Back to Multiple platform build/check report for BioC 3.18:   simplified   long
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This page was generated on 2024-03-27 11:38:08 -0400 (Wed, 27 Mar 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.3.3 (2024-02-29) -- "Angel Food Cake" 4667
palomino4Windows Server 2022 Datacenterx644.3.3 (2024-02-29 ucrt) -- "Angel Food Cake" 4403
merida1macOS 12.7.1 Montereyx86_644.3.3 (2024-02-29) -- "Angel Food Cake" 4426
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: 2024-03-25 14:05:07 -0400 (Mon, 25 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: 2024-03-26 08:49:14 -0400 (Tue, 26 Mar 2024)
EndedAt: 2024-03-26 09:17:56 -0400 (Tue, 26 Mar 2024)
EllapsedTime: 1722.5 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     47.280  1.019  51.090
plotDoubletFinderResults   43.601  0.227  44.415
runDoubletFinder           37.527  0.188  38.497
runScDblFinder             34.377  0.521  36.204
importExampleData          25.136  2.498  29.721
plotBatchCorrCompare       13.825  0.154  14.265
plotScdsHybridResults      13.213  0.279  14.131
plotTSCANClusterDEG        11.523  0.152  12.121
plotBcdsResults            11.361  0.224  11.685
plotDecontXResults         11.259  0.081  11.685
plotFindMarkerHeatmap      10.660  0.066  11.403
plotEmptyDropsScatter      10.399  0.077  11.761
plotEmptyDropsResults      10.353  0.053  11.053
runDecontX                 10.170  0.070  10.329
plotDEGViolin               9.790  0.156  10.171
runEmptyDrops               9.694  0.046  10.324
runSeuratSCTransform        8.949  0.127   9.571
convertSCEToSeurat          8.633  0.290   9.076
plotCxdsResults             8.739  0.078   9.161
plotDEGRegression           8.210  0.078   8.502
detectCellOutlier           8.070  0.185   8.301
runUMAP                     7.976  0.080   8.457
plotUMAP                    7.825  0.083   7.992
getFindMarkerTopTable       7.533  0.066   7.621
runFindMarker               7.508  0.069   7.820
plotDEGHeatmap              6.384  0.115   6.591
importGeneSetsFromMSigDB    5.722  0.201   6.087
plotTSCANClusterPseudo      5.275  0.040   5.516
plotTSCANPseudotimeHeatmap  5.099  0.042   5.240
plotTSCANDimReduceFeatures  4.980  0.041   5.282
plotTSCANResults            4.906  0.040   5.035
plotRunPerCellQCResults     4.857  0.035   5.029
getEnrichRResult            0.696  0.060  14.880
runEnrichR                  0.661  0.065  19.344
* 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 
  0.354   0.115   0.434 

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...
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'
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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 
456.062   9.497 521.035 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0040.0030.007
SEG0.0050.0040.009
calcEffectSizes0.5470.0150.564
combineSCE3.9080.0473.972
computeZScore0.4260.0290.461
convertSCEToSeurat8.6330.2909.076
convertSeuratToSCE1.0680.0161.094
dedupRowNames0.1110.0040.116
detectCellOutlier8.0700.1858.301
diffAbundanceFET0.1080.0090.118
discreteColorPalette0.0120.0010.013
distinctColors0.0040.0000.005
downSampleCells1.4240.1851.631
downSampleDepth1.1780.0511.252
expData-ANY-character-method0.6700.0100.683
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.7460.0090.757
expData-set0.7510.0140.767
expData0.7400.0580.799
expDataNames-ANY-method0.6470.0090.656
expDataNames0.6480.0090.658
expDeleteDataTag0.0610.0050.065
expSetDataTag0.0400.0020.043
expTaggedData0.0440.0040.048
exportSCE0.0420.0060.048
exportSCEtoAnnData0.1330.0030.137
exportSCEtoFlatFile0.1330.0050.139
featureIndex0.0590.0090.068
generateSimulatedData0.0910.0090.100
getBiomarker0.1100.0100.121
getDEGTopTable1.8830.0521.944
getDiffAbundanceResults0.0800.0040.084
getEnrichRResult 0.696 0.06014.880
getFindMarkerTopTable7.5330.0667.621
getMSigDBTable0.0070.0060.014
getPathwayResultNames0.0400.0060.046
getSampleSummaryStatsTable0.6610.0080.671
getSoupX0.0000.0010.001
getTSCANResults3.8390.0553.906
getTopHVG2.2150.0252.259
importAnnData0.0030.0010.004
importBUStools0.5970.0070.638
importCellRanger2.4360.0652.708
importCellRangerV2Sample0.5710.0070.655
importCellRangerV3Sample0.8560.0210.883
importDropEst0.6820.0050.694
importExampleData25.136 2.49829.721
importGeneSetsFromCollection1.6010.1431.785
importGeneSetsFromGMT0.1260.0100.149
importGeneSetsFromList0.2650.0120.320
importGeneSetsFromMSigDB5.7220.2016.087
importMitoGeneSet0.1030.0120.117
importOptimus0.0030.0010.004
importSEQC0.5520.0380.648
importSTARsolo0.6350.0710.715
iterateSimulations0.7970.0470.872
listSampleSummaryStatsTables0.8250.0130.871
mergeSCEColData1.0000.0331.072
mouseBrainSubsetSCE0.0660.0080.076
msigdb_table0.0020.0050.007
plotBarcodeRankDropsResults1.7970.0321.848
plotBarcodeRankScatter1.8320.0151.853
plotBatchCorrCompare13.825 0.15414.265
plotBatchVariance0.7240.0570.818
plotBcdsResults11.361 0.22411.685
plotBubble2.3100.0252.346
plotClusterAbundance1.9140.0161.980
plotCxdsResults8.7390.0789.161
plotDEGHeatmap6.3840.1156.591
plotDEGRegression8.2100.0788.502
plotDEGViolin 9.790 0.15610.171
plotDEGVolcano2.1560.0232.278
plotDecontXResults11.259 0.08111.685
plotDimRed0.5830.0080.595
plotDoubletFinderResults43.601 0.22744.415
plotEmptyDropsResults10.353 0.05311.053
plotEmptyDropsScatter10.399 0.07711.761
plotFindMarkerHeatmap10.660 0.06611.403
plotMASTThresholdGenes3.7150.0483.929
plotPCA1.1160.0151.171
plotPathway1.8220.0201.950
plotRunPerCellQCResults4.8570.0355.029
plotSCEBarAssayData0.3910.0100.404
plotSCEBarColData0.2980.0090.308
plotSCEBatchFeatureMean0.5080.0050.515
plotSCEDensity0.4500.0110.466
plotSCEDensityAssayData0.3490.0110.375
plotSCEDensityColData0.4440.0100.464
plotSCEDimReduceColData1.6400.0241.767
plotSCEDimReduceFeatures0.7800.0170.862
plotSCEHeatmap1.4770.0121.572
plotSCEScatter0.7880.0140.883
plotSCEViolin0.5430.0110.599
plotSCEViolinAssayData0.5730.0140.628
plotSCEViolinColData0.5150.0110.565
plotScDblFinderResults47.280 1.01951.090
plotScanpyDotPlot0.0410.0060.049
plotScanpyEmbedding0.0400.0050.046
plotScanpyHVG0.0410.0050.045
plotScanpyHeatmap0.0430.0050.050
plotScanpyMarkerGenes0.0420.0040.048
plotScanpyMarkerGenesDotPlot0.0470.0060.055
plotScanpyMarkerGenesHeatmap0.0420.0030.045
plotScanpyMarkerGenesMatrixPlot0.0440.0040.049
plotScanpyMarkerGenesViolin0.0410.0060.050
plotScanpyMatrixPlot0.0430.0030.047
plotScanpyPCA0.0410.0060.050
plotScanpyPCAGeneRanking0.0410.0030.045
plotScanpyPCAVariance0.0400.0080.053
plotScanpyViolin0.0430.0040.049
plotScdsHybridResults13.213 0.27914.131
plotScrubletResults0.0440.0070.055
plotSeuratElbow0.0440.0080.054
plotSeuratHVG0.0400.0050.044
plotSeuratJackStraw0.0390.0040.045
plotSeuratReduction0.0410.0040.047
plotSoupXResults0.0000.0010.000
plotTSCANClusterDEG11.523 0.15212.121
plotTSCANClusterPseudo5.2750.0405.516
plotTSCANDimReduceFeatures4.9800.0415.282
plotTSCANPseudotimeGenes4.8530.0354.932
plotTSCANPseudotimeHeatmap5.0990.0425.240
plotTSCANResults4.9060.0405.035
plotTSNE1.0240.0141.048
plotTopHVG0.8090.0180.832
plotUMAP7.8250.0837.992
readSingleCellMatrix0.0090.0020.010
reportCellQC0.3760.0090.390
reportDropletQC0.0380.0040.042
reportQCTool0.3820.0080.390
retrieveSCEIndex0.0510.0060.057
runBBKNN0.0000.0010.001
runBarcodeRankDrops0.8690.0110.885
runBcds3.6310.0713.724
runCellQC0.3800.0070.388
runClusterSummaryMetrics1.6300.0671.710
runComBatSeq0.9730.0211.004
runCxds0.9890.0141.014
runCxdsBcdsHybrid3.7530.0573.845
runDEAnalysis1.4760.0141.503
runDecontX10.170 0.07010.329
runDimReduce0.9640.0100.979
runDoubletFinder37.527 0.18838.497
runDropletQC0.0390.0060.046
runEmptyDrops 9.694 0.04610.324
runEnrichR 0.661 0.06519.344
runFastMNN3.6100.0493.819
runFeatureSelection0.4480.0060.470
runFindMarker7.5080.0697.820
runGSVA1.6030.0281.686
runHarmony0.0820.0020.085
runKMeans0.9490.0150.981
runLimmaBC0.1830.0050.199
runMNNCorrect1.1140.0101.171
runModelGeneVar0.9730.0131.013
runNormalization3.0840.0363.219
runPerCellQC1.1480.0161.199
runSCANORAMA0.0000.0000.001
runSCMerge0.0070.0020.009
runScDblFinder34.377 0.52136.204
runScanpyFindClusters0.0420.0050.048
runScanpyFindHVG0.0400.0030.044
runScanpyFindMarkers0.0410.0090.049
runScanpyNormalizeData0.4220.0080.444
runScanpyPCA0.0400.0060.050
runScanpyScaleData0.0420.0050.048
runScanpyTSNE0.0400.0050.046
runScanpyUMAP0.0400.0050.048
runScranSNN1.6310.0291.757
runScrublet0.0390.0030.043
runSeuratFindClusters0.0400.0030.045
runSeuratFindHVG1.7600.1251.980
runSeuratHeatmap0.0400.0040.045
runSeuratICA0.0400.0050.046
runSeuratJackStraw0.0420.0050.050
runSeuratNormalizeData0.0410.0050.047
runSeuratPCA0.0410.0030.045
runSeuratSCTransform8.9490.1279.571
runSeuratScaleData0.0400.0050.045
runSeuratUMAP0.0400.0050.046
runSingleR0.0790.0060.085
runSoupX0.0010.0010.001
runTSCAN3.2960.0393.374
runTSCANClusterDEAnalysis3.6030.0363.753
runTSCANDEG3.4090.0333.601
runTSNE1.7120.0221.811
runUMAP7.9760.0808.457
runVAM1.1750.0141.244
runZINBWaVE0.0070.0020.009
sampleSummaryStats0.6490.0120.690
scaterCPM0.2340.0050.261
scaterPCA0.9310.0130.991
scaterlogNormCounts0.4960.0040.522
sce0.0390.0090.051
sctkListGeneSetCollections0.1620.0090.179
sctkPythonInstallConda0.0000.0010.001
sctkPythonInstallVirtualEnv0.0010.0000.001
selectSCTKConda0.0000.0010.001
selectSCTKVirtualEnvironment0.0000.0010.000
setRowNames0.1800.0060.197
setSCTKDisplayRow0.8660.0120.911
singleCellTK0.0010.0010.002
subDiffEx1.0860.0471.183
subsetSCECols0.3810.0100.401
subsetSCERows0.8720.0150.922
summarizeSCE0.1270.0100.143
trimCounts0.3630.0110.393