Back to Multiple platform build/check report for BioC 3.20: simplified long |
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This page was generated on 2024-06-11 15:42 -0400 (Tue, 11 Jun 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
---|---|---|---|---|
nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.4.0 RC (2024-04-16 r86468) -- "Puppy Cup" | 4679 |
palomino4 | Windows Server 2022 Datacenter | x64 | 4.4.0 RC (2024-04-16 r86468 ucrt) -- "Puppy Cup" | 4414 |
merida1 | macOS 12.7.4 Monterey | x86_64 | 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" | 4441 |
kjohnson1 | macOS 13.6.6 Ventura | arm64 | 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" | 4394 |
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 1937/2239 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
singleCellTK 2.15.0 (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 12.7.4 Monterey / x86_64 | OK | OK | OK | OK | |||||||||
kjohnson1 | macOS 13.6.6 Ventura / arm64 | OK | 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: 2.15.0 |
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_2.15.0.tar.gz |
StartedAt: 2024-06-10 10:11:17 -0400 (Mon, 10 Jun 2024) |
EndedAt: 2024-06-10 10:42:46 -0400 (Mon, 10 Jun 2024) |
EllapsedTime: 1889.5 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_2.15.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/singleCellTK.Rcheck’ * using R version 4.4.0 Patched (2024-04-24 r86482) * using platform: x86_64-apple-darwin20 * R was compiled by Apple clang version 14.0.0 (clang-1400.0.29.202) GNU Fortran (GCC) 12.2.0 * running under: macOS Monterey 12.7.4 * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘singleCellTK/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘singleCellTK’ version ‘2.15.0’ * 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 6.8Mb 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 ... NOTE License stub is invalid DCF. * 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 ... NOTE checkRd: (-1) dedupRowNames.Rd:10: Lost braces 10 | \item{x}{A matrix like or /linkS4class{SingleCellExperiment} object, on which | ^ checkRd: (-1) dedupRowNames.Rd:14: Lost braces 14 | /linkS4class{SingleCellExperiment} object. When set to \code{TRUE}, will | ^ checkRd: (-1) dedupRowNames.Rd:22: Lost braces 22 | By default, a matrix or /linkS4class{SingleCellExperiment} object | ^ checkRd: (-1) dedupRowNames.Rd:24: Lost braces 24 | When \code{x} is a /linkS4class{SingleCellExperiment} and \code{as.rowData} | ^ checkRd: (-1) plotBubble.Rd:42: Lost braces 42 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.} | ^ checkRd: (-1) runClusterSummaryMetrics.Rd:27: Lost braces 27 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.} | ^ checkRd: (-1) runEmptyDrops.Rd:66: Lost braces 66 | provided \\linkS4class{SingleCellExperiment} object. | ^ checkRd: (-1) runSCMerge.Rd:44: Lost braces 44 | construct pseudo-replicates. The length of code{kmeansK} needs to be the same | ^ * 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 50.245 1.124 58.454 plotDoubletFinderResults 47.457 0.308 50.881 runDoubletFinder 41.867 0.373 47.182 runScDblFinder 35.162 0.557 40.229 importExampleData 29.207 2.800 35.629 plotBatchCorrCompare 14.700 0.254 15.531 plotScdsHybridResults 13.824 0.174 15.707 plotTSCANClusterDEG 13.210 0.179 14.901 plotBcdsResults 12.214 0.454 14.577 plotDecontXResults 12.020 0.157 12.816 plotFindMarkerHeatmap 11.937 0.079 12.918 plotDEGViolin 11.037 0.203 12.465 plotEmptyDropsResults 10.569 0.077 11.354 plotEmptyDropsScatter 10.470 0.050 11.015 runEmptyDrops 9.897 0.074 11.286 runDecontX 9.589 0.145 10.643 detectCellOutlier 9.504 0.180 10.523 runSeuratSCTransform 9.379 0.133 10.837 convertSCEToSeurat 9.168 0.315 10.096 plotDEGRegression 9.233 0.150 11.207 plotCxdsResults 8.950 0.157 9.898 runFindMarker 8.530 0.107 9.815 getFindMarkerTopTable 8.488 0.118 9.491 plotUMAP 8.453 0.106 9.250 runUMAP 8.334 0.070 9.440 plotDEGHeatmap 7.311 0.161 8.392 plotTSCANDimReduceFeatures 5.774 0.058 6.639 plotTSCANPseudotimeHeatmap 5.718 0.060 6.567 plotTSCANClusterPseudo 5.670 0.045 6.442 plotTSCANPseudotimeGenes 5.391 0.049 6.171 plotRunPerCellQCResults 5.371 0.046 5.756 plotTSCANResults 5.334 0.049 6.096 importGeneSetsFromMSigDB 4.934 0.181 5.569 * 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 ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 3 NOTEs See ‘/Users/biocbuild/bbs-3.20-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/4.4-x86_64/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 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-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) NULL > > proc.time() user system elapsed 0.363 0.115 0.482
singleCellTK.Rcheck/tests/testthat.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-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 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, 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% [----|----|----|----|----|----|----|----|----|----| **************************************************| No annotation package name available in the input data object. Attempting to directly match identifiers in data to gene sets. 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% No annotation package name available in the input data object. Attempting to directly match identifiers in data to gene sets. 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 21 | SKIP 0 | PASS 224 ] [ FAIL 0 | WARN 21 | SKIP 0 | PASS 224 ] > > proc.time() user system elapsed 485.305 10.055 546.774
singleCellTK.Rcheck/singleCellTK-Ex.timings
name | user | system | elapsed | |
MitoGenes | 0.005 | 0.005 | 0.010 | |
SEG | 0.004 | 0.005 | 0.009 | |
calcEffectSizes | 0.500 | 0.057 | 0.602 | |
combineSCE | 3.476 | 0.140 | 4.162 | |
computeZScore | 0.464 | 0.020 | 0.556 | |
convertSCEToSeurat | 9.168 | 0.315 | 10.096 | |
convertSeuratToSCE | 1.199 | 0.018 | 1.317 | |
dedupRowNames | 0.124 | 0.007 | 0.139 | |
detectCellOutlier | 9.504 | 0.180 | 10.523 | |
diffAbundanceFET | 0.103 | 0.007 | 0.123 | |
discreteColorPalette | 0.011 | 0.001 | 0.015 | |
distinctColors | 0.004 | 0.000 | 0.004 | |
downSampleCells | 1.435 | 0.170 | 1.746 | |
downSampleDepth | 1.247 | 0.063 | 1.429 | |
expData-ANY-character-method | 0.733 | 0.009 | 0.802 | |
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method | 0.837 | 0.010 | 0.929 | |
expData-set | 0.847 | 0.024 | 0.977 | |
expData | 0.802 | 0.072 | 0.984 | |
expDataNames-ANY-method | 0.807 | 0.077 | 0.949 | |
expDataNames | 0.709 | 0.010 | 0.774 | |
expDeleteDataTag | 0.064 | 0.003 | 0.075 | |
expSetDataTag | 0.044 | 0.003 | 0.050 | |
expTaggedData | 0.051 | 0.002 | 0.058 | |
exportSCE | 0.042 | 0.007 | 0.051 | |
exportSCEtoAnnData | 0.139 | 0.004 | 0.157 | |
exportSCEtoFlatFile | 0.140 | 0.004 | 0.155 | |
featureIndex | 0.079 | 0.006 | 0.089 | |
generateSimulatedData | 0.095 | 0.008 | 0.110 | |
getBiomarker | 0.115 | 0.008 | 0.136 | |
getDEGTopTable | 2.063 | 0.056 | 2.391 | |
getDiffAbundanceResults | 0.097 | 0.005 | 0.143 | |
getEnrichRResult | 0.733 | 0.058 | 2.416 | |
getFindMarkerTopTable | 8.488 | 0.118 | 9.491 | |
getMSigDBTable | 0.008 | 0.006 | 0.018 | |
getPathwayResultNames | 0.047 | 0.008 | 0.060 | |
getSampleSummaryStatsTable | 0.753 | 0.009 | 0.841 | |
getSoupX | 0.000 | 0.001 | 0.000 | |
getTSCANResults | 4.270 | 0.064 | 4.704 | |
getTopHVG | 2.654 | 0.026 | 2.898 | |
importAnnData | 0.002 | 0.001 | 0.003 | |
importBUStools | 0.654 | 0.007 | 0.724 | |
importCellRanger | 2.756 | 0.065 | 3.135 | |
importCellRangerV2Sample | 0.650 | 0.006 | 0.729 | |
importCellRangerV3Sample | 0.982 | 0.026 | 1.099 | |
importDropEst | 0.749 | 0.008 | 0.817 | |
importExampleData | 29.207 | 2.800 | 35.629 | |
importGeneSetsFromCollection | 1.734 | 0.148 | 2.086 | |
importGeneSetsFromGMT | 0.146 | 0.012 | 0.179 | |
importGeneSetsFromList | 0.305 | 0.011 | 0.343 | |
importGeneSetsFromMSigDB | 4.934 | 0.181 | 5.569 | |
importMitoGeneSet | 0.119 | 0.018 | 0.154 | |
importOptimus | 0.003 | 0.001 | 0.006 | |
importSEQC | 0.695 | 0.040 | 0.804 | |
importSTARsolo | 0.674 | 0.015 | 0.764 | |
iterateSimulations | 0.838 | 0.020 | 0.953 | |
listSampleSummaryStatsTables | 1.006 | 0.014 | 1.105 | |
mergeSCEColData | 1.138 | 0.032 | 1.266 | |
mouseBrainSubsetSCE | 0.072 | 0.006 | 0.086 | |
msigdb_table | 0.003 | 0.005 | 0.008 | |
plotBarcodeRankDropsResults | 2.024 | 0.028 | 2.235 | |
plotBarcodeRankScatter | 2.169 | 0.021 | 2.392 | |
plotBatchCorrCompare | 14.700 | 0.254 | 15.531 | |
plotBatchVariance | 0.782 | 0.071 | 1.011 | |
plotBcdsResults | 12.214 | 0.454 | 14.577 | |
plotBubble | 2.183 | 0.088 | 2.586 | |
plotClusterAbundance | 2.007 | 0.022 | 2.142 | |
plotCxdsResults | 8.950 | 0.157 | 9.898 | |
plotDEGHeatmap | 7.311 | 0.161 | 8.392 | |
plotDEGRegression | 9.233 | 0.150 | 11.207 | |
plotDEGViolin | 11.037 | 0.203 | 12.465 | |
plotDEGVolcano | 2.340 | 0.027 | 2.471 | |
plotDecontXResults | 12.020 | 0.157 | 12.816 | |
plotDimRed | 0.667 | 0.011 | 0.748 | |
plotDoubletFinderResults | 47.457 | 0.308 | 50.881 | |
plotEmptyDropsResults | 10.569 | 0.077 | 11.354 | |
plotEmptyDropsScatter | 10.470 | 0.050 | 11.015 | |
plotFindMarkerHeatmap | 11.937 | 0.079 | 12.918 | |
plotMASTThresholdGenes | 4.038 | 0.053 | 4.289 | |
plotPCA | 1.193 | 0.020 | 1.280 | |
plotPathway | 2.078 | 0.027 | 2.207 | |
plotRunPerCellQCResults | 5.371 | 0.046 | 5.756 | |
plotSCEBarAssayData | 0.439 | 0.013 | 0.541 | |
plotSCEBarColData | 0.345 | 0.010 | 0.393 | |
plotSCEBatchFeatureMean | 0.553 | 0.008 | 0.668 | |
plotSCEDensity | 0.573 | 0.014 | 0.616 | |
plotSCEDensityAssayData | 0.389 | 0.011 | 0.426 | |
plotSCEDensityColData | 0.502 | 0.013 | 0.523 | |
plotSCEDimReduceColData | 1.748 | 0.019 | 1.797 | |
plotSCEDimReduceFeatures | 0.952 | 0.018 | 0.990 | |
plotSCEHeatmap | 1.632 | 0.020 | 1.771 | |
plotSCEScatter | 0.855 | 0.016 | 0.942 | |
plotSCEViolin | 0.591 | 0.013 | 0.659 | |
plotSCEViolinAssayData | 0.676 | 0.016 | 0.756 | |
plotSCEViolinColData | 0.575 | 0.014 | 0.640 | |
plotScDblFinderResults | 50.245 | 1.124 | 58.454 | |
plotScanpyDotPlot | 0.044 | 0.008 | 0.057 | |
plotScanpyEmbedding | 0.050 | 0.006 | 0.062 | |
plotScanpyHVG | 0.049 | 0.004 | 0.061 | |
plotScanpyHeatmap | 0.044 | 0.007 | 0.061 | |
plotScanpyMarkerGenes | 0.040 | 0.009 | 0.101 | |
plotScanpyMarkerGenesDotPlot | 0.040 | 0.005 | 0.061 | |
plotScanpyMarkerGenesHeatmap | 0.042 | 0.006 | 0.054 | |
plotScanpyMarkerGenesMatrixPlot | 0.042 | 0.004 | 0.053 | |
plotScanpyMarkerGenesViolin | 0.040 | 0.004 | 0.046 | |
plotScanpyMatrixPlot | 0.041 | 0.005 | 0.049 | |
plotScanpyPCA | 0.041 | 0.005 | 0.048 | |
plotScanpyPCAGeneRanking | 0.040 | 0.005 | 0.048 | |
plotScanpyPCAVariance | 0.039 | 0.004 | 0.049 | |
plotScanpyViolin | 0.041 | 0.007 | 0.057 | |
plotScdsHybridResults | 13.824 | 0.174 | 15.707 | |
plotScrubletResults | 0.047 | 0.005 | 0.058 | |
plotSeuratElbow | 0.044 | 0.005 | 0.056 | |
plotSeuratHVG | 0.045 | 0.007 | 0.062 | |
plotSeuratJackStraw | 0.049 | 0.006 | 0.065 | |
plotSeuratReduction | 0.043 | 0.005 | 0.058 | |
plotSoupXResults | 0.000 | 0.001 | 0.001 | |
plotTSCANClusterDEG | 13.210 | 0.179 | 14.901 | |
plotTSCANClusterPseudo | 5.670 | 0.045 | 6.442 | |
plotTSCANDimReduceFeatures | 5.774 | 0.058 | 6.639 | |
plotTSCANPseudotimeGenes | 5.391 | 0.049 | 6.171 | |
plotTSCANPseudotimeHeatmap | 5.718 | 0.060 | 6.567 | |
plotTSCANResults | 5.334 | 0.049 | 6.096 | |
plotTSNE | 1.247 | 0.018 | 1.458 | |
plotTopHVG | 1.187 | 0.024 | 1.338 | |
plotUMAP | 8.453 | 0.106 | 9.250 | |
readSingleCellMatrix | 0.010 | 0.002 | 0.011 | |
reportCellQC | 0.422 | 0.010 | 0.659 | |
reportDropletQC | 0.047 | 0.007 | 0.064 | |
reportQCTool | 0.419 | 0.010 | 0.454 | |
retrieveSCEIndex | 0.054 | 0.005 | 0.064 | |
runBBKNN | 0.000 | 0.001 | 0.002 | |
runBarcodeRankDrops | 0.964 | 0.018 | 1.012 | |
runBcds | 3.876 | 0.105 | 4.273 | |
runCellQC | 0.429 | 0.020 | 0.478 | |
runClusterSummaryMetrics | 1.781 | 0.073 | 1.925 | |
runComBatSeq | 1.038 | 0.035 | 1.697 | |
runCxds | 1.087 | 0.022 | 1.152 | |
runCxdsBcdsHybrid | 3.857 | 0.097 | 4.093 | |
runDEAnalysis | 1.727 | 0.056 | 1.861 | |
runDecontX | 9.589 | 0.145 | 10.643 | |
runDimReduce | 1.107 | 0.022 | 1.254 | |
runDoubletFinder | 41.867 | 0.373 | 47.182 | |
runDropletQC | 0.046 | 0.008 | 0.063 | |
runEmptyDrops | 9.897 | 0.074 | 11.286 | |
runEnrichR | 0.678 | 0.042 | 2.049 | |
runFastMNN | 4.260 | 0.073 | 4.864 | |
runFeatureSelection | 0.481 | 0.014 | 0.563 | |
runFindMarker | 8.530 | 0.107 | 9.815 | |
runGSVA | 2.070 | 0.059 | 2.391 | |
runHarmony | 0.086 | 0.002 | 0.100 | |
runKMeans | 1.072 | 0.021 | 1.255 | |
runLimmaBC | 0.194 | 0.004 | 0.233 | |
runMNNCorrect | 1.343 | 0.018 | 1.550 | |
runModelGeneVar | 1.130 | 0.015 | 1.318 | |
runNormalization | 3.276 | 0.041 | 3.731 | |
runPerCellQC | 1.214 | 0.019 | 1.387 | |
runSCANORAMA | 0 | 0 | 0 | |
runSCMerge | 0.007 | 0.002 | 0.012 | |
runScDblFinder | 35.162 | 0.557 | 40.229 | |
runScanpyFindClusters | 0.045 | 0.006 | 0.062 | |
runScanpyFindHVG | 0.043 | 0.005 | 0.054 | |
runScanpyFindMarkers | 0.039 | 0.004 | 0.048 | |
runScanpyNormalizeData | 0.463 | 0.007 | 0.525 | |
runScanpyPCA | 0.044 | 0.004 | 0.053 | |
runScanpyScaleData | 0.041 | 0.006 | 0.052 | |
runScanpyTSNE | 0.045 | 0.006 | 0.062 | |
runScanpyUMAP | 0.047 | 0.005 | 0.057 | |
runScranSNN | 1.817 | 0.025 | 2.057 | |
runScrublet | 0.042 | 0.003 | 0.047 | |
runSeuratFindClusters | 0.049 | 0.004 | 0.058 | |
runSeuratFindHVG | 1.912 | 0.103 | 2.321 | |
runSeuratHeatmap | 0.044 | 0.004 | 0.054 | |
runSeuratICA | 0.039 | 0.004 | 0.051 | |
runSeuratJackStraw | 0.040 | 0.004 | 0.049 | |
runSeuratNormalizeData | 0.045 | 0.008 | 0.061 | |
runSeuratPCA | 0.047 | 0.006 | 0.058 | |
runSeuratSCTransform | 9.379 | 0.133 | 10.837 | |
runSeuratScaleData | 0.044 | 0.008 | 0.058 | |
runSeuratUMAP | 0.044 | 0.006 | 0.054 | |
runSingleR | 0.090 | 0.006 | 0.124 | |
runSoupX | 0.000 | 0.000 | 0.001 | |
runTSCAN | 3.601 | 0.051 | 4.099 | |
runTSCANClusterDEAnalysis | 3.873 | 0.040 | 4.462 | |
runTSCANDEG | 3.685 | 0.035 | 4.187 | |
runTSNE | 1.715 | 0.022 | 1.967 | |
runUMAP | 8.334 | 0.070 | 9.440 | |
runVAM | 1.315 | 0.014 | 1.515 | |
runZINBWaVE | 0.007 | 0.001 | 0.009 | |
sampleSummaryStats | 0.726 | 0.012 | 0.826 | |
scaterCPM | 0.236 | 0.004 | 0.267 | |
scaterPCA | 1.569 | 0.019 | 1.798 | |
scaterlogNormCounts | 0.499 | 0.010 | 0.583 | |
sce | 0.044 | 0.009 | 0.059 | |
sctkListGeneSetCollections | 0.176 | 0.010 | 0.208 | |
sctkPythonInstallConda | 0.000 | 0.001 | 0.000 | |
sctkPythonInstallVirtualEnv | 0 | 0 | 0 | |
selectSCTKConda | 0.001 | 0.001 | 0.001 | |
selectSCTKVirtualEnvironment | 0 | 0 | 0 | |
setRowNames | 0.306 | 0.017 | 0.371 | |
setSCTKDisplayRow | 0.920 | 0.016 | 1.056 | |
singleCellTK | 0.000 | 0.001 | 0.001 | |
subDiffEx | 1.110 | 0.034 | 1.286 | |
subsetSCECols | 0.417 | 0.012 | 0.475 | |
subsetSCERows | 0.962 | 0.018 | 1.101 | |
summarizeSCE | 0.137 | 0.007 | 0.156 | |
trimCounts | 0.363 | 0.013 | 0.424 | |