Back to Multiple platform build/check report for BioC 3.15
ABCDEFGHIJKLMNOPQR[S]TUVWXYZ

This page was generated on 2022-10-19 13:20:54 -0400 (Wed, 19 Oct 2022).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 20.04.5 LTS)x86_644.2.1 (2022-06-23) -- "Funny-Looking Kid" 4386
palomino3Windows Server 2022 Datacenterx644.2.1 (2022-06-23 ucrt) -- "Funny-Looking Kid" 4138
merida1macOS 10.14.6 Mojavex86_644.2.1 (2022-06-23) -- "Funny-Looking Kid" 4205
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 nebbiolo1


To the developers/maintainers of the singleCellTK package:
- Please 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 How and When does the builder pull? When will my changes propagate? for more information.
- Make sure to use the following settings in order to reproduce any error or warning you see on this page.

raw results

Package 1856/2140HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.6.0  (landing page)
Yichen Wang
Snapshot Date: 2022-10-18 13:55:19 -0400 (Tue, 18 Oct 2022)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: RELEASE_3_15
git_last_commit: b6fc536
git_last_commit_date: 2022-04-26 11:48:48 -0400 (Tue, 26 Apr 2022)
nebbiolo1Linux (Ubuntu 20.04.5 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 10.14.6 Mojave / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published

Summary

Package: singleCellTK
Version: 2.6.0
Command: /home/biocbuild/bbs-3.15-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.15-bioc/R/library --no-vignettes --timings singleCellTK_2.6.0.tar.gz
StartedAt: 2022-10-18 21:52:27 -0400 (Tue, 18 Oct 2022)
EndedAt: 2022-10-18 22:06:12 -0400 (Tue, 18 Oct 2022)
EllapsedTime: 824.8 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.15-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.15-bioc/R/library --no-vignettes --timings singleCellTK_2.6.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.15-bioc/meat/singleCellTK.Rcheck’
* using R version 4.2.1 (2022-06-23)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.6.0’
* 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  5.3Mb
  sub-directories of 1Mb or more:
    shiny   2.3Mb
* 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 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     26.569  0.492  26.786
plotDoubletFinderResults   24.193  0.196  24.382
runDoubletFinder           19.140  0.104  19.246
runScDblFinder             17.727  0.683  18.144
importExampleData          13.528  1.728  15.988
plotBatchCorrCompare       11.535  0.350  11.869
plotTSCANPseudotimeHeatmap 10.189  0.008  10.197
plotScdsHybridResults       9.547  0.100   8.738
plotBcdsResults             8.522  0.240   7.809
plotDecontXResults          7.267  0.188   7.458
runDecontX                  7.072  0.112   7.185
plotEmptyDropsResults       6.952  0.008   6.960
plotEmptyDropsScatter       6.702  0.004   6.706
plotUMAP                    6.571  0.064   6.627
runEmptyDrops               6.528  0.004   6.532
plotCxdsResults             6.286  0.068   6.345
plotMarkerDiffExp           6.145  0.004   6.149
detectCellOutlier           5.782  0.280   6.065
* 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
  ‘/home/biocbuild/bbs-3.15-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.



Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.15-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.15-bioc/R/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 4.2.1 (2022-06-23) -- "Funny-Looking Kid"
Copyright (C) 2022 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 
  0.172   0.035   0.191 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.2.1 (2022-06-23) -- "Funny-Looking Kid"
Copyright (C) 2022 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, 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':

    aperm, 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Uploading data to Enrichr... Done.
  Querying HDSigDB_Human_2021... Done.
Parsing results... Done.
Estimating GSVA scores for 34 gene sets.
Estimating ECDFs with Gaussian kernels

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |==                                                                    |   3%
  |                                                                            
  |====                                                                  |   6%
  |                                                                            
  |======                                                                |   9%
  |                                                                            
  |========                                                              |  12%
  |                                                                            
  |==========                                                            |  15%
  |                                                                            
  |============                                                          |  18%
  |                                                                            
  |==============                                                        |  21%
  |                                                                            
  |================                                                      |  24%
  |                                                                            
  |===================                                                   |  26%
  |                                                                            
  |=====================                                                 |  29%
  |                                                                            
  |=======================                                               |  32%
  |                                                                            
  |=========================                                             |  35%
  |                                                                            
  |===========================                                           |  38%
  |                                                                            
  |=============================                                         |  41%
  |                                                                            
  |===============================                                       |  44%
  |                                                                            
  |=================================                                     |  47%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |=====================================                                 |  53%
  |                                                                            
  |=======================================                               |  56%
  |                                                                            
  |=========================================                             |  59%
  |                                                                            
  |===========================================                           |  62%
  |                                                                            
  |=============================================                         |  65%
  |                                                                            
  |===============================================                       |  68%
  |                                                                            
  |=================================================                     |  71%
  |                                                                            
  |===================================================                   |  74%
  |                                                                            
  |======================================================                |  76%
  |                                                                            
  |========================================================              |  79%
  |                                                                            
  |==========================================================            |  82%
  |                                                                            
  |============================================================          |  85%
  |                                                                            
  |==============================================================        |  88%
  |                                                                            
  |================================================================      |  91%
  |                                                                            
  |==================================================================    |  94%
  |                                                                            
  |====================================================================  |  97%
  |                                                                            
  |======================================================================| 100%

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |======================================================================| 100%

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

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

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 17 | SKIP 0 | PASS 162 ]

[ FAIL 0 | WARN 17 | SKIP 0 | PASS 162 ]
> 
> proc.time()
   user  system elapsed 
209.636   7.884 282.346 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0000.003
SEG0.0030.0000.002
calcEffectSizes0.1540.0000.154
combineSCE1.5680.0431.612
computeZScore0.2770.0240.302
convertSCEToSeurat3.8230.3644.187
convertSeuratToSCE0.4090.0000.408
dedupRowNames0.0550.0000.056
detectCellOutlier5.7820.2806.065
diffAbundanceFET0.1150.0040.119
discreteColorPalette0.0070.0000.007
distinctColors0.0020.0000.002
downSampleCells0.6200.0440.665
downSampleDepth0.5320.0040.536
expData-ANY-character-method0.3180.0040.322
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3720.0470.420
expData-set0.3520.0160.368
expData0.3130.0120.325
expDataNames-ANY-method0.3270.0000.328
expDataNames0.3060.0040.311
expDeleteDataTag0.0370.0040.041
expSetDataTag0.0280.0000.027
expTaggedData0.0290.0000.028
exportSCE0.0220.0040.026
exportSCEtoAnnData0.1030.0000.103
exportSCEtoFlatFile0.0910.0080.098
featureIndex0.0360.0040.039
findMarkerDiffExp3.8460.0473.895
findMarkerTopTable3.3450.0283.374
generateSimulatedData0.0400.0040.044
getBiomarker0.0410.0070.049
getDEGTopTable0.5710.0090.579
getDiffAbundanceResults0.0360.0040.040
getEnrichRResult0.2590.0072.867
getMSigDBTable0.0020.0040.006
getSampleSummaryStatsTable0.3270.0090.335
getSoupX0.3710.0060.378
getTSNE0.2450.0080.253
getTopHVG0.2230.0040.228
getUMAP4.2510.1414.383
importAnnData0.0010.0000.001
importBUStools0.2390.0040.244
importCellRanger1.0420.0401.085
importCellRangerV2Sample0.2360.0040.241
importCellRangerV3Sample0.3380.0040.342
importDropEst0.3060.0360.343
importExampleData13.528 1.72815.988
importGeneSetsFromCollection0.7100.0720.783
importGeneSetsFromGMT0.0620.0000.063
importGeneSetsFromList0.1100.0040.115
importGeneSetsFromMSigDB2.2510.2192.470
importMitoGeneSet0.0550.0000.054
importOptimus0.0010.0000.001
importSEQC0.3070.0160.324
importSTARsolo0.2500.0000.251
iterateSimulations0.3460.0000.346
listSampleSummaryStatsTables0.4050.0040.410
mergeSCEColData0.4980.0000.497
mouseBrainSubsetSCE0.0230.0030.027
msigdb_table0.0020.0000.002
plotBarcodeRankDropsResults0.9880.0241.011
plotBarcodeRankScatter0.6320.0000.632
plotBatchCorrCompare11.535 0.35011.869
plotBatchVariance0.2450.0000.245
plotBcdsResults8.5220.2407.809
plotClusterAbundance0.9460.0440.990
plotClusterPseudo2.9180.0282.947
plotCxdsResults6.2860.0686.345
plotDEGHeatmap2.7420.0322.774
plotDEGRegression3.3590.0163.368
plotDEGViolin4.0630.0554.112
plotDEGVolcano0.9620.0000.963
plotDecontXResults7.2670.1887.458
plotDimRed0.2780.0320.309
plotDoubletFinderResults24.193 0.19624.382
plotEmptyDropsResults6.9520.0086.960
plotEmptyDropsScatter6.7020.0046.706
plotMASTThresholdGenes1.5440.0161.560
plotMarkerDiffExp6.1450.0046.149
plotPCA0.4470.0040.451
plotPathway0.7780.0070.786
plotRunPerCellQCResults0.0250.0000.024
plotSCEBarAssayData0.1390.0000.139
plotSCEBarColData0.1130.0000.113
plotSCEBatchFeatureMean0.1870.0000.188
plotSCEDensity0.2640.0080.272
plotSCEDensityAssayData0.1560.0000.156
plotSCEDensityColData0.2010.0000.201
plotSCEDimReduceColData0.7160.0000.717
plotSCEDimReduceFeatures0.3280.0070.336
plotSCEHeatmap0.7450.0040.748
plotSCEScatter0.3220.0040.326
plotSCEViolin0.2010.0080.209
plotSCEViolinAssayData0.2050.0080.213
plotSCEViolinColData0.1940.0040.198
plotScDblFinderResults26.569 0.49226.786
plotScdsHybridResults9.5470.1008.738
plotScrubletResults0.0240.0000.023
plotSeuratElbow0.0230.0000.023
plotSeuratHVG0.0230.0000.023
plotSeuratJackStraw0.0230.0000.023
plotSeuratReduction0.0190.0040.023
plotSoupXResults0.1730.0120.185
plotTSCANDEgenes2.8310.0202.851
plotTSCANPseudotimeGenes4.1440.0164.160
plotTSCANPseudotimeHeatmap10.189 0.00810.197
plotTSCANResults2.0740.0032.077
plotTSNE0.4790.0160.494
plotTopHVG0.4910.0120.504
plotUMAP6.5710.0646.627
readSingleCellMatrix0.0040.0000.004
reportCellQC0.1650.0000.164
reportDropletQC0.0240.0000.023
reportQCTool0.1670.0000.166
retrieveSCEIndex0.0290.0000.028
runBBKNN000
runBarcodeRankDrops0.4670.0240.490
runBcds2.2540.0161.396
runCellQC0.2310.0040.235
runComBatSeq0.4190.0000.419
runCxds0.5180.0000.519
runCxdsBcdsHybrid2.3880.0151.534
runDEAnalysis0.7540.0030.758
runDecontX7.0720.1127.185
runDimReduce0.9490.0000.949
runDoubletFinder19.140 0.10419.246
runDropletQC0.0240.0000.025
runEmptyDrops6.5280.0046.532
runEnrichR0.2870.0161.256
runFastMNN1.5620.0601.623
runFeatureSelection0.1920.0080.200
runGSVA0.7350.0080.743
runKMeans0.3980.0000.398
runLimmaBC0.0750.0040.079
runMNNCorrect0.5440.0000.544
runNormalization0.5660.0200.586
runPerCellQC0.5270.0040.531
runSCANORAMA0.0010.0000.001
runSCMerge0.0000.0040.004
runScDblFinder17.727 0.68318.144
runScranSNN0.4480.0440.492
runScrublet0.0220.0040.026
runSeuratFindClusters0.0240.0000.024
runSeuratFindHVG0.0230.0000.024
runSeuratHeatmap0.0240.0000.023
runSeuratICA0.0190.0040.024
runSeuratJackStraw0.0240.0000.024
runSeuratNormalizeData0.0250.0000.025
runSeuratPCA0.0240.0000.023
runSeuratSCTransform3.1060.2113.319
runSeuratScaleData0.0200.0040.025
runSeuratUMAP0.0230.0000.023
runSingleR0.0350.0000.035
runSoupX0.1770.0000.177
runTSCAN1.9920.0202.012
runTSCANClusterDEAnalysis2.3800.0242.405
runTSCANDEG2.0640.0322.096
runVAM0.5260.0160.542
runZINBWaVE0.0040.0000.004
sampleSummaryStats0.3310.0120.343
scaterCPM0.1340.0040.138
scaterPCA0.4810.0080.489
scaterlogNormCounts0.2460.0120.258
sce0.0230.0000.023
scranModelGeneVar0.2090.0040.213
sctkListGeneSetCollections0.2020.0040.206
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment0.0010.0000.001
setRowNames0.0790.0000.079
setSCTKDisplayRow0.4010.0040.405
singleCellTK000
subDiffEx0.4980.0160.514
subsetSCECols0.1830.0040.187
subsetSCERows0.4430.0240.467
summarizeSCE0.0530.0040.057
trimCounts0.2530.0080.261