Back to Multiple platform build/check report for BioC 3.19:   simplified   long
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This page was generated on 2024-06-18 18:02 -0400 (Tue, 18 Jun 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 (2024-04-24) -- "Puppy Cup" 4758
palomino3Windows Server 2022 Datacenterx644.4.0 (2024-04-24 ucrt) -- "Puppy Cup" 4492
merida1macOS 12.7.4 Montereyx86_644.4.0 (2024-04-24) -- "Puppy Cup" 4464
kjohnson1macOS 13.6.6 Venturaarm644.4.0 (2024-04-24) -- "Puppy Cup" 4464
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 1992/2300HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.14.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-06-16 14:00 -0400 (Sun, 16 Jun 2024)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: RELEASE_3_19
git_last_commit: cd29b84
git_last_commit_date: 2024-04-30 11:06:02 -0400 (Tue, 30 Apr 2024)
nebbiolo1Linux (Ubuntu 22.04.3 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 12.7.4 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson1macOS 13.6.6 Ventura / arm64  OK    OK    OK    OK  UNNEEDED, same version is already published


CHECK results for singleCellTK on kjohnson1

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: 2.14.0
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_2.14.0.tar.gz
StartedAt: 2024-06-18 10:41:43 -0400 (Tue, 18 Jun 2024)
EndedAt: 2024-06-18 10:59:22 -0400 (Tue, 18 Jun 2024)
EllapsedTime: 1059.2 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_2.14.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.19-bioc/meat/singleCellTK.Rcheck’
* using R version 4.4.0 (2024-04-24)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Ventura 13.6.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.14.0’
* 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  6.9Mb
  sub-directories of 1Mb or more:
    R         1.1Mb
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code 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 whether 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 ... NOTE
checkRd: (-1) dedupRowNames.Rd:10: Lost braces
    10 | \item{x}{A matrix like or /linkS4class{SingleCellExperiment} object, on which
       |                                       ^
checkRd: (-1) dedupRowNames.Rd:14: Lost braces
    14 | /linkS4class{SingleCellExperiment} object. When set to \code{TRUE}, will
       |             ^
checkRd: (-1) dedupRowNames.Rd:22: Lost braces
    22 | By default, a matrix or /linkS4class{SingleCellExperiment} object
       |                                     ^
checkRd: (-1) dedupRowNames.Rd:24: Lost braces
    24 | When \code{x} is a /linkS4class{SingleCellExperiment} and \code{as.rowData}
       |                                ^
checkRd: (-1) plotBubble.Rd:42: Lost braces
    42 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runClusterSummaryMetrics.Rd:27: Lost braces
    27 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runEmptyDrops.Rd:66: Lost braces
    66 | provided \\linkS4class{SingleCellExperiment} object.
       |                       ^
checkRd: (-1) runSCMerge.Rd:44: Lost braces
    44 | construct pseudo-replicates. The length of code{kmeansK} needs to be the same
       |                                                ^
* 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 43.569  0.220  44.100
plotScDblFinderResults   38.969  0.682  39.835
runDoubletFinder         38.673  0.187  39.045
runScDblFinder           25.837  0.499  26.438
importExampleData        23.151  1.625  27.188
plotBatchCorrCompare     13.923  0.108  14.090
plotScdsHybridResults    10.325  0.143  10.605
plotBcdsResults           9.897  0.188  10.161
plotDecontXResults        9.620  0.070   9.743
runDecontX                8.738  0.053   8.840
runUMAP                   8.415  0.083   8.666
detectCellOutlier         7.864  0.116   8.133
plotCxdsResults           7.881  0.072   8.095
plotUMAP                  7.647  0.067   7.772
plotEmptyDropsResults     6.663  0.030   6.719
plotEmptyDropsScatter     6.634  0.029   6.681
runSeuratSCTransform      6.411  0.110   6.548
plotTSCANClusterDEG       5.735  0.084   5.842
runEmptyDrops             5.717  0.023   5.785
convertSCEToSeurat        4.883  0.187   5.102
* 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 ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 3 NOTEs
See
  ‘/Users/biocbuild/bbs-3.19-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/4.4-arm64/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 4.4.0 (2024-04-24) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

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 
  0.219   0.066   0.273 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.0 (2024-04-24) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

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, 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
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
No annotation package name available in the input data object.
Attempting to directly match identifiers in data to gene sets.
Estimating GSVA scores for 34 gene sets.
Estimating ECDFs with Gaussian kernels

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No annotation package name available in the input data object.
Attempting to directly match identifiers in data to gene sets.
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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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...
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'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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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 21 | SKIP 0 | PASS 224 ]

[ FAIL 0 | WARN 21 | SKIP 0 | PASS 224 ]
> 
> proc.time()
   user  system elapsed 
308.614   6.009 323.182 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0040.007
SEG0.0030.0030.007
calcEffectSizes0.2080.0180.231
combineSCE1.5140.0481.569
computeZScore0.3350.0080.349
convertSCEToSeurat4.8830.1875.102
convertSeuratToSCE0.5480.0090.560
dedupRowNames0.0680.0030.072
detectCellOutlier7.8640.1168.133
diffAbundanceFET0.0780.0060.085
discreteColorPalette0.0080.0010.009
distinctColors0.0020.0000.002
downSampleCells0.8200.0730.896
downSampleDepth0.6520.0380.694
expData-ANY-character-method0.3330.0070.341
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3940.0080.406
expData-set0.3690.0110.381
expData0.3400.0220.362
expDataNames-ANY-method0.3620.0290.392
expDataNames0.3220.0070.330
expDeleteDataTag0.0560.0030.059
expSetDataTag0.0380.0020.041
expTaggedData0.0410.0020.044
exportSCE0.0370.0050.041
exportSCEtoAnnData0.1420.0030.146
exportSCEtoFlatFile0.1350.0030.139
featureIndex0.0470.0050.052
generateSimulatedData0.0740.0060.081
getBiomarker0.0770.0070.084
getDEGTopTable0.9670.0391.013
getDiffAbundanceResults0.0680.0040.072
getEnrichRResult0.3740.0433.841
getFindMarkerTopTable3.4930.0613.582
getMSigDBTable0.0050.0040.009
getPathwayResultNames0.0380.0050.043
getSampleSummaryStatsTable0.3500.0050.357
getSoupX0.0000.0000.001
getTSCANResults2.030.052.10
getTopHVG1.3730.0211.400
importAnnData0.0020.0010.002
importBUStools0.2890.0050.297
importCellRanger1.2760.0401.325
importCellRangerV2Sample0.2610.0040.267
importCellRangerV3Sample0.4290.0170.448
importDropEst0.3270.0040.334
importExampleData23.151 1.62527.188
importGeneSetsFromCollection0.8530.0780.945
importGeneSetsFromGMT0.0840.0080.092
importGeneSetsFromList0.1390.0070.146
importGeneSetsFromMSigDB3.1700.1273.310
importMitoGeneSet0.0680.0090.079
importOptimus0.0020.0010.003
importSEQC0.3310.0100.342
importSTARsolo0.2760.0050.282
iterateSimulations0.3960.0110.410
listSampleSummaryStatsTables0.5030.0090.514
mergeSCEColData0.5140.0220.536
mouseBrainSubsetSCE0.0510.0070.058
msigdb_table0.0020.0030.004
plotBarcodeRankDropsResults0.9670.0190.988
plotBarcodeRankScatter0.9090.0110.922
plotBatchCorrCompare13.923 0.10814.090
plotBatchVariance0.3600.0190.385
plotBcdsResults 9.897 0.18810.161
plotBubble1.1690.0541.234
plotClusterAbundance0.7960.0100.839
plotCxdsResults7.8810.0728.095
plotDEGHeatmap3.1690.0963.277
plotDEGRegression3.8050.0483.869
plotDEGViolin4.5340.0854.644
plotDEGVolcano1.1900.0161.211
plotDecontXResults9.6200.0709.743
plotDimRed0.3340.0080.345
plotDoubletFinderResults43.569 0.22044.100
plotEmptyDropsResults6.6630.0306.719
plotEmptyDropsScatter6.6340.0296.681
plotFindMarkerHeatmap4.7260.0334.767
plotMASTThresholdGenes1.6740.0321.717
plotPCA0.5290.0130.545
plotPathway0.9120.0150.931
plotRunPerCellQCResults2.1980.0232.231
plotSCEBarAssayData0.2330.0100.244
plotSCEBarColData0.1650.0080.173
plotSCEBatchFeatureMean0.2230.0040.227
plotSCEDensity0.2930.0080.301
plotSCEDensityAssayData0.1940.0090.203
plotSCEDensityColData0.2370.0090.248
plotSCEDimReduceColData0.7820.0180.804
plotSCEDimReduceFeatures0.4500.0100.462
plotSCEHeatmap0.6890.0110.704
plotSCEScatter0.3750.0110.387
plotSCEViolin0.2620.0090.272
plotSCEViolinAssayData0.3410.0100.353
plotSCEViolinColData0.2640.0080.274
plotScDblFinderResults38.969 0.68239.835
plotScanpyDotPlot0.0360.0020.038
plotScanpyEmbedding0.0360.0020.038
plotScanpyHVG0.0360.0030.039
plotScanpyHeatmap0.0350.0040.039
plotScanpyMarkerGenes0.0340.0030.037
plotScanpyMarkerGenesDotPlot0.0350.0030.040
plotScanpyMarkerGenesHeatmap0.0360.0020.039
plotScanpyMarkerGenesMatrixPlot0.0370.0050.042
plotScanpyMarkerGenesViolin0.0360.0050.043
plotScanpyMatrixPlot0.0350.0030.038
plotScanpyPCA0.0330.0030.037
plotScanpyPCAGeneRanking0.0330.0030.036
plotScanpyPCAVariance0.0340.0040.039
plotScanpyViolin0.0340.0020.036
plotScdsHybridResults10.325 0.14310.605
plotScrubletResults0.0400.0070.047
plotSeuratElbow0.0340.0030.037
plotSeuratHVG0.0380.0040.042
plotSeuratJackStraw0.0340.0040.039
plotSeuratReduction0.0350.0060.042
plotSoupXResults000
plotTSCANClusterDEG5.7350.0845.842
plotTSCANClusterPseudo2.4320.0352.472
plotTSCANDimReduceFeatures2.4910.0322.534
plotTSCANPseudotimeGenes2.3240.0302.362
plotTSCANPseudotimeHeatmap2.5450.0312.583
plotTSCANResults2.3440.0292.382
plotTSNE0.5840.0130.601
plotTopHVG0.5830.0130.599
plotUMAP7.6470.0677.772
readSingleCellMatrix0.0070.0010.008
reportCellQC0.1920.0060.199
reportDropletQC0.0440.0050.049
reportQCTool0.1980.0060.205
retrieveSCEIndex0.0430.0060.050
runBBKNN000
runBarcodeRankDrops0.4550.0120.468
runBcds2.0740.0832.189
runCellQC0.2110.0100.221
runClusterSummaryMetrics0.8020.0380.844
runComBatSeq0.5290.0130.546
runCxds0.5280.0080.539
runCxdsBcdsHybrid2.1400.0942.250
runDEAnalysis0.8900.0210.913
runDecontX8.7380.0538.840
runDimReduce0.5240.0120.537
runDoubletFinder38.673 0.18739.045
runDropletQC0.0370.0050.042
runEmptyDrops5.7170.0235.785
runEnrichR0.3310.0263.646
runFastMNN1.6140.0431.669
runFeatureSelection0.2580.0100.268
runFindMarker3.7250.0523.785
runGSVA0.9610.0340.997
runHarmony0.0410.0010.042
runKMeans0.5020.0140.518
runLimmaBC0.0830.0020.085
runMNNCorrect0.6570.0100.677
runModelGeneVar0.5110.0070.521
runNormalization2.8660.0372.912
runPerCellQC0.5630.0130.577
runSCANORAMA000
runSCMerge0.0050.0010.006
runScDblFinder25.837 0.49926.438
runScanpyFindClusters0.0140.0030.018
runScanpyFindHVG0.0140.0020.016
runScanpyFindMarkers0.0140.0020.017
runScanpyNormalizeData0.1040.0050.110
runScanpyPCA0.0160.0050.021
runScanpyScaleData0.0140.0040.018
runScanpyTSNE0.0140.0040.019
runScanpyUMAP0.0140.0040.018
runScranSNN0.6630.0310.696
runScrublet0.0360.0010.039
runSeuratFindClusters0.0380.0010.039
runSeuratFindHVG0.8300.0580.887
runSeuratHeatmap0.0420.0030.045
runSeuratICA0.0380.0020.040
runSeuratJackStraw0.0390.0020.040
runSeuratNormalizeData0.0400.0020.041
runSeuratPCA0.0360.0020.038
runSeuratSCTransform6.4110.1106.548
runSeuratScaleData0.0370.0030.039
runSeuratUMAP0.0370.0030.040
runSingleR0.0400.0040.043
runSoupX0.0000.0000.001
runTSCAN1.4540.0601.519
runTSCANClusterDEAnalysis1.4070.0251.449
runTSCANDEG1.5880.0211.615
runTSNE1.1320.0191.170
runUMAP8.4150.0838.666
runVAM0.5690.0130.586
runZINBWaVE0.0050.0010.007
sampleSummaryStats0.3190.0080.331
scaterCPM0.1880.0060.196
scaterPCA0.7150.0170.735
scaterlogNormCounts0.3080.0060.315
sce0.0360.0070.043
sctkListGeneSetCollections0.0950.0070.101
sctkPythonInstallConda0.0010.0010.000
sctkPythonInstallVirtualEnv000
selectSCTKConda0.0010.0010.000
selectSCTKVirtualEnvironment000
setRowNames0.1850.0140.201
setSCTKDisplayRow0.4340.0100.448
singleCellTK0.0000.0010.000
subDiffEx0.5790.0350.617
subsetSCECols0.1980.0110.210
subsetSCERows0.4420.0160.459
summarizeSCE0.0880.0080.096
trimCounts0.2660.0170.286