Back to Multiple platform build/check report for BioC 3.17:   simplified   long
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This page was generated on 2023-10-16 11:35:47 -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 nebbiolo1


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: /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_2.10.0.tar.gz
StartedAt: 2023-10-16 01:09:46 -0400 (Mon, 16 Oct 2023)
EndedAt: 2023-10-16 01:23:47 -0400 (Mon, 16 Oct 2023)
EllapsedTime: 841.2 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_2.10.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.17-bioc/meat/singleCellTK.Rcheck’
* using R version 4.3.1 (2023-06-16)
* using platform: x86_64-pc-linux-gnu (64-bit)
* R was compiled by
    gcc (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0
    GNU Fortran (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0
* running under: Ubuntu 22.04.3 LTS
* using session charset: UTF-8
* 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  5.5Mb
  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 loading without being on the library search path ... 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   27.770  0.580  28.347
plotDoubletFinderResults 26.815  0.392  27.204
runScDblFinder           20.192  0.712  20.904
importExampleData        17.779  2.392  20.788
runDoubletFinder         18.344  0.028  18.372
plotBatchCorrCompare     14.406  0.644  15.044
plotBcdsResults          12.569  0.352  11.940
plotScdsHybridResults    10.456  0.108   9.672
plotDecontXResults        9.330  0.192   9.523
plotCxdsResults           7.474  0.223   7.695
plotEmptyDropsResults     6.768  0.040   6.808
plotEmptyDropsScatter     6.705  0.079   6.784
runDecontX                6.363  0.084   6.446
runEmptyDrops             6.309  0.004   6.312
plotUMAP                  5.973  0.152   6.121
runUMAP                   5.806  0.284   6.089
detectCellOutlier         5.497  0.184   5.681
plotTSCANClusterDEG       5.026  0.032   5.058
* 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 version 4.3.1 (2023-06-16) -- "Beagle Scouts"
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 
  0.161   0.024   0.175 

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

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

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**************************************************|
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[----|----|----|----|----|----|----|----|----|----|
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Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 20 | SKIP 0 | PASS 220 ]

[ FAIL 0 | WARN 20 | SKIP 0 | PASS 220 ]
> 
> proc.time()
   user  system elapsed 
238.112   8.657 248.050 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0000.003
SEG0.0030.0000.003
calcEffectSizes0.1480.0360.184
combineSCE1.5280.0431.573
computeZScore0.2810.0200.301
convertSCEToSeurat3.1890.2553.445
convertSeuratToSCE0.4270.0030.431
dedupRowNames0.0640.0000.064
detectCellOutlier5.4970.1845.681
diffAbundanceFET0.0480.0000.048
discreteColorPalette0.0060.0000.006
distinctColors0.0000.0020.002
downSampleCells0.6310.0410.672
downSampleDepth0.4910.0120.503
expData-ANY-character-method0.2790.0110.290
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3480.0010.347
expData-set0.3640.0000.364
expData0.2950.0000.294
expDataNames-ANY-method0.3190.0110.330
expDataNames0.2880.0000.288
expDeleteDataTag0.0320.0040.036
expSetDataTag0.0260.0000.026
expTaggedData0.0260.0000.027
exportSCE0.0200.0030.024
exportSCEtoAnnData0.0970.0010.097
exportSCEtoFlatFile0.0910.0040.095
featureIndex0.0360.0000.037
generateSimulatedData0.0410.0030.045
getBiomarker0.0500.0010.050
getDEGTopTable0.8190.0240.842
getDiffAbundanceResults0.0380.0000.038
getEnrichRResult0.7790.0632.996
getFindMarkerTopTable3.4250.2363.661
getMSigDBTable0.0040.0000.005
getPathwayResultNames0.0180.0080.026
getSampleSummaryStatsTable0.3620.0160.378
getSoupX0.0000.0000.001
getTSCANResults1.7890.1321.922
getTopHVG0.8340.0400.875
importAnnData0.0020.0000.001
importBUStools0.2940.0080.302
importCellRanger1.1610.0601.223
importCellRangerV2Sample0.2490.0040.253
importCellRangerV3Sample0.4150.0280.444
importDropEst0.3710.0040.376
importExampleData17.779 2.39220.788
importGeneSetsFromCollection0.7820.0400.822
importGeneSetsFromGMT0.0780.0000.077
importGeneSetsFromList0.1400.0080.148
importGeneSetsFromMSigDB2.5810.2602.841
importMitoGeneSet0.0540.0040.058
importOptimus0.0020.0000.002
importSEQC0.4410.0920.534
importSTARsolo0.2800.0360.316
iterateSimulations0.3730.0200.392
listSampleSummaryStatsTables0.4900.0080.498
mergeSCEColData0.5430.0200.563
mouseBrainSubsetSCE0.0210.0080.029
msigdb_table0.0010.0000.002
plotBarcodeRankDropsResults1.0220.0641.085
plotBarcodeRankScatter0.7550.0440.799
plotBatchCorrCompare14.406 0.64415.044
plotBatchVariance0.3790.0230.402
plotBcdsResults12.569 0.35211.940
plotClusterAbundance1.2730.0241.296
plotCxdsResults7.4740.2237.695
plotDEGHeatmap3.1990.0713.270
plotDEGRegression4.1070.0524.152
plotDEGViolin4.8380.1284.959
plotDEGVolcano1.1530.0321.185
plotDecontXResults9.3300.1929.523
plotDimRed0.2660.0070.273
plotDoubletFinderResults26.815 0.39227.204
plotEmptyDropsResults6.7680.0406.808
plotEmptyDropsScatter6.7050.0796.784
plotFindMarkerHeatmap4.5550.0324.587
plotMASTThresholdGenes1.4890.0081.497
plotPCA0.5010.0000.501
plotPathway0.8240.0000.824
plotRunPerCellQCResults2.0840.0082.090
plotSCEBarAssayData0.1760.0000.176
plotSCEBarColData0.1280.0000.128
plotSCEBatchFeatureMean0.2160.0000.216
plotSCEDensity0.2010.0040.205
plotSCEDensityAssayData0.1570.0040.161
plotSCEDensityColData0.1950.0040.199
plotSCEDimReduceColData0.8960.0000.896
plotSCEDimReduceFeatures0.3510.0000.351
plotSCEHeatmap0.7870.0000.787
plotSCEScatter0.3420.0120.354
plotSCEViolin0.2180.0080.226
plotSCEViolinAssayData0.2410.0000.241
plotSCEViolinColData0.2270.0040.231
plotScDblFinderResults27.770 0.58028.347
plotScanpyDotPlot0.0270.0000.026
plotScanpyEmbedding0.0260.0000.026
plotScanpyHVG0.0260.0000.025
plotScanpyHeatmap0.0250.0000.025
plotScanpyMarkerGenes0.0260.0000.026
plotScanpyMarkerGenesDotPlot0.0260.0000.026
plotScanpyMarkerGenesHeatmap0.0270.0000.027
plotScanpyMarkerGenesMatrixPlot0.0270.0000.027
plotScanpyMarkerGenesViolin0.0280.0000.027
plotScanpyMatrixPlot0.0260.0000.027
plotScanpyPCA0.0250.0000.025
plotScanpyPCAGeneRanking0.0230.0030.026
plotScanpyPCAVariance0.0240.0040.028
plotScanpyViolin0.0280.0000.028
plotScdsHybridResults10.456 0.108 9.672
plotScrubletResults0.0240.0000.025
plotSeuratElbow0.0240.0000.023
plotSeuratHVG0.0230.0000.024
plotSeuratJackStraw0.0210.0030.023
plotSeuratReduction0.0240.0000.024
plotSoupXResults0.0010.0000.000
plotTSCANClusterDEG5.0260.0325.058
plotTSCANClusterPseudo2.0280.0442.071
plotTSCANDimReduceFeatures1.9730.0121.984
plotTSCANPseudotimeGenes1.9050.0121.917
plotTSCANPseudotimeHeatmap2.0290.0282.058
plotTSCANResults1.9140.0121.925
plotTSNE0.4540.0000.454
plotTopHVG0.3790.0040.383
plotUMAP5.9730.1526.121
readSingleCellMatrix0.0010.0040.005
reportCellQC0.170.000.17
reportDropletQC0.0260.0000.026
reportQCTool0.170.000.17
retrieveSCEIndex0.0290.0000.029
runBBKNN000
runBarcodeRankDrops0.4050.0040.409
runBcds2.3570.0161.466
runCellQC0.1920.0000.192
runComBatSeq0.4310.0040.435
runCxds0.5160.0040.519
runCxdsBcdsHybrid2.4400.0201.533
runDEAnalysis0.6770.0080.686
runDecontX6.3630.0846.446
runDimReduce0.4600.0040.464
runDoubletFinder18.344 0.02818.372
runDropletQC0.0240.0000.024
runEmptyDrops6.3090.0046.312
runEnrichR0.7090.0202.569
runFastMNN1.7770.5762.353
runFeatureSelection0.2100.0280.238
runFindMarker3.2860.4603.746
runGSVA0.6520.1920.844
runHarmony0.0390.0040.043
runKMeans0.4410.0680.509
runLimmaBC0.0740.0000.074
runMNNCorrect0.5020.0240.526
runModelGeneVar0.4360.0360.472
runNormalization0.5560.0400.596
runPerCellQC0.5590.0880.647
runSCANORAMA0.0010.0000.000
runSCMerge0.0050.0000.005
runScDblFinder20.192 0.71220.904
runScanpyFindClusters0.0220.0030.025
runScanpyFindHVG0.0240.0000.024
runScanpyFindMarkers0.0240.0000.024
runScanpyNormalizeData0.1850.0200.206
runScanpyPCA0.0210.0040.025
runScanpyScaleData0.0230.0000.023
runScanpyTSNE0.0230.0000.023
runScanpyUMAP0.0230.0000.023
runScranSNN0.6590.0640.722
runScrublet0.0250.0000.024
runSeuratFindClusters0.0190.0040.023
runSeuratFindHVG0.6390.0840.723
runSeuratHeatmap0.0240.0000.023
runSeuratICA0.0190.0040.023
runSeuratJackStraw0.0230.0000.023
runSeuratNormalizeData0.0230.0000.023
runSeuratPCA0.0220.0000.023
runSeuratSCTransform2.8050.3483.154
runSeuratScaleData0.0240.0000.025
runSeuratUMAP0.0160.0070.023
runSingleR0.0360.0000.036
runSoupX0.0010.0000.000
runTSCAN1.5280.0321.561
runTSCANClusterDEAnalysis1.5130.0201.534
runTSCANDEG1.3730.0201.393
runTSNE0.8390.0000.839
runUMAP5.8060.2846.089
runVAM0.5120.0080.519
runZINBWaVE0.0040.0000.005
sampleSummaryStats0.2650.0080.273
scaterCPM0.1330.0080.141
scaterPCA0.4250.0080.433
scaterlogNormCounts0.2380.0200.258
sce0.0230.0000.024
sctkListGeneSetCollections0.0750.0040.080
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv0.0000.0000.001
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0860.0020.089
setSCTKDisplayRow0.3950.0080.403
singleCellTK000
subDiffEx0.4760.0160.493
subsetSCECols0.1640.0000.164
subsetSCERows0.3810.0000.381
summarizeSCE0.0530.0040.057
trimCounts0.2280.0040.231