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This page was generated on 2023-10-16 11:37:33 -0400 (Mon, 16 Oct 2023).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.2 LTS)x86_644.3.1 (2023-06-16) -- "Beagle Scouts" 4626
palomino3Windows Server 2022 Datacenterx644.3.1 (2023-06-16 ucrt) -- "Beagle Scouts" 4379
merida1macOS 12.6.4 Montereyx86_644.3.1 (2023-06-16) -- "Beagle Scouts" 4395
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 1934/2230HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.10.0  (landing page)
Yichen Wang
Snapshot Date: 2023-10-15 14:00:13 -0400 (Sun, 15 Oct 2023)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: RELEASE_3_17
git_last_commit: 277e675
git_last_commit_date: 2023-04-25 11:01:21 -0400 (Tue, 25 Apr 2023)
nebbiolo1Linux (Ubuntu 22.04.2 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino3Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
merida1macOS 12.6.4 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson2macOS 12.6.1 Monterey / 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: 2.10.0
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_2.10.0.tar.gz
StartedAt: 2023-10-16 07:13:34 -0400 (Mon, 16 Oct 2023)
EndedAt: 2023-10-16 07:45:39 -0400 (Mon, 16 Oct 2023)
EllapsedTime: 1924.2 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_2.10.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.17-bioc/meat/singleCellTK.Rcheck’
* using R version 4.3.1 (2023-06-16)
* using platform: x86_64-apple-darwin20 (64-bit)
* R was compiled by
    Apple clang version 14.0.3 (clang-1403.0.22.14.1)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.6.4
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.10.0’
* 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  6.7Mb
  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     46.659  0.970  62.881
plotDoubletFinderResults   33.999  0.251  43.418
runScDblFinder             33.694  0.500  42.135
importExampleData          24.147  2.590  34.522
runDoubletFinder           25.401  0.219  31.954
plotBatchCorrCompare       14.431  0.182  17.783
plotScdsHybridResults      12.992  0.208  16.967
plotTSCANClusterDEG        12.328  0.230  16.490
plotBcdsResults            12.150  0.227  14.700
plotDecontXResults         11.661  0.098  15.012
plotFindMarkerHeatmap      11.003  0.058  14.721
plotEmptyDropsResults      10.523  0.055  13.998
plotEmptyDropsScatter      10.474  0.056  13.737
plotDEGViolin              10.093  0.138  12.990
runEmptyDrops               9.811  0.043  12.087
plotCxdsResults             9.090  0.076  11.235
runDecontX                  8.718  0.085  11.137
plotDEGRegression           8.668  0.087  11.277
runUMAP                     8.034  0.064   9.957
detectCellOutlier           7.807  0.199  10.230
plotUMAP                    7.894  0.075  10.038
getFindMarkerTopTable       7.256  0.171  10.209
runFindMarker               7.276  0.069   8.850
runSeuratSCTransform        7.063  0.130   8.851
plotDEGHeatmap              6.802  0.127   8.907
convertSCEToSeurat          5.927  0.271   7.425
importGeneSetsFromMSigDB    5.950  0.186   7.500
plotTSCANPseudotimeHeatmap  5.069  0.041   6.653
plotTSCANClusterPseudo      5.012  0.046   6.400
plotTSCANDimReduceFeatures  5.016  0.040   6.557
plotRunPerCellQCResults     4.929  0.038   6.482
plotTSCANPseudotimeGenes    4.873  0.036   6.351
plotTSCANResults            4.749  0.039   6.272
getTSCANResults             3.881  0.097   5.414
runCxdsBcdsHybrid           3.816  0.058   5.022
plotMASTThresholdGenes      3.670  0.045   5.033
getEnrichRResult            0.677  0.060   8.531
* 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-3.17-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 4.3.1 (2023-06-16) -- "Beagle Scouts"
Copyright (C) 2023 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   0.118   0.465

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.3.1 (2023-06-16) -- "Beagle Scouts"
Copyright (C) 2023 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


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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Error in fitdistr(mahalanobis.sq.null[nonzero.values], "gamma", lower = 0.01) : 
  optimization failed
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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Performing log-normalization
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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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: 9590

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8042
Number of communities: 6
Elapsed time: 0 seconds
Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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**************************************************|
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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 20 | SKIP 0 | PASS 220 ]

[ FAIL 0 | WARN 20 | SKIP 0 | PASS 220 ]
> 
> proc.time()
   user  system elapsed 
425.079   9.237 544.198

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0050.0050.010
SEG0.0050.0050.013
calcEffectSizes0.4110.0150.546
combineSCE3.2720.1004.251
computeZScore0.4720.0200.591
convertSCEToSeurat5.9270.2717.425
convertSeuratToSCE0.9330.0221.177
dedupRowNames0.1120.0050.145
detectCellOutlier 7.807 0.19910.230
diffAbundanceFET0.0810.0050.110
discreteColorPalette0.0110.0010.015
distinctColors0.0040.0000.004
downSampleCells1.3770.1741.996
downSampleDepth1.0710.0381.405
expData-ANY-character-method0.7040.0220.940
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.7750.0201.013
expData-set0.7500.0200.965
expData0.7610.0831.007
expDataNames-ANY-method0.6640.0100.825
expDataNames0.6630.0130.838
expDeleteDataTag0.0610.0040.080
expSetDataTag0.0450.0070.073
expTaggedData0.0460.0050.063
exportSCE0.0400.0060.058
exportSCEtoAnnData0.1430.0030.185
exportSCEtoFlatFile0.1440.0050.188
featureIndex0.0690.0070.099
generateSimulatedData0.0790.0060.108
getBiomarker0.0950.0070.127
getDEGTopTable1.8690.0562.419
getDiffAbundanceResults0.0710.0020.086
getEnrichRResult0.6770.0608.531
getFindMarkerTopTable 7.256 0.17110.209
getMSigDBTable0.0080.0090.017
getPathwayResultNames0.0400.0070.063
getSampleSummaryStatsTable0.7370.0191.051
getSoupX0.0000.0010.001
getTSCANResults3.8810.0975.414
getTopHVG1.8620.0502.597
importAnnData0.0030.0020.015
importBUStools0.5960.0110.813
importCellRanger2.3480.0893.581
importCellRangerV2Sample0.5880.0090.787
importCellRangerV3Sample0.8470.0321.308
importDropEst0.7450.0331.036
importExampleData24.147 2.59034.522
importGeneSetsFromCollection1.5720.1652.095
importGeneSetsFromGMT0.1320.0120.170
importGeneSetsFromList0.2680.0100.341
importGeneSetsFromMSigDB5.9500.1867.500
importMitoGeneSet0.1100.0150.153
importOptimus0.0030.0020.006
importSEQC0.5810.0200.734
importSTARsolo0.6620.0710.883
iterateSimulations0.7990.0621.032
listSampleSummaryStatsTables0.8540.0091.013
mergeSCEColData1.0200.0351.305
mouseBrainSubsetSCE0.0460.0050.067
msigdb_table0.0030.0040.008
plotBarcodeRankDropsResults1.9200.0362.364
plotBarcodeRankScatter1.5680.0172.042
plotBatchCorrCompare14.431 0.18217.783
plotBatchVariance0.7350.0550.931
plotBcdsResults12.150 0.22714.700
plotClusterAbundance2.5470.0403.171
plotCxdsResults 9.090 0.07611.235
plotDEGHeatmap6.8020.1278.907
plotDEGRegression 8.668 0.08711.277
plotDEGViolin10.093 0.13812.990
plotDEGVolcano2.3060.0242.976
plotDecontXResults11.661 0.09815.012
plotDimRed0.5830.0070.746
plotDoubletFinderResults33.999 0.25143.418
plotEmptyDropsResults10.523 0.05513.998
plotEmptyDropsScatter10.474 0.05613.737
plotFindMarkerHeatmap11.003 0.05814.721
plotMASTThresholdGenes3.6700.0455.033
plotPCA1.1880.0161.573
plotPathway1.8290.0182.452
plotRunPerCellQCResults4.9290.0386.482
plotSCEBarAssayData0.3740.0090.488
plotSCEBarColData0.2870.0070.383
plotSCEBatchFeatureMean0.5830.0060.771
plotSCEDensity0.4760.0080.609
plotSCEDensityAssayData0.3630.0070.486
plotSCEDensityColData0.4640.0100.657
plotSCEDimReduceColData1.9060.0242.536
plotSCEDimReduceFeatures0.8030.0101.043
plotSCEHeatmap1.6560.0132.179
plotSCEScatter0.7750.0111.027
plotSCEViolin0.5220.0090.665
plotSCEViolinAssayData0.5440.0100.728
plotSCEViolinColData0.5320.0080.711
plotScDblFinderResults46.659 0.97062.881
plotScanpyDotPlot0.0430.0040.057
plotScanpyEmbedding0.0410.0040.057
plotScanpyHVG0.0390.0050.056
plotScanpyHeatmap0.0400.0060.060
plotScanpyMarkerGenes0.0410.0030.055
plotScanpyMarkerGenesDotPlot0.0410.0050.057
plotScanpyMarkerGenesHeatmap0.0460.0050.066
plotScanpyMarkerGenesMatrixPlot0.0440.0070.070
plotScanpyMarkerGenesViolin0.0440.0050.064
plotScanpyMatrixPlot0.0430.0040.065
plotScanpyPCA0.0460.0040.066
plotScanpyPCAGeneRanking0.0430.0040.061
plotScanpyPCAVariance0.0420.0050.057
plotScanpyViolin0.0420.0040.073
plotScdsHybridResults12.992 0.20816.967
plotScrubletResults0.0400.0050.058
plotSeuratElbow0.0420.0050.059
plotSeuratHVG0.0410.0050.068
plotSeuratJackStraw0.0440.0060.092
plotSeuratReduction0.0420.0050.080
plotSoupXResults000
plotTSCANClusterDEG12.328 0.23016.490
plotTSCANClusterPseudo5.0120.0466.400
plotTSCANDimReduceFeatures5.0160.0406.557
plotTSCANPseudotimeGenes4.8730.0366.351
plotTSCANPseudotimeHeatmap5.0690.0416.653
plotTSCANResults4.7490.0396.272
plotTSNE1.1720.0151.531
plotTopHVG0.8260.0181.062
plotUMAP 7.894 0.07510.038
readSingleCellMatrix0.0080.0010.013
reportCellQC0.3830.0070.487
reportDropletQC0.0390.0040.055
reportQCTool0.3750.0060.485
retrieveSCEIndex0.0540.0050.072
runBBKNN0.0000.0010.001
runBarcodeRankDrops0.9170.0111.203
runBcds3.7610.0594.883
runCellQC0.4010.0180.524
runComBatSeq0.9990.0521.320
runCxds1.1200.0421.498
runCxdsBcdsHybrid3.8160.0585.022
runDEAnalysis1.5230.0131.945
runDecontX 8.718 0.08511.137
runDimReduce1.0120.0121.298
runDoubletFinder25.401 0.21931.954
runDropletQC0.0400.0040.054
runEmptyDrops 9.811 0.04312.087
runEnrichR0.6260.0432.217
runFastMNN3.7600.0614.753
runFeatureSelection0.4390.0050.534
runFindMarker7.2760.0698.850
runGSVA1.5250.0211.890
runHarmony0.0810.0020.099
runKMeans0.8930.0131.106
runLimmaBC0.1680.0020.212
runMNNCorrect1.0950.0081.377
runModelGeneVar1.0020.0121.234
runNormalization1.1730.0111.446
runPerCellQC1.1800.0171.458
runSCANORAMA0.0000.0010.002
runSCMerge0.0070.0010.011
runScDblFinder33.694 0.50042.135
runScanpyFindClusters0.0410.0030.055
runScanpyFindHVG0.0410.0060.058
runScanpyFindMarkers0.0440.0060.065
runScanpyNormalizeData0.4280.0080.538
runScanpyPCA0.0410.0030.055
runScanpyScaleData0.0430.0040.057
runScanpyTSNE0.0410.0050.060
runScanpyUMAP0.0430.0040.054
runScranSNN1.5870.0201.975
runScrublet0.0420.0030.053
runSeuratFindClusters0.0410.0060.062
runSeuratFindHVG1.5390.1262.060
runSeuratHeatmap0.0420.0060.059
runSeuratICA0.0410.0050.054
runSeuratJackStraw0.0410.0050.056
runSeuratNormalizeData0.0380.0050.052
runSeuratPCA0.0410.0040.054
runSeuratSCTransform7.0630.1308.851
runSeuratScaleData0.0410.0060.058
runSeuratUMAP0.0380.0060.055
runSingleR0.0810.0050.112
runSoupX0.0000.0010.001
runTSCAN3.1560.0293.951
runTSCANClusterDEAnalysis3.5390.0354.364
runTSCANDEG3.2760.0283.874
runTSNE1.7170.0202.047
runUMAP8.0340.0649.957
runVAM1.2420.0141.551
runZINBWaVE0.0070.0020.010
sampleSummaryStats0.6670.0110.824
scaterCPM0.2360.0040.296
scaterPCA0.9190.0111.131
scaterlogNormCounts0.4920.0060.609
sce0.0390.0080.058
sctkListGeneSetCollections0.1640.0110.210
sctkPythonInstallConda0.0000.0000.001
sctkPythonInstallVirtualEnv0.0000.0010.001
selectSCTKConda0.0000.0000.001
selectSCTKVirtualEnvironment0.0010.0000.000
setRowNames0.1820.0090.233
setSCTKDisplayRow0.9380.0201.174
singleCellTK0.0010.0000.001
subDiffEx0.9920.0231.243
subsetSCECols0.4090.0130.514
subsetSCERows0.9180.0151.135
summarizeSCE0.1260.0060.160
trimCounts0.4350.0070.541