Back to Multiple platform build/check report for BioC 3.18:   simplified   long
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This page was generated on 2024-03-29 11:38:16 -0400 (Fri, 29 Mar 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.3.3 (2024-02-29) -- "Angel Food Cake" 4669
palomino4Windows Server 2022 Datacenterx644.3.3 (2024-02-29 ucrt) -- "Angel Food Cake" 4404
merida1macOS 12.7.1 Montereyx86_644.3.3 (2024-02-29) -- "Angel Food Cake" 4427
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 1971/2266HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.12.2  (landing page)
Joshua David Campbell
Snapshot Date: 2024-03-27 14:05:05 -0400 (Wed, 27 Mar 2024)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: RELEASE_3_18
git_last_commit: 14c92130
git_last_commit_date: 2024-02-05 14:45:10 -0400 (Mon, 05 Feb 2024)
nebbiolo2Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino4Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
merida1macOS 12.7.1 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson1macOS 13.6.1 Ventura / arm64see weekly results here

CHECK results for singleCellTK on merida1


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.

raw results

Summary

Package: singleCellTK
Version: 2.12.2
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.12.2.tar.gz
StartedAt: 2024-03-28 09:02:30 -0400 (Thu, 28 Mar 2024)
EndedAt: 2024-03-28 09:31:42 -0400 (Thu, 28 Mar 2024)
EllapsedTime: 1751.4 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### 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_2.12.2.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.18-bioc/meat/singleCellTK.Rcheck’
* using R version 4.3.3 (2024-02-29)
* using platform: x86_64-apple-darwin20 (64-bit)
* 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 12.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.12.2’
* 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  6.8Mb
  sub-directories of 1Mb or more:
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking 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 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
plotScDblFinderResults     47.508  1.126  51.505
plotDoubletFinderResults   42.999  0.282  44.498
runDoubletFinder           37.743  0.227  39.942
runScDblFinder             31.137  0.496  33.062
importExampleData          27.053  2.702  35.604
plotBatchCorrCompare       14.205  0.136  14.775
plotScdsHybridResults      12.960  0.338  14.295
plotBcdsResults            11.675  0.272  12.256
plotTSCANClusterDEG        11.554  0.161  12.401
plotDecontXResults         11.332  0.101  11.572
runDecontX                 10.348  0.121  12.444
plotEmptyDropsScatter      10.390  0.051  10.668
plotEmptyDropsResults      10.243  0.058  10.696
plotFindMarkerHeatmap      10.248  0.048  10.353
runEmptyDrops               9.692  0.050  10.367
plotDEGViolin               9.554  0.142   9.751
runSeuratSCTransform        8.754  0.155   9.445
plotCxdsResults             8.780  0.081   8.938
convertSCEToSeurat          8.537  0.291   9.256
detectCellOutlier           8.026  0.219   8.661
plotDEGRegression           8.007  0.080   8.140
plotUMAP                    7.875  0.091   8.396
runUMAP                     7.867  0.093   8.482
getFindMarkerTopTable       7.664  0.085   8.305
runFindMarker               7.454  0.082   8.157
plotDEGHeatmap              6.352  0.121   6.535
importGeneSetsFromMSigDB    5.977  0.228   6.754
plotTSCANClusterPseudo      5.224  0.049   5.561
plotTSCANPseudotimeHeatmap  5.125  0.047   5.441
plotTSCANPseudotimeGenes    4.988  0.044   5.316
plotTSCANResults            4.984  0.041   5.254
plotTSCANDimReduceFeatures  4.961  0.043   5.276
plotRunPerCellQCResults     4.883  0.042   5.063
getEnrichRResult            0.681  0.056  10.070
runEnrichR                  0.639  0.054  12.092
* 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-3.18-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.

Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.3-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)

Tests output

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-apple-darwin20 (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 
  0.356   0.116   0.443

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-apple-darwin20 (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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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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

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Estimating GSVA scores for 2 gene sets.
Estimating ECDFs with Gaussian kernels

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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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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...
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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**************************************************|
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**************************************************|

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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 22 | SKIP 0 | PASS 223 ]

[ FAIL 0 | WARN 22 | SKIP 0 | PASS 223 ]
> 
> proc.time()
   user  system elapsed 
455.806  10.387 520.589

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0050.0050.009
SEG0.0050.0050.010
calcEffectSizes0.5410.0160.567
combineSCE3.8960.0564.034
computeZScore0.4230.0220.458
convertSCEToSeurat8.5370.2919.256
convertSeuratToSCE1.0730.0221.165
dedupRowNames0.1120.0050.121
detectCellOutlier8.0260.2198.661
diffAbundanceFET0.0990.0060.112
discreteColorPalette0.0110.0010.011
distinctColors0.0040.0010.005
downSampleCells1.4250.1971.717
downSampleDepth1.1390.0601.243
expData-ANY-character-method0.6670.0110.706
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.7480.0100.792
expData-set0.7590.0160.819
expData0.7520.0630.854
expDataNames-ANY-method0.6630.0110.700
expDataNames0.6640.0090.697
expDeleteDataTag0.0590.0030.069
expSetDataTag0.0440.0050.049
expTaggedData0.0480.0050.054
exportSCE0.0430.0060.053
exportSCEtoAnnData0.1470.0060.161
exportSCEtoFlatFile0.1440.0030.155
featureIndex0.0740.0080.088
generateSimulatedData0.0950.0080.107
getBiomarker0.1090.0080.123
getDEGTopTable1.9330.0582.095
getDiffAbundanceResults0.0900.0070.104
getEnrichRResult 0.681 0.05610.070
getFindMarkerTopTable7.6640.0858.305
getMSigDBTable0.0080.0080.017
getPathwayResultNames0.0400.0080.048
getSampleSummaryStatsTable0.6880.0130.751
getSoupX000
getTSCANResults3.7840.0633.924
getTopHVG2.1990.0312.410
importAnnData0.0030.0010.004
importBUStools0.6230.0120.721
importCellRanger2.5240.0652.724
importCellRangerV2Sample0.6010.0050.630
importCellRangerV3Sample0.8910.0230.948
importDropEst0.7070.0060.746
importExampleData27.053 2.70235.604
importGeneSetsFromCollection1.5920.1541.789
importGeneSetsFromGMT0.1290.0110.142
importGeneSetsFromList0.2690.0100.280
importGeneSetsFromMSigDB5.9770.2286.754
importMitoGeneSet0.1050.0150.127
importOptimus0.0030.0010.004
importSEQC0.5300.0430.580
importSTARsolo0.6050.0680.680
iterateSimulations0.8270.0500.923
listSampleSummaryStatsTables0.8190.0090.837
mergeSCEColData1.0170.0301.055
mouseBrainSubsetSCE0.0650.0070.072
msigdb_table0.0030.0050.008
plotBarcodeRankDropsResults1.7580.0301.796
plotBarcodeRankScatter1.7900.0141.809
plotBatchCorrCompare14.205 0.13614.775
plotBatchVariance0.7030.0400.754
plotBcdsResults11.675 0.27212.256
plotBubble2.2950.0182.356
plotClusterAbundance1.8760.0121.912
plotCxdsResults8.7800.0818.938
plotDEGHeatmap6.3520.1216.535
plotDEGRegression8.0070.0808.140
plotDEGViolin9.5540.1429.751
plotDEGVolcano2.1090.0222.142
plotDecontXResults11.332 0.10111.572
plotDimRed0.5910.0100.638
plotDoubletFinderResults42.999 0.28244.498
plotEmptyDropsResults10.243 0.05810.696
plotEmptyDropsScatter10.390 0.05110.668
plotFindMarkerHeatmap10.248 0.04810.353
plotMASTThresholdGenes3.5740.0523.662
plotPCA1.1030.0171.128
plotPathway1.8010.0181.833
plotRunPerCellQCResults4.8830.0425.063
plotSCEBarAssayData0.3950.0120.442
plotSCEBarColData0.3010.0090.313
plotSCEBatchFeatureMean0.5340.0050.576
plotSCEDensity0.4710.0110.530
plotSCEDensityAssayData0.3700.0110.466
plotSCEDensityColData0.4720.0130.618
plotSCEDimReduceColData1.6450.0231.732
plotSCEDimReduceFeatures0.8030.0160.867
plotSCEHeatmap1.4960.0181.625
plotSCEScatter0.7480.0130.783
plotSCEViolin0.5080.0120.522
plotSCEViolinAssayData0.5390.0110.567
plotSCEViolinColData0.5010.0120.514
plotScDblFinderResults47.508 1.12651.505
plotScanpyDotPlot0.0390.0040.044
plotScanpyEmbedding0.0400.0060.046
plotScanpyHVG0.0400.0030.046
plotScanpyHeatmap0.0390.0050.047
plotScanpyMarkerGenes0.0410.0060.048
plotScanpyMarkerGenesDotPlot0.0410.0050.050
plotScanpyMarkerGenesHeatmap0.0430.0050.072
plotScanpyMarkerGenesMatrixPlot0.0410.0050.046
plotScanpyMarkerGenesViolin0.0440.0040.049
plotScanpyMatrixPlot0.0400.0050.046
plotScanpyPCA0.0400.0040.045
plotScanpyPCAGeneRanking0.0420.0050.050
plotScanpyPCAVariance0.0410.0060.048
plotScanpyViolin0.0440.0070.052
plotScdsHybridResults12.960 0.33814.295
plotScrubletResults0.0410.0060.051
plotSeuratElbow0.0410.0050.051
plotSeuratHVG0.0400.0050.047
plotSeuratJackStraw0.0420.0040.047
plotSeuratReduction0.0420.0050.049
plotSoupXResults0.0000.0010.001
plotTSCANClusterDEG11.554 0.16112.401
plotTSCANClusterPseudo5.2240.0495.561
plotTSCANDimReduceFeatures4.9610.0435.276
plotTSCANPseudotimeGenes4.9880.0445.316
plotTSCANPseudotimeHeatmap5.1250.0475.441
plotTSCANResults4.9840.0415.254
plotTSNE1.0680.0171.138
plotTopHVG0.8130.0190.876
plotUMAP7.8750.0918.396
readSingleCellMatrix0.0090.0020.011
reportCellQC0.3930.0080.442
reportDropletQC0.0400.0070.052
reportQCTool0.3710.0080.391
retrieveSCEIndex0.0530.0030.063
runBBKNN0.0010.0010.001
runBarcodeRankDrops0.8900.0110.950
runBcds3.7030.0623.932
runCellQC0.3860.0090.416
runClusterSummaryMetrics1.5920.0601.745
runComBatSeq0.9700.0261.051
runCxds0.9930.0121.052
runCxdsBcdsHybrid3.7580.0704.119
runDEAnalysis1.4910.0221.727
runDecontX10.348 0.12112.444
runDimReduce0.9780.0121.013
runDoubletFinder37.743 0.22739.942
runDropletQC0.0440.0070.054
runEmptyDrops 9.692 0.05010.367
runEnrichR 0.639 0.05412.092
runFastMNN3.6160.0703.874
runFeatureSelection0.4410.0090.470
runFindMarker7.4540.0828.157
runGSVA1.6190.0311.742
runHarmony0.0840.0030.091
runKMeans0.9410.0181.012
runLimmaBC0.1670.0030.178
runMNNCorrect1.0960.0121.174
runModelGeneVar0.9660.0121.046
runNormalization3.0660.0353.186
runPerCellQC1.1280.0161.195
runSCANORAMA0.0000.0000.001
runSCMerge0.0070.0020.011
runScDblFinder31.137 0.49633.062
runScanpyFindClusters0.0420.0070.053
runScanpyFindHVG0.0400.0070.048
runScanpyFindMarkers0.0390.0050.047
runScanpyNormalizeData0.4230.0090.437
runScanpyPCA0.0420.0060.051
runScanpyScaleData0.0400.0060.049
runScanpyTSNE0.0410.0050.051
runScanpyUMAP0.0400.0050.046
runScranSNN1.6550.0211.739
runScrublet0.0410.0030.046
runSeuratFindClusters0.0400.0040.046
runSeuratFindHVG1.7440.1221.969
runSeuratHeatmap0.0400.0080.052
runSeuratICA0.0400.0060.049
runSeuratJackStraw0.0400.0060.048
runSeuratNormalizeData0.0410.0060.048
runSeuratPCA0.0400.0070.048
runSeuratSCTransform8.7540.1559.445
runSeuratScaleData0.0410.0060.049
runSeuratUMAP0.0400.0050.047
runSingleR0.0800.0050.088
runSoupX0.0010.0010.001
runTSCAN3.2050.0383.405
runTSCANClusterDEAnalysis3.5370.0483.656
runTSCANDEG3.3910.0333.503
runTSNE1.7030.0251.806
runUMAP7.8670.0938.482
runVAM1.1980.0151.324
runZINBWaVE0.0070.0020.009
sampleSummaryStats0.6380.0110.681
scaterCPM0.2340.0040.245
scaterPCA0.9020.0120.950
scaterlogNormCounts0.4800.0060.514
sce0.0390.0070.054
sctkListGeneSetCollections0.1620.0110.186
sctkPythonInstallConda0.0000.0010.001
sctkPythonInstallVirtualEnv0.0010.0000.001
selectSCTKConda0.0000.0010.001
selectSCTKVirtualEnvironment0.0000.0000.001
setRowNames0.1760.0080.192
setSCTKDisplayRow0.8710.0170.930
singleCellTK0.0000.0000.001
subDiffEx1.0770.0551.199
subsetSCECols0.3780.0130.396
subsetSCERows0.9090.0150.959
summarizeSCE0.1270.0110.167
trimCounts0.3680.0100.428