Back to Multiple platform build/check report for BioC 3.17
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This page was generated on 2023-04-12 10:55:42 -0400 (Wed, 12 Apr 2023).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.1 LTS)x86_644.3.0 alpha (2023-04-03 r84154) 4547
nebbiolo2Linux (Ubuntu 20.04.5 LTS)x86_64R Under development (unstable) (2023-02-14 r83833) -- "Unsuffered Consequences" 4333
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

CHECK results for singleCellTK on nebbiolo2


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.

Note: If "R CMD check" recently failed on the Linux builder over a missing dependency, add the missing dependency to "Suggests" in your DESCRIPTION file. See the Renviron.bioc for details.

raw results

Package 1914/2207HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.9.0  (landing page)
Yichen Wang
Snapshot Date: 2023-04-11 14:00:16 -0400 (Tue, 11 Apr 2023)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: 4468720
git_last_commit_date: 2022-11-01 11:17:41 -0400 (Tue, 01 Nov 2022)
nebbiolo1Linux (Ubuntu 22.04.1 LTS) / x86_64  OK    OK    OK  
nebbiolo2Linux (Ubuntu 20.04.5 LTS) / x86_64  OK    OK    OK  

Summary

Package: singleCellTK
Version: 2.9.0
Command: /home/biocbuild/bbs-3.17-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.17-bioc/R/site-library --timings singleCellTK_2.9.0.tar.gz
StartedAt: 2023-04-12 09:27:06 -0400 (Wed, 12 Apr 2023)
EndedAt: 2023-04-12 09:42:49 -0400 (Wed, 12 Apr 2023)
EllapsedTime: 943.2 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.17-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.17-bioc/R/site-library --timings singleCellTK_2.9.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.17-bioc/meat/singleCellTK.Rcheck’
* using R Under development (unstable) (2023-02-14 r83833)
* using platform: x86_64-pc-linux-gnu (64-bit)
* R was compiled by
    gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
    GNU Fortran (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
* running under: Ubuntu 20.04.6 LTS
* using session charset: UTF-8
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.9.0’
* 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   1.6Mb
    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 loading without being on the library search path ... 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   39.254  0.616  39.866
plotDoubletFinderResults 28.471  0.272  28.739
runDoubletFinder         23.960  0.223  24.184
importExampleData        21.808  1.633  24.267
runScDblFinder           21.609  0.649  22.257
plotScdsHybridResults    12.433  0.121  11.224
plotBatchCorrCompare     12.262  0.168  12.415
plotUMAP                 10.209  0.040  10.243
plotBcdsResults           9.630  0.332   8.662
runDecontX                9.788  0.104   9.892
plotDecontXResults        8.014  0.076   8.090
plotEmptyDropsResults     7.804  0.012   7.816
plotEmptyDropsScatter     7.558  0.016   7.573
plotCxdsResults           7.354  0.020   7.370
runUMAP                   7.202  0.133   7.329
runEmptyDrops             6.540  0.004   6.544
detectCellOutlier         6.131  0.207   6.340
plotTSCANClusterDEG       5.982  0.012   5.994
* 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 ...
  ‘singleCellTK.Rmd’ using ‘UTF-8’... OK
 NONE
* checking re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  ‘/home/biocbuild/bbs-3.17-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.



Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.17-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.17-bioc/R/site-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 Under development (unstable) (2023-02-14 r83833) -- "Unsuffered Consequences"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (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 
  0.197   0.042   0.224 

singleCellTK.Rcheck/tests/testthat.Rout


R Under development (unstable) (2023-02-14 r83833) -- "Unsuffered Consequences"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (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 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


Attaching package: 'DelayedArray'

The following objects are masked from 'package:base':

    apply, rowsum, 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: 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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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 21 | SKIP 0 | PASS 221 ]

[ FAIL 0 | WARN 21 | SKIP 0 | PASS 221 ]
> 
> proc.time()
   user  system elapsed 
256.006   4.641 261.141 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0000.0020.002
SEG0.0020.0000.002
calcEffectSizes0.1580.0000.158
combineSCE1.5820.0361.618
computeZScore0.2790.0200.299
convertSCEToSeurat3.3460.1753.522
convertSeuratToSCE0.4530.0040.457
dedupRowNames0.0540.0000.054
detectCellOutlier6.1310.2076.340
diffAbundanceFET0.0490.0000.049
discreteColorPalette0.0080.0000.007
distinctColors0.0020.0000.002
downSampleCells0.7130.0190.733
downSampleDepth0.5340.0080.542
expData-ANY-character-method0.3140.0050.318
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3660.0190.386
expData-set0.3830.0030.387
expData0.3040.0010.304
expDataNames-ANY-method0.3280.0000.327
expDataNames0.3130.0000.313
expDeleteDataTag0.0380.0000.038
expSetDataTag0.0270.0000.027
expTaggedData0.0290.0000.029
exportSCE0.0260.0000.026
exportSCEtoAnnData0.0960.0030.100
exportSCEtoFlatFile0.1000.0040.104
featureIndex0.0420.0000.042
generateSimulatedData0.0510.0010.051
getBiomarker0.0490.0030.053
getDEGTopTable0.8610.0270.889
getDiffAbundanceResults0.0380.0050.042
getEnrichRResult0.3710.0282.128
getFindMarkerTopTable3.9660.0854.052
getMSigDBTable0.0030.0000.004
getPathwayResultNames0.0260.0000.026
getSampleSummaryStatsTable0.4040.0040.408
getSoupX0.3940.0040.397
getTSCANResults1.9600.0482.008
getTopHVG0.8500.0120.863
importAnnData0.0010.0000.002
importBUStools0.2700.0040.275
importCellRanger1.1640.0321.197
importCellRangerV2Sample0.2950.0080.302
importCellRangerV3Sample0.4250.0040.430
importDropEst0.3340.0040.339
importExampleData21.808 1.63324.267
importGeneSetsFromCollection0.7780.0080.786
importGeneSetsFromGMT0.0720.0000.072
importGeneSetsFromList0.1320.0000.132
importGeneSetsFromMSigDB4.0180.2484.266
importMitoGeneSet0.0580.0000.057
importOptimus0.0020.0000.001
importSEQC0.2770.0000.277
importSTARsolo0.2950.0000.295
iterateSimulations0.3610.0040.365
listSampleSummaryStatsTables0.5260.0080.534
mergeSCEColData0.4900.0000.491
mouseBrainSubsetSCE0.0280.0000.028
msigdb_table0.0020.0000.002
plotBarcodeRankDropsResults1.0180.0241.042
plotBarcodeRankScatter0.8190.0080.827
plotBatchCorrCompare12.262 0.16812.415
plotBatchVariance0.3430.0200.363
plotBcdsResults9.6300.3328.662
plotClusterAbundance1.1110.0601.171
plotCxdsResults7.3540.0207.370
plotDEGHeatmap2.8640.0122.878
plotDEGRegression3.8610.0323.888
plotDEGViolin4.1890.1324.315
plotDEGVolcano0.9880.0000.988
plotDecontXResults8.0140.0768.090
plotDimRed0.2650.0000.266
plotDoubletFinderResults28.471 0.27228.739
plotEmptyDropsResults7.8040.0127.816
plotEmptyDropsScatter7.5580.0167.573
plotFindMarkerHeatmap4.5410.0154.558
plotMASTThresholdGenes1.4790.0121.492
plotPCA0.4800.0080.488
plotPathway0.8950.0050.899
plotRunPerCellQCResults1.5110.0031.515
plotSCEBarAssayData0.1730.0010.173
plotSCEBarColData0.1560.0000.156
plotSCEBatchFeatureMean0.2560.0000.259
plotSCEDensity0.2460.0030.250
plotSCEDensityAssayData0.1810.0080.189
plotSCEDensityColData0.3190.0080.327
plotSCEDimReduceColData0.8530.0080.861
plotSCEDimReduceFeatures0.4610.0000.461
plotSCEHeatmap0.9090.0000.909
plotSCEScatter0.3980.0000.398
plotSCEViolin0.2540.0080.262
plotSCEViolinAssayData0.2890.0000.289
plotSCEViolinColData0.3490.0040.353
plotScDblFinderResults39.254 0.61639.866
plotScdsHybridResults12.433 0.12111.224
plotScrubletResults0.0260.0000.026
plotSeuratElbow0.0210.0040.024
plotSeuratHVG0.0250.0000.025
plotSeuratJackStraw0.0250.0000.025
plotSeuratReduction0.0260.0000.026
plotSoupXResults0.1940.0000.195
plotTSCANClusterDEG5.9820.0125.994
plotTSCANClusterPseudo2.7690.0202.789
plotTSCANDimReduceFeatures2.5320.0442.576
plotTSCANPseudotimeGenes2.3080.0002.308
plotTSCANPseudotimeHeatmap2.5570.0042.561
plotTSCANResults2.3700.0722.441
plotTSNE0.540.000.54
plotTopHVG0.5350.0040.540
plotUMAP10.209 0.04010.243
readSingleCellMatrix0.0050.0030.007
reportCellQC0.2910.0000.291
reportDropletQC0.0360.0000.035
reportQCTool0.3120.0000.312
retrieveSCEIndex0.0450.0000.045
runBBKNN0.0010.0000.000
runBarcodeRankDrops0.6540.0040.657
runBcds3.3820.0762.088
runCellQC0.3020.0000.302
runComBatSeq0.7590.0320.790
runCxds0.7850.0560.841
runCxdsBcdsHybrid3.3050.0552.070
runDEAnalysis0.7280.0000.728
runDecontX9.7880.1049.892
runDimReduce0.5380.0000.538
runDoubletFinder23.960 0.22324.184
runDropletQC0.0260.0000.026
runEmptyDrops6.5400.0046.544
runEnrichR0.2970.0481.718
runFastMNN1.7450.1841.928
runFeatureSelection0.2150.0040.220
runFindMarker3.3130.2163.528
runGSVA0.7020.0400.743
runHarmony0.0350.0040.040
runKMeans0.4610.0440.505
runLimmaBC0.0760.0080.084
runMNNCorrect0.5410.0320.573
runModelGeneVar0.4520.0160.468
runNormalization0.6350.0320.667
runPerCellQC0.4750.0160.491
runSCANORAMA000
runSCMerge0.0050.0000.005
runScDblFinder21.609 0.64922.257
runScranSNN0.7120.0830.796
runScrublet0.0240.0000.023
runSeuratFindClusters0.0240.0000.023
runSeuratFindHVG0.5420.0160.557
runSeuratHeatmap0.0240.0000.024
runSeuratICA0.0240.0000.024
runSeuratJackStraw0.0250.0000.024
runSeuratNormalizeData0.0260.0040.029
runSeuratPCA0.0240.0000.024
runSeuratSCTransform3.1890.2273.418
runSeuratScaleData0.0270.0000.027
runSeuratUMAP0.0260.0000.026
runSingleR0.0370.0040.042
runSoupX0.1970.0000.197
runTSCAN1.5560.0081.563
runTSCANClusterDEAnalysis1.6390.0081.647
runTSCANDEG1.4600.0081.467
runTSNE0.8780.0000.879
runUMAP7.2020.1337.329
runVAM0.5360.0080.544
runZINBWaVE0.0040.0000.004
sampleSummaryStats0.2910.0040.295
scaterCPM0.1390.0040.143
scaterPCA0.4320.0120.443
scaterlogNormCounts0.2430.0120.256
sce0.0250.0000.025
sctkListGeneSetCollections0.0800.0000.079
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment0.0000.0000.001
setRowNames0.0830.0000.083
setSCTKDisplayRow0.5940.0040.598
singleCellTK000
subDiffEx0.6720.0160.688
subsetSCECols0.1830.0000.183
subsetSCERows0.4310.0000.431
summarizeSCE0.0560.0040.060
trimCounts0.2630.0200.284