plotBarcodeRankDropsResults | 0.79 | 0.06 | 0.85 | |
plotBarcodeRankScatter | 0.803 | 0.028 | 0.832 | |
plotBatchCorrCompare | 10.011 | 0.335 | 10.340 | |
plotBatchVariance | 0.318 | 0.008 | 0.326 | |
plotBcdsResults | 7.876 | 0.264 | 7.272 | |
plotBubble | 1.058 | 0.004 | 1.062 | |
plotClusterAbundance | 0.861 | 0.000 | 0.861 | |
plotCxdsResults | 6.181 | 0.100 | 6.278 | |
plotDEGHeatmap | 2.798 | 0.076 | 2.875 | |
plotDEGRegression | 3.538 | 0.084 | 3.617 | |
plotDEGViolin | 4.247 | 0.104 | 4.344 | |
plotDEGVolcano | 0.997 | 0.016 | 1.013 | |
plotDecontXResults | 7.494 | 0.168 | 7.661 | |
plotDimRed | 0.27 | 0.00 | 0.27 | |
plotDoubletFinderResults | 34.606 | 0.456 | 35.059 | |
plotEmptyDropsResults | 6.654 | 0.012 | 6.666 | |
plotEmptyDropsScatter | 6.596 | 0.044 | 6.640 | |
plotFindMarkerHeatmap | 4.120 | 0.044 | 4.165 | |
plotMASTThresholdGenes | 1.502 | 0.032 | 1.533 | |
plotPCA | 0.456 | 0.000 | 0.456 | |
plotPathway | 0.792 | 0.004 | 0.797 | |
plotRunPerCellQCResults | 2.155 | 0.020 | 2.176 | |
plotSCEBarAssayData | 0.176 | 0.003 | 0.180 | |
plotSCEBarColData | 0.141 | 0.000 | 0.141 | |
plotSCEBatchFeatureMean | 0.221 | 0.000 | 0.221 | |
plotSCEDensity | 0.249 | 0.004 | 0.253 | |
plotSCEDensityAssayData | 0.162 | 0.000 | 0.162 | |
plotSCEDensityColData | 0.204 | 0.000 | 0.204 | |
plotSCEDimReduceColData | 0.72 | 0.00 | 0.72 | |
plotSCEDimReduceFeatures | 0.394 | 0.000 | 0.394 | |
plotSCEHeatmap | 0.613 | 0.011 | 0.625 | |
plotSCEScatter | 0.396 | 0.004 | 0.400 | |
plotSCEViolin | 0.235 | 0.008 | 0.243 | |
plotSCEViolinAssayData | 0.241 | 0.004 | 0.245 | |
plotSCEViolinColData | 0.224 | 0.008 | 0.232 | |
plotScDblFinderResults | 28.735 | 0.644 | 29.377 | |
plotScanpyDotPlot | 0.021 | 0.004 | 0.025 | |
plotScanpyEmbedding | 0.024 | 0.000 | 0.024 | |
plotScanpyHVG | 0.023 | 0.000 | 0.023 | |
plotScanpyHeatmap | 0.023 | 0.000 | 0.023 | |
plotScanpyMarkerGenes | 0.024 | 0.000 | 0.024 | |
plotScanpyMarkerGenesDotPlot | 0.019 | 0.004 | 0.024 | |
plotScanpyMarkerGenesHeatmap | 0.025 | 0.000 | 0.024 | |
plotScanpyMarkerGenesMatrixPlot | 0.024 | 0.000 | 0.023 | |
plotScanpyMarkerGenesViolin | 0.024 | 0.000 | 0.024 | |
plotScanpyMatrixPlot | 0.024 | 0.000 | 0.023 | |
plotScanpyPCA | 0.023 | 0.000 | 0.024 | |
plotScanpyPCAGeneRanking | 0.025 | 0.000 | 0.025 | |
plotScanpyPCAVariance | 0.026 | 0.000 | 0.026 | |
plotScanpyViolin | 0.026 | 0.000 | 0.026 | |
plotScdsHybridResults | 9.242 | 0.108 | 8.463 | |
plotScrubletResults | 0.023 | 0.000 | 0.023 | |
plotSeuratElbow | 0.023 | 0.000 | 0.023 | |
plotSeuratHVG | 0.024 | 0.000 | 0.024 | |
plotSeuratJackStraw | 0.024 | 0.000 | 0.024 | |
plotSeuratReduction | 0.024 | 0.000 | 0.024 | |
plotSoupXResults | 0 | 0 | 0 | |
plotTSCANClusterDEG | 5.216 | 0.032 | 5.248 | |
plotTSCANClusterPseudo | 2.334 | 0.028 | 2.362 | |
plotTSCANDimReduceFeatures | 2.185 | 0.008 | 2.193 | |
plotTSCANPseudotimeGenes | 2.080 | 0.008 | 2.087 | |
plotTSCANPseudotimeHeatmap | 2.197 | 0.036 | 2.232 | |
plotTSCANResults | 2.108 | 0.004 | 2.113 | |
plotTSNE | 0.472 | 0.000 | 0.472 | |
plotTopHVG | 0.390 | 0.000 | 0.389 | |
plotUMAP | 5.996 | 0.028 | 6.021 | |
readSingleCellMatrix | 0.005 | 0.000 | 0.004 | |
reportCellQC | 0.163 | 0.000 | 0.163 | |
reportDropletQC | 0.023 | 0.000 | 0.023 | |
reportQCTool | 0.166 | 0.000 | 0.166 | |
retrieveSCEIndex | 0.029 | 0.000 | 0.029 | |
runBBKNN | 0 | 0 | 0 | |
runBarcodeRankDrops | 0.391 | 0.000 | 0.391 | |
runBcds | 2.262 | 0.028 | 1.423 | |
runCellQC | 0.167 | 0.004 | 0.170 | |
runClusterSummaryMetrics | 0.696 | 0.004 | 0.701 | |
runComBatSeq | 0.428 | 0.000 | 0.428 | |
runCxds | 0.438 | 0.008 | 0.446 | |
runCxdsBcdsHybrid | 2.321 | 0.004 | 1.447 | |
runDEAnalysis | 0.665 | 0.000 | 0.664 | |
runDecontX | 6.490 | 0.104 | 6.594 | |
runDimReduce | 0.423 | 0.000 | 0.423 | |
runDoubletFinder | 28.700 | 0.196 | 28.896 | |
runDropletQC | 0.024 | 0.000 | 0.023 | |
runEmptyDrops | 6.331 | 0.000 | 6.331 | |
runEnrichR | 0.551 | 0.017 | 2.376 | |
runFastMNN | 1.665 | 0.056 | 1.721 | |
runFeatureSelection | 0.219 | 0.000 | 0.218 | |
runFindMarker | 3.303 | 0.012 | 3.315 | |
runGSVA | 0.767 | 0.008 | 0.775 | |
runHarmony | 0.034 | 0.000 | 0.034 | |
runKMeans | 0.407 | 0.000 | 0.407 | |
runLimmaBC | 0.073 | 0.000 | 0.072 | |
runMNNCorrect | 0.497 | 0.000 | 0.497 | |
runModelGeneVar | 0.443 | 0.008 | 0.452 | |
runNormalization | 2.340 | 0.016 | 2.357 | |
runPerCellQC | 0.502 | 0.000 | 0.502 | |
runSCANORAMA | 0 | 0 | 0 | |
runSCMerge | 0.004 | 0.000 | 0.004 | |
runScDblFinder | 20.776 | 0.140 | 20.916 | |
runScanpyFindClusters | 0.020 | 0.004 | 0.024 | |
runScanpyFindHVG | 0.023 | 0.000 | 0.023 | |
runScanpyFindMarkers | 0.023 | 0.000 | 0.023 | |
runScanpyNormalizeData | 0.183 | 0.008 | 0.191 | |
runScanpyPCA | 0.024 | 0.000 | 0.024 | |
runScanpyScaleData | 0.023 | 0.000 | 0.024 | |
runScanpyTSNE | 0.024 | 0.000 | 0.024 | |
runScanpyUMAP | 0.024 | 0.000 | 0.024 | |
runScranSNN | 0.731 | 0.024 | 0.755 | |
runScrublet | 0.024 | 0.000 | 0.024 | |
runSeuratFindClusters | 0.025 | 0.000 | 0.025 | |
runSeuratFindHVG | 2.294 | 0.160 | 2.454 | |
runSeuratHeatmap | 0.024 | 0.000 | 0.024 | |
runSeuratICA | 0.023 | 0.000 | 0.023 | |
runSeuratJackStraw | 0.024 | 0.000 | 0.024 | |
runSeuratNormalizeData | 0.024 | 0.000 | 0.024 | |
runSeuratPCA | 0.023 | 0.000 | 0.023 | |
runSeuratSCTransform | 29.793 | 0.180 | 29.976 | |
runSeuratScaleData | 0.020 | 0.004 | 0.024 | |
runSeuratUMAP | 0.023 | 0.000 | 0.023 | |
runSingleR | 0.034 | 0.000 | 0.034 | |
runSoupX | 0 | 0 | 0 | |
runTSCAN | 1.385 | 0.000 | 1.384 | |
runTSCANClusterDEAnalysis | 1.527 | 0.004 | 1.531 | |
runTSCANDEG | 1.497 | 0.020 | 1.517 | |
runTSNE | 0.884 | 0.000 | 0.884 | |
runUMAP | 6.308 | 0.084 | 6.389 | |
runVAM | 0.504 | 0.000 | 0.504 | |
runZINBWaVE | 0.004 | 0.000 | 0.004 | |
sampleSummaryStats | 0.269 | 0.000 | 0.269 | |
scaterCPM | 0.120 | 0.016 | 0.136 | |
scaterPCA | 0.406 | 0.008 | 0.414 | |
scaterlogNormCounts | 0.244 | 0.004 | 0.247 | |
sce | 0.023 | 0.000 | 0.023 | |
sctkListGeneSetCollections | 0.072 | 0.000 | 0.072 | |
sctkPythonInstallConda | 0.001 | 0.000 | 0.001 | |
sctkPythonInstallVirtualEnv | 0 | 0 | 0 | |
selectSCTKConda | 0.001 | 0.000 | 0.001 | |
selectSCTKVirtualEnvironment | 0 | 0 | 0 | |
setRowNames | 0.081 | 0.000 | 0.081 | |
setSCTKDisplayRow | 0.392 | 0.000 | 0.392 | |
singleCellTK | 0 | 0 | 0 | |
subDiffEx | 0.508 | 0.004 | 0.512 | |
subsetSCECols | 0.163 | 0.000 | 0.163 | |
subsetSCERows | 0.379 | 0.000 | 0.379 | |
summarizeSCE | 0.064 | 0.000 | 0.064 | |
trimCounts | 0.206 | 0.000 | 0.206 | |