Back to Multiple platform build/check report for BioC 3.18:   simplified   long
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This page was generated on 2024-03-04 11:37:52 -0500 (Mon, 04 Mar 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.3.2 Patched (2023-11-13 r85521) -- "Eye Holes" 4692
palomino4Windows Server 2022 Datacenterx644.3.2 (2023-10-31 ucrt) -- "Eye Holes" 4445
lconwaymacOS 12.7.1 Montereyx86_644.3.2 Patched (2023-11-01 r85457) -- "Eye Holes" 4466
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 1971/2266HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.12.2  (landing page)
Joshua David Campbell
Snapshot Date: 2024-03-03 14:05:05 -0500 (Sun, 03 Mar 2024)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: RELEASE_3_18
git_last_commit: 14c92130
git_last_commit_date: 2024-02-05 14:45:10 -0500 (Mon, 05 Feb 2024)
nebbiolo2Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino4Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
lconwaymacOS 12.7.1 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson1macOS 13.6.1 Ventura / arm64see weekly results here

CHECK results for singleCellTK on lconway


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: 2.12.2
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_2.12.2.tar.gz
StartedAt: 2024-03-03 23:32:26 -0500 (Sun, 03 Mar 2024)
EndedAt: 2024-03-03 23:49:05 -0500 (Sun, 03 Mar 2024)
EllapsedTime: 999.1 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_2.12.2.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.18-bioc/meat/singleCellTK.Rcheck’
* using R version 4.3.2 Patched (2023-11-01 r85457)
* 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 12.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.12.2’
* 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  6.8Mb
  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
plotDoubletFinderResults 34.808  0.301  35.280
runDoubletFinder         31.910  0.274  32.360
plotScDblFinderResults   29.548  0.881  30.601
runScDblFinder           20.396  0.485  20.973
importExampleData        18.553  2.158  21.308
plotBatchCorrCompare     11.684  0.151  11.897
plotScdsHybridResults     9.709  0.310  10.091
runDecontX                8.810  0.132   8.980
plotBcdsResults           8.161  0.209   8.414
plotDecontXResults        8.233  0.094   8.367
plotUMAP                  6.991  0.108   7.140
runUMAP                   6.755  0.090   6.864
plotCxdsResults           6.509  0.085   6.622
plotEmptyDropsScatter     6.438  0.060   6.541
plotEmptyDropsResults     6.329  0.051   6.415
plotTSCANClusterDEG       6.073  0.128   6.239
runEmptyDrops             6.149  0.049   6.229
runSeuratSCTransform      5.845  0.159   6.045
detectCellOutlier         5.717  0.153   5.897
plotDEGViolin             5.082  0.110   5.215
plotFindMarkerHeatmap     4.920  0.047   5.130
getEnrichRResult          0.362  0.047   8.270
* 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-3.18-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 4.3.2 Patched (2023-11-01 r85457) -- "Eye Holes"
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 
  0.228   0.081   0.302 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.3.2 Patched (2023-11-01 r85457) -- "Eye Holes"
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

Loading required package: SparseArray

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
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
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: 9849

Running Louvain algorithm...
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
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 22 | SKIP 0 | PASS 223 ]

[ FAIL 0 | WARN 22 | SKIP 0 | PASS 223 ]
> 
> proc.time()
   user  system elapsed 
282.466   7.436 294.115 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0020.0010.004
SEG0.0020.0030.005
calcEffectSizes0.3030.0090.314
combineSCE1.9780.0292.013
computeZScore0.2830.0140.301
convertSCEToSeurat4.6950.2414.960
convertSeuratToSCE0.5600.0180.582
dedupRowNames0.0650.0040.069
detectCellOutlier5.7170.1535.897
diffAbundanceFET0.0760.0070.084
discreteColorPalette0.0080.0010.009
distinctColors0.0030.0010.004
downSampleCells0.7470.1040.855
downSampleDepth0.6500.0490.703
expData-ANY-character-method0.3640.0100.376
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.4260.0110.440
expData-set0.3950.0110.408
expData0.3480.0240.375
expDataNames-ANY-method0.3090.0070.316
expDataNames0.3370.0110.350
expDeleteDataTag0.0420.0050.048
expSetDataTag0.0310.0050.035
expTaggedData0.0330.0040.037
exportSCE0.0260.0040.031
exportSCEtoAnnData0.0970.0020.101
exportSCEtoFlatFile0.0980.0050.105
featureIndex0.0490.0040.053
generateSimulatedData0.0660.0070.073
getBiomarker0.0590.0080.067
getDEGTopTable0.9670.0441.015
getDiffAbundanceResults0.0540.0040.058
getEnrichRResult0.3620.0478.270
getFindMarkerTopTable4.0070.0684.098
getMSigDBTable0.0040.0030.007
getPathwayResultNames0.0280.0040.033
getSampleSummaryStatsTable0.3350.0060.342
getSoupX000
getTSCANResults2.1050.0582.174
getTopHVG1.1670.0221.196
importAnnData0.0010.0000.001
importBUStools0.3160.0070.328
importCellRanger1.3550.0691.448
importCellRangerV2Sample0.2480.0030.251
importCellRangerV3Sample0.4770.0190.504
importDropEst0.3690.0060.379
importExampleData18.553 2.15821.308
importGeneSetsFromCollection0.8420.1390.991
importGeneSetsFromGMT0.0770.0060.083
importGeneSetsFromList0.1350.0060.142
importGeneSetsFromMSigDB3.6010.1793.798
importMitoGeneSet0.0530.0070.060
importOptimus0.0020.0000.002
importSEQC0.3090.0210.333
importSTARsolo0.3480.0290.381
iterateSimulations0.4740.0230.499
listSampleSummaryStatsTables0.3980.0090.408
mergeSCEColData0.5660.0280.600
mouseBrainSubsetSCE0.0430.0040.047
msigdb_table0.0020.0020.004
plotBarcodeRankDropsResults0.9640.0300.999
plotBarcodeRankScatter0.9320.0140.952
plotBatchCorrCompare11.684 0.15111.897
plotBatchVariance0.3400.0190.362
plotBcdsResults8.1610.2098.414
plotBubble1.2350.0201.264
plotClusterAbundance0.9850.0131.003
plotCxdsResults6.5090.0856.622
plotDEGHeatmap3.0480.1133.176
plotDEGRegression3.7520.0543.818
plotDEGViolin5.0820.1105.215
plotDEGVolcano1.2400.0271.280
plotDecontXResults8.2330.0948.367
plotDimRed0.3290.0110.347
plotDoubletFinderResults34.808 0.30135.280
plotEmptyDropsResults6.3290.0516.415
plotEmptyDropsScatter6.4380.0606.541
plotFindMarkerHeatmap4.9200.0475.130
plotMASTThresholdGenes1.7910.0381.837
plotPCA0.6490.0160.668
plotPathway0.9530.0160.973
plotRunPerCellQCResults2.4470.0242.479
plotSCEBarAssayData0.2240.0080.234
plotSCEBarColData0.1620.0060.169
plotSCEBatchFeatureMean0.3010.0060.307
plotSCEDensity0.2390.0090.248
plotSCEDensityAssayData0.1690.0070.176
plotSCEDensityColData0.2440.0090.254
plotSCEDimReduceColData0.8810.0170.903
plotSCEDimReduceFeatures0.4560.0110.471
plotSCEHeatmap0.7880.0120.805
plotSCEScatter0.4160.0100.431
plotSCEViolin0.2930.0070.300
plotSCEViolinAssayData0.3270.0090.338
plotSCEViolinColData0.2840.0080.292
plotScDblFinderResults29.548 0.88130.601
plotScanpyDotPlot0.0280.0040.032
plotScanpyEmbedding0.0280.0030.033
plotScanpyHVG0.0310.0050.038
plotScanpyHeatmap0.0290.0050.035
plotScanpyMarkerGenes0.0280.0040.031
plotScanpyMarkerGenesDotPlot0.0320.0060.038
plotScanpyMarkerGenesHeatmap0.0320.0040.037
plotScanpyMarkerGenesMatrixPlot0.0280.0060.035
plotScanpyMarkerGenesViolin0.0300.0050.034
plotScanpyMatrixPlot0.0320.0050.037
plotScanpyPCA0.0300.0040.036
plotScanpyPCAGeneRanking0.0280.0040.032
plotScanpyPCAVariance0.0310.0040.036
plotScanpyViolin0.0320.0040.042
plotScdsHybridResults 9.709 0.31010.091
plotScrubletResults0.0320.0060.040
plotSeuratElbow0.0330.0070.040
plotSeuratHVG0.0300.0070.036
plotSeuratJackStraw0.0290.0040.033
plotSeuratReduction0.0270.0030.031
plotSoupXResults0.0000.0010.001
plotTSCANClusterDEG6.0730.1286.239
plotTSCANClusterPseudo2.7680.0452.832
plotTSCANDimReduceFeatures2.8410.0382.896
plotTSCANPseudotimeGenes2.6880.0402.747
plotTSCANPseudotimeHeatmap2.8100.0462.878
plotTSCANResults2.8560.0472.925
plotTSNE0.6270.0180.651
plotTopHVG0.4790.0180.505
plotUMAP6.9910.1087.140
readSingleCellMatrix0.0060.0010.008
reportCellQC0.2100.0080.219
reportDropletQC0.0320.0050.037
reportQCTool0.1950.0060.206
retrieveSCEIndex0.0350.0050.040
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runBarcodeRankDrops0.5280.0100.540
runBcds1.9260.0511.991
runCellQC0.2000.0060.208
runClusterSummaryMetrics0.8950.0550.954
runComBatSeq0.5390.0240.568
runCxds0.6040.0130.619
runCxdsBcdsHybrid2.3150.0642.390
runDEAnalysis0.8680.0130.887
runDecontX8.8100.1328.980
runDimReduce0.5700.0110.585
runDoubletFinder31.910 0.27432.360
runDropletQC0.0260.0040.030
runEmptyDrops6.1490.0496.229
runEnrichR0.3160.0354.960
runFastMNN1.7720.0461.826
runFeatureSelection0.2560.0070.264
runFindMarker4.1220.0664.209
runGSVA0.8400.0320.876
runHarmony0.0450.0010.046
runKMeans0.4650.0130.481
runLimmaBC0.0960.0020.100
runMNNCorrect0.5660.0060.574
runModelGeneVar0.4040.0070.412
runNormalization2.3310.0362.374
runPerCellQC0.5970.0100.610
runSCANORAMA0.0000.0010.000
runSCMerge0.0050.0010.006
runScDblFinder20.396 0.48520.973
runScanpyFindClusters0.0310.0040.036
runScanpyFindHVG0.0310.0030.035
runScanpyFindMarkers0.0320.0030.036
runScanpyNormalizeData0.2470.0070.255
runScanpyPCA0.0200.0020.023
runScanpyScaleData0.0210.0020.024
runScanpyTSNE0.0210.0030.023
runScanpyUMAP0.0210.0030.024
runScranSNN0.8960.0180.920
runScrublet0.0240.0040.027
runSeuratFindClusters0.0300.0030.034
runSeuratFindHVG0.9450.1241.075
runSeuratHeatmap0.0280.0040.033
runSeuratICA0.0340.0030.037
runSeuratJackStraw0.0290.0070.036
runSeuratNormalizeData0.0330.0060.038
runSeuratPCA0.0310.0040.035
runSeuratSCTransform5.8450.1596.045
runSeuratScaleData0.0290.0050.034
runSeuratUMAP0.0290.0070.038
runSingleR0.0430.0020.045
runSoupX000
runTSCAN1.4360.0251.463
runTSCANClusterDEAnalysis1.8300.0351.874
runTSCANDEG1.7820.0241.813
runTSNE0.9760.0241.009
runUMAP6.7550.0906.864
runVAM0.6120.0110.627
runZINBWaVE0.0050.0010.006
sampleSummaryStats0.3490.0070.358
scaterCPM0.1530.0040.159
scaterPCA0.4650.0100.476
scaterlogNormCounts0.2760.0040.281
sce0.0230.0020.026
sctkListGeneSetCollections0.0980.0040.102
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv0.0000.0010.000
selectSCTKConda0.0000.0000.001
selectSCTKVirtualEnvironment000
setRowNames0.1020.0050.108
setSCTKDisplayRow0.4530.0100.467
singleCellTK000
subDiffEx0.6570.0300.692
subsetSCECols0.2160.0080.226
subsetSCERows0.4920.0110.505
summarizeSCE0.0880.0090.097
trimCounts0.2270.0070.235