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This page was generated on 2024-04-17 11:38:15 -0400 (Wed, 17 Apr 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.3.3 (2024-02-29) -- "Angel Food Cake" 4676
palomino4Windows Server 2022 Datacenterx644.3.3 (2024-02-29 ucrt) -- "Angel Food Cake" 4414
merida1macOS 12.7.1 Montereyx86_644.3.3 (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 1971/2266HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.12.2  (landing page)
Joshua David Campbell
Snapshot Date: 2024-04-15 14:05:01 -0400 (Mon, 15 Apr 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: 2024-04-16 09:01:57 -0400 (Tue, 16 Apr 2024)
EndedAt: 2024-04-16 09:30:55 -0400 (Tue, 16 Apr 2024)
EllapsedTime: 1738.2 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     46.483  1.125  51.647
plotDoubletFinderResults   43.702  0.297  45.226
runDoubletFinder           37.724  0.224  39.920
runScDblFinder             32.528  0.521  34.439
importExampleData          25.910  2.615  30.939
plotBatchCorrCompare       13.939  0.317  15.236
plotScdsHybridResults      13.025  0.302  14.086
plotBcdsResults            11.715  0.307  12.748
plotTSCANClusterDEG        11.762  0.166  12.941
plotDecontXResults         11.230  0.153  11.796
plotFindMarkerHeatmap      10.550  0.063  11.527
plotEmptyDropsScatter      10.353  0.049  10.981
runDecontX                 10.286  0.088  10.873
plotDEGViolin              10.052  0.283  11.563
plotEmptyDropsResults      10.263  0.046  10.508
runEmptyDrops               9.713  0.042  10.122
runSeuratSCTransform        8.919  0.131   9.309
plotCxdsResults             8.849  0.103   9.156
convertSCEToSeurat          8.364  0.326   9.011
plotDEGRegression           8.389  0.195   9.599
detectCellOutlier           7.986  0.250   8.962
runUMAP                     8.007  0.076   8.373
getFindMarkerTopTable       7.834  0.112   8.373
plotUMAP                    7.749  0.093   8.485
runFindMarker               7.595  0.075   8.022
plotDEGHeatmap              6.363  0.138   6.614
importGeneSetsFromMSigDB    5.747  0.205   6.168
plotTSCANClusterPseudo      5.294  0.047   5.763
plotTSCANPseudotimeHeatmap  5.140  0.048   5.599
plotTSCANDimReduceFeatures  5.083  0.043   5.580
plotTSCANResults            5.062  0.044   5.557
plotTSCANPseudotimeGenes    4.989  0.040   5.460
plotRunPerCellQCResults     4.931  0.039   5.272
getEnrichRResult            0.684  0.056  20.790
runEnrichR                  0.626  0.038  17.305
* 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 
  0.353   0.110   0.438

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 
455.250   9.379 493.792

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0050.0050.110
SEG0.0040.0050.011
calcEffectSizes0.5250.0200.553
combineSCE3.7840.0793.968
computeZScore0.4380.0372.801
convertSCEToSeurat8.3640.3269.011
convertSeuratToSCE1.0010.0231.042
dedupRowNames0.1080.0070.159
detectCellOutlier7.9860.2508.962
diffAbundanceFET0.0980.0060.111
discreteColorPalette0.0100.0010.011
distinctColors0.0040.0000.004
downSampleCells1.4230.1681.681
downSampleDepth1.1560.0461.291
expData-ANY-character-method0.6810.0090.737
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.7630.0120.808
expData-set0.7400.0130.795
expData0.7430.0690.901
expDataNames-ANY-method0.6540.0110.738
expDataNames0.6460.0080.716
expDeleteDataTag0.0620.0030.072
expSetDataTag0.0440.0030.049
expTaggedData0.0470.0020.051
exportSCE0.0410.0070.054
exportSCEtoAnnData0.1430.0030.151
exportSCEtoFlatFile0.1420.0050.152
featureIndex0.0680.0080.085
generateSimulatedData0.0940.0100.106
getBiomarker0.1150.0110.133
getDEGTopTable1.8980.0572.118
getDiffAbundanceResults0.0860.0050.100
getEnrichRResult 0.684 0.05620.790
getFindMarkerTopTable7.8340.1128.373
getMSigDBTable0.0080.0060.014
getPathwayResultNames0.0410.0060.050
getSampleSummaryStatsTable0.6770.0110.756
getSoupX0.0000.0010.001
getTSCANResults3.9220.0614.208
getTopHVG2.1900.0292.339
importAnnData0.0030.0000.004
importBUStools0.5940.0080.624
importCellRanger2.4790.0642.666
importCellRangerV2Sample0.5970.0140.644
importCellRangerV3Sample0.8580.0250.967
importDropEst0.6890.0080.811
importExampleData25.910 2.61530.939
importGeneSetsFromCollection1.6180.1521.860
importGeneSetsFromGMT0.1310.0100.145
importGeneSetsFromList0.2690.0120.295
importGeneSetsFromMSigDB5.7470.2056.168
importMitoGeneSet0.1020.0130.115
importOptimus0.0020.0010.003
importSEQC0.5500.0390.594
importSTARsolo0.6290.0820.717
iterateSimulations0.7820.0430.829
listSampleSummaryStatsTables0.8080.0090.825
mergeSCEColData0.9700.0371.173
mouseBrainSubsetSCE0.0640.0090.090
msigdb_table0.0030.0050.009
plotBarcodeRankDropsResults1.8280.0412.010
plotBarcodeRankScatter1.9310.0412.260
plotBatchCorrCompare13.939 0.31715.236
plotBatchVariance0.7220.0480.828
plotBcdsResults11.715 0.30712.748
plotBubble2.3510.0382.514
plotClusterAbundance1.8860.0232.109
plotCxdsResults8.8490.1039.156
plotDEGHeatmap6.3630.1386.614
plotDEGRegression8.3890.1959.599
plotDEGViolin10.052 0.28311.563
plotDEGVolcano2.1470.0382.305
plotDecontXResults11.230 0.15311.796
plotDimRed0.5830.0120.632
plotDoubletFinderResults43.702 0.29745.226
plotEmptyDropsResults10.263 0.04610.508
plotEmptyDropsScatter10.353 0.04910.981
plotFindMarkerHeatmap10.550 0.06311.527
plotMASTThresholdGenes3.6370.0443.960
plotPCA1.1310.0201.303
plotPathway1.8710.0201.992
plotRunPerCellQCResults4.9310.0395.272
plotSCEBarAssayData0.3870.0110.426
plotSCEBarColData0.3080.0090.346
plotSCEBatchFeatureMean0.5320.0060.579
plotSCEDensity0.4640.0120.521
plotSCEDensityAssayData0.3710.0100.408
plotSCEDensityColData0.4650.0110.516
plotSCEDimReduceColData1.6460.0211.812
plotSCEDimReduceFeatures0.8200.0120.896
plotSCEHeatmap1.5070.0131.637
plotSCEScatter0.7890.0140.877
plotSCEViolin0.5300.0110.582
plotSCEViolinAssayData0.5560.0100.580
plotSCEViolinColData0.5210.0110.565
plotScDblFinderResults46.483 1.12551.647
plotScanpyDotPlot0.0400.0040.050
plotScanpyEmbedding0.0400.0070.052
plotScanpyHVG0.0410.0060.050
plotScanpyHeatmap0.0430.0070.051
plotScanpyMarkerGenes0.0390.0060.046
plotScanpyMarkerGenesDotPlot0.0410.0040.049
plotScanpyMarkerGenesHeatmap0.0400.0060.048
plotScanpyMarkerGenesMatrixPlot0.0410.0060.053
plotScanpyMarkerGenesViolin0.0400.0060.052
plotScanpyMatrixPlot0.0400.0020.045
plotScanpyPCA0.0400.0040.049
plotScanpyPCAGeneRanking0.0390.0030.047
plotScanpyPCAVariance0.0400.0060.053
plotScanpyViolin0.0400.0050.050
plotScdsHybridResults13.025 0.30214.086
plotScrubletResults0.0390.0040.043
plotSeuratElbow0.0390.0050.046
plotSeuratHVG0.0400.0040.046
plotSeuratJackStraw0.0420.0080.051
plotSeuratReduction0.0400.0060.051
plotSoupXResults0.0000.0010.002
plotTSCANClusterDEG11.762 0.16612.941
plotTSCANClusterPseudo5.2940.0475.763
plotTSCANDimReduceFeatures5.0830.0435.580
plotTSCANPseudotimeGenes4.9890.0405.460
plotTSCANPseudotimeHeatmap5.1400.0485.599
plotTSCANResults5.0620.0445.557
plotTSNE1.0850.0191.181
plotTopHVG0.8100.0190.917
plotUMAP7.7490.0938.485
readSingleCellMatrix0.0080.0010.010
reportCellQC0.3750.0090.397
reportDropletQC0.0390.0040.045
reportQCTool0.3750.0070.399
retrieveSCEIndex0.0520.0050.058
runBBKNN000
runBarcodeRankDrops0.8770.0130.925
runBcds3.7150.0604.093
runCellQC0.3840.0110.423
runClusterSummaryMetrics1.6370.0691.822
runComBatSeq0.9930.0221.047
runCxds0.9990.0131.045
runCxdsBcdsHybrid3.8010.0624.034
runDEAnalysis1.4860.0151.580
runDecontX10.286 0.08810.873
runDimReduce1.0050.0111.066
runDoubletFinder37.724 0.22439.920
runDropletQC0.0390.0040.043
runEmptyDrops 9.713 0.04210.122
runEnrichR 0.626 0.03817.305
runFastMNN3.6770.0563.847
runFeatureSelection0.4530.0090.482
runFindMarker7.5950.0758.022
runGSVA1.6220.0271.699
runHarmony0.0880.0030.093
runKMeans0.9500.0171.008
runLimmaBC0.1680.0020.174
runMNNCorrect1.0860.0091.130
runModelGeneVar1.0010.0141.050
runNormalization3.0440.0383.189
runPerCellQC1.1760.0161.237
runSCANORAMA0.0000.0010.001
runSCMerge0.0080.0010.009
runScDblFinder32.528 0.52134.439
runScanpyFindClusters0.0420.0050.050
runScanpyFindHVG0.0390.0050.049
runScanpyFindMarkers0.0390.0050.046
runScanpyNormalizeData0.4150.0070.435
runScanpyPCA0.0410.0040.046
runScanpyScaleData0.0400.0050.045
runScanpyTSNE0.0390.0030.042
runScanpyUMAP0.0400.0040.045
runScranSNN1.6570.0221.725
runScrublet0.0410.0040.046
runSeuratFindClusters0.0450.0050.051
runSeuratFindHVG1.7980.1351.984
runSeuratHeatmap0.0400.0050.044
runSeuratICA0.0450.0060.051
runSeuratJackStraw0.0450.0050.052
runSeuratNormalizeData0.0460.0060.055
runSeuratPCA0.0440.0050.051
runSeuratSCTransform8.9190.1319.309
runSeuratScaleData0.0400.0050.048
runSeuratUMAP0.0420.0050.048
runSingleR0.0820.0050.092
runSoupX0.0000.0000.001
runTSCAN3.2810.0313.413
runTSCANClusterDEAnalysis3.5790.0363.723
runTSCANDEG3.3970.0303.536
runTSNE1.7310.0221.809
runUMAP8.0070.0768.373
runVAM1.2080.0131.262
runZINBWaVE0.0070.0020.009
sampleSummaryStats0.6770.0110.737
scaterCPM0.2380.0040.249
scaterPCA0.9360.0160.982
scaterlogNormCounts0.4800.0050.500
sce0.0390.0090.050
sctkListGeneSetCollections0.1600.0100.173
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv0.0000.0010.001
selectSCTKConda0.0000.0010.001
selectSCTKVirtualEnvironment0.0000.0010.001
setRowNames0.1790.0080.193
setSCTKDisplayRow0.8900.0160.939
singleCellTK0.0000.0000.001
subDiffEx1.1070.0481.193
subsetSCECols0.3920.0170.425
subsetSCERows0.9030.0150.968
summarizeSCE0.1300.0130.150
trimCounts0.3640.0100.391