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This page was generated on 2023-09-28 11:37:56 -0400 (Thu, 28 Sep 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: 2023-09-27 14:00:15 -0400 (Wed, 27 Sep 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

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: 2023-09-28 07:43:14 -0400 (Thu, 28 Sep 2023)
EndedAt: 2023-09-28 08:18:03 -0400 (Thu, 28 Sep 2023)
EllapsedTime: 2088.7 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     46.721  0.949  64.837
runScDblFinder             35.873  0.471  46.252
plotDoubletFinderResults   33.822  0.243  44.658
importExampleData          24.558  2.551  35.413
runDoubletFinder           25.041  0.166  32.603
plotBatchCorrCompare       14.450  0.158  18.753
plotScdsHybridResults      12.795  0.173  17.342
plotTSCANClusterDEG        12.156  0.208  16.732
plotBcdsResults            12.049  0.206  15.396
plotDecontXResults         11.623  0.091  15.244
plotFindMarkerHeatmap      10.718  0.053  14.700
plotEmptyDropsResults      10.511  0.044  14.191
plotEmptyDropsScatter      10.440  0.052  14.397
plotDEGViolin              10.123  0.153  13.094
runEmptyDrops               9.836  0.073  12.723
plotCxdsResults             9.211  0.081  11.612
plotDEGRegression           8.689  0.088  11.221
runDecontX                  8.692  0.080  11.482
detectCellOutlier           8.102  0.179  10.746
runUMAP                     7.913  0.069  10.037
plotUMAP                    7.879  0.077  10.322
runFindMarker               7.299  0.073   9.186
getFindMarkerTopTable       7.181  0.077   9.489
runSeuratSCTransform        7.022  0.122   9.094
plotDEGHeatmap              6.663  0.131   8.676
convertSCEToSeurat          6.030  0.269   7.974
importGeneSetsFromMSigDB    6.027  0.201   8.095
plotTSCANPseudotimeHeatmap  5.084  0.041   6.911
plotTSCANClusterPseudo      5.038  0.045   6.956
plotTSCANDimReduceFeatures  5.006  0.039   6.883
plotRunPerCellQCResults     4.968  0.037   6.761
plotTSCANPseudotimeGenes    4.964  0.038   6.959
plotTSCANResults            4.768  0.039   6.275
getTSCANResults             3.871  0.056   5.162
runCxdsBcdsHybrid           3.840  0.059   5.084
runEnrichR                  0.620  0.047  67.589
* 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 
  0.363   0.121   0.479

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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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 19 | SKIP 0 | PASS 220 ]

[ FAIL 0 | WARN 19 | SKIP 0 | PASS 220 ]
> 
> proc.time()
   user  system elapsed 
433.711   9.250 576.846

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0040.0040.013
SEG0.0040.0050.012
calcEffectSizes0.4120.0150.566
combineSCE3.3340.1024.505
computeZScore0.4810.0160.634
convertSCEToSeurat6.0300.2697.974
convertSeuratToSCE0.9530.0241.329
dedupRowNames0.1130.0050.173
detectCellOutlier 8.102 0.17910.746
diffAbundanceFET0.0830.0040.112
discreteColorPalette0.0110.0010.016
distinctColors0.0040.0010.005
downSampleCells1.4080.1842.090
downSampleDepth1.0850.0461.460
expData-ANY-character-method0.6880.0100.926
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.7710.0101.035
expData-set0.7790.0251.039
expData0.7540.0781.147
expDataNames-ANY-method0.6690.0100.878
expDataNames0.6590.0120.849
expDeleteDataTag0.0610.0030.085
expSetDataTag0.0440.0040.068
expTaggedData0.0480.0030.069
exportSCE0.0390.0060.057
exportSCEtoAnnData0.1400.0050.193
exportSCEtoFlatFile0.1440.0050.196
featureIndex0.0640.0060.085
generateSimulatedData0.0790.0070.122
getBiomarker0.0960.0070.127
getDEGTopTable1.9150.0592.605
getDiffAbundanceResults0.0730.0020.097
getEnrichRResult0.6620.0542.368
getFindMarkerTopTable7.1810.0779.489
getMSigDBTable0.0080.0060.017
getPathwayResultNames0.0460.0060.066
getSampleSummaryStatsTable0.7310.0080.955
getSoupX0.0000.0000.001
getTSCANResults3.8710.0565.162
getTopHVG1.7170.0162.255
importAnnData0.0030.0010.004
importBUStools0.7640.0241.041
importCellRanger2.3520.0633.225
importCellRangerV2Sample0.5810.0050.734
importCellRangerV3Sample0.8540.0211.126
importDropEst0.7700.0381.031
importExampleData24.558 2.55135.413
importGeneSetsFromCollection1.5900.1592.209
importGeneSetsFromGMT0.1300.0070.170
importGeneSetsFromList0.2790.0090.362
importGeneSetsFromMSigDB6.0270.2018.095
importMitoGeneSet0.1160.0150.178
importOptimus0.0030.0010.008
importSEQC0.5570.0260.745
importSTARsolo0.6330.0580.933
iterateSimulations0.7800.0571.152
listSampleSummaryStatsTables0.8640.0101.168
mergeSCEColData1.0380.0351.387
mouseBrainSubsetSCE0.0470.0060.078
msigdb_table0.0020.0050.011
plotBarcodeRankDropsResults1.8940.0352.479
plotBarcodeRankScatter1.5800.0212.397
plotBatchCorrCompare14.450 0.15818.753
plotBatchVariance0.7430.0501.033
plotBcdsResults12.049 0.20615.396
plotClusterAbundance2.5720.0383.175
plotCxdsResults 9.211 0.08111.612
plotDEGHeatmap6.6630.1318.676
plotDEGRegression 8.689 0.08811.221
plotDEGViolin10.123 0.15313.094
plotDEGVolcano2.3230.0252.955
plotDecontXResults11.623 0.09115.244
plotDimRed0.6030.0080.869
plotDoubletFinderResults33.822 0.24344.658
plotEmptyDropsResults10.511 0.04414.191
plotEmptyDropsScatter10.440 0.05214.397
plotFindMarkerHeatmap10.718 0.05314.700
plotMASTThresholdGenes3.6000.0434.961
plotPCA1.1980.0151.646
plotPathway1.8170.0212.482
plotRunPerCellQCResults4.9680.0376.761
plotSCEBarAssayData0.3800.0080.508
plotSCEBarColData0.2930.0060.388
plotSCEBatchFeatureMean0.5720.0050.776
plotSCEDensity0.4720.0090.655
plotSCEDensityAssayData0.3620.0080.490
plotSCEDensityColData0.4610.0080.620
plotSCEDimReduceColData1.9080.0242.601
plotSCEDimReduceFeatures0.7980.0101.097
plotSCEHeatmap1.6850.0172.290
plotSCEScatter0.7880.0111.083
plotSCEViolin0.5170.0100.713
plotSCEViolinAssayData0.5610.0100.765
plotSCEViolinColData0.5170.0100.713
plotScDblFinderResults46.721 0.94964.837
plotScanpyDotPlot0.0450.0050.067
plotScanpyEmbedding0.0430.0040.061
plotScanpyHVG0.0410.0050.062
plotScanpyHeatmap0.0420.0030.057
plotScanpyMarkerGenes0.0420.0030.065
plotScanpyMarkerGenesDotPlot0.0420.0060.064
plotScanpyMarkerGenesHeatmap0.0410.0030.056
plotScanpyMarkerGenesMatrixPlot0.0410.0040.055
plotScanpyMarkerGenesViolin0.0410.0020.060
plotScanpyMatrixPlot0.0420.0060.061
plotScanpyPCA0.0410.0040.062
plotScanpyPCAGeneRanking0.0440.0040.059
plotScanpyPCAVariance0.0430.0040.061
plotScanpyViolin0.0410.0050.063
plotScdsHybridResults12.795 0.17317.342
plotScrubletResults0.0460.0080.073
plotSeuratElbow0.0450.0030.062
plotSeuratHVG0.0400.0040.057
plotSeuratJackStraw0.0410.0060.065
plotSeuratReduction0.0420.0040.064
plotSoupXResults000
plotTSCANClusterDEG12.156 0.20816.732
plotTSCANClusterPseudo5.0380.0456.956
plotTSCANDimReduceFeatures5.0060.0396.883
plotTSCANPseudotimeGenes4.9640.0386.959
plotTSCANPseudotimeHeatmap5.0840.0416.911
plotTSCANResults4.7680.0396.275
plotTSNE1.1640.0181.542
plotTopHVG0.8330.0191.109
plotUMAP 7.879 0.07710.322
readSingleCellMatrix0.0090.0020.014
reportCellQC0.3760.0080.500
reportDropletQC0.0400.0040.056
reportQCTool0.3820.0090.506
retrieveSCEIndex0.0500.0050.067
runBBKNN0.0000.0010.002
runBarcodeRankDrops0.8990.0101.193
runBcds3.7640.0614.976
runCellQC0.4040.0190.559
runComBatSeq0.9880.0371.345
runCxds1.1220.0411.526
runCxdsBcdsHybrid3.8400.0595.084
runDEAnalysis1.5360.0152.054
runDecontX 8.692 0.08011.482
runDimReduce1.0150.0101.322
runDoubletFinder25.041 0.16632.603
runDropletQC0.0410.0050.079
runEmptyDrops 9.836 0.07312.723
runEnrichR 0.620 0.04767.589
runFastMNN3.6970.0514.769
runFeatureSelection0.4330.0050.567
runFindMarker7.2990.0739.186
runGSVA1.5270.0181.972
runHarmony0.0790.0030.106
runKMeans0.8750.0121.124
runLimmaBC0.1650.0020.210
runMNNCorrect1.1020.0081.394
runModelGeneVar1.0040.0121.285
runNormalization1.1770.0131.499
runPerCellQC1.1950.0161.528
runSCANORAMA0.0000.0000.001
runSCMerge0.0070.0020.008
runScDblFinder35.873 0.47146.252
runScanpyFindClusters0.0420.0050.062
runScanpyFindHVG0.0420.0040.058
runScanpyFindMarkers0.0420.0050.061
runScanpyNormalizeData0.4430.0070.562
runScanpyPCA0.0420.0040.061
runScanpyScaleData0.0430.0040.062
runScanpyTSNE0.0430.0080.063
runScanpyUMAP0.0440.0050.061
runScranSNN1.5900.0212.033
runScrublet0.0430.0020.055
runSeuratFindClusters0.0460.0040.066
runSeuratFindHVG1.5360.1202.085
runSeuratHeatmap0.0430.0040.059
runSeuratICA0.0420.0050.062
runSeuratJackStraw0.0420.0050.061
runSeuratNormalizeData0.0440.0050.061
runSeuratPCA0.0440.0030.058
runSeuratSCTransform7.0220.1229.094
runSeuratScaleData0.0410.0050.062
runSeuratUMAP0.0400.0070.057
runSingleR0.0800.0050.108
runSoupX0.0010.0010.001
runTSCAN3.1550.0244.017
runTSCANClusterDEAnalysis3.5020.0394.589
runTSCANDEG3.3330.0264.122
runTSNE1.6910.0242.170
runUMAP 7.913 0.06910.037
runVAM1.2480.0141.664
runZINBWaVE0.0070.0020.011
sampleSummaryStats0.6610.0090.854
scaterCPM0.2400.0040.318
scaterPCA0.9330.0111.200
scaterlogNormCounts0.4880.0060.635
sce0.0380.0100.067
sctkListGeneSetCollections0.1670.0100.218
sctkPythonInstallConda0.0000.0010.001
sctkPythonInstallVirtualEnv0.0000.0000.001
selectSCTKConda0.0000.0010.001
selectSCTKVirtualEnvironment0.0000.0010.001
setRowNames0.1940.0080.253
setSCTKDisplayRow0.9650.0211.263
singleCellTK0.0000.0010.001
subDiffEx0.9780.0211.254
subsetSCECols0.3990.0110.521
subsetSCERows0.9440.0151.218
summarizeSCE0.1210.0050.159
trimCounts0.4190.0100.565