Back to Multiple platform build/check report for BioC 3.20:   simplified   long
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This page was generated on 2024-07-23 11:42 -0400 (Tue, 23 Jul 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.4.1 (2024-06-14) -- "Race for Your Life" 4688
palomino8Windows Server 2022 Datacenterx644.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 4280
lconwaymacOS 12.7.1 Montereyx86_644.4.1 (2024-06-14) -- "Race for Your Life" 4455
kjohnson3macOS 13.6.5 Venturaarm644.4.1 (2024-06-14) -- "Race for Your Life" 4404
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 1945/2248HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.15.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-07-22 14:00 -0400 (Mon, 22 Jul 2024)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: 4d7a515
git_last_commit_date: 2024-04-30 11:06:02 -0400 (Tue, 30 Apr 2024)
nebbiolo2Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino8Windows Server 2022 Datacenter / x64  ERROR    ERROR  skippedskipped
lconwaymacOS 12.7.1 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson3macOS 13.6.5 Ventura / arm64  OK    OK    OK    OK  UNNEEDED, same version is already published


CHECK results for singleCellTK on kjohnson3

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: 2.15.0
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_2.15.0.tar.gz
StartedAt: 2024-07-23 00:54:32 -0400 (Tue, 23 Jul 2024)
EndedAt: 2024-07-23 01:00:02 -0400 (Tue, 23 Jul 2024)
EllapsedTime: 330.6 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_2.15.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/singleCellTK.Rcheck’
* using R version 4.4.1 (2024-06-14)
* 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 13.6.7
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.15.0’
* 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  6.8Mb
  sub-directories of 1Mb or more:
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... 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 13.806  0.042  13.853
runDoubletFinder         12.494  0.044  12.540
plotScDblFinderResults   12.160  0.180  12.359
runScDblFinder            8.478  0.121   8.609
importExampleData         6.513  0.483   7.477
* 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-3.20-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 4.4.1 (2024-06-14) -- "Race for Your Life"
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 
  0.074   0.020   0.090 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.1 (2024-06-14) -- "Race for Your Life"
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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  |======================================================================| 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...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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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 
 95.521   1.845  99.140 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0010.0010.001
SEG0.0000.0010.002
calcEffectSizes0.0640.0030.067
combineSCE0.4360.0090.445
computeZScore0.3670.0040.372
convertSCEToSeurat1.4560.0581.517
convertSeuratToSCE0.1320.0060.138
dedupRowNames0.0200.0020.020
detectCellOutlier2.1310.0372.176
diffAbundanceFET0.0270.0010.028
discreteColorPalette0.0020.0000.002
distinctColors0.0010.0000.000
downSampleCells0.2440.0260.270
downSampleDepth0.1730.0100.183
expData-ANY-character-method0.0870.0020.088
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.0940.0020.096
expData-set0.0970.0030.101
expData0.0850.0050.091
expDataNames-ANY-method0.0740.0020.076
expDataNames0.0760.0010.077
expDeleteDataTag0.0140.0010.016
expSetDataTag0.0100.0010.011
expTaggedData0.0110.0000.011
exportSCE0.0090.0020.011
exportSCEtoAnnData0.0420.0010.043
exportSCEtoFlatFile0.0420.0010.043
featureIndex0.0150.0020.017
generateSimulatedData0.0200.0020.022
getBiomarker0.0220.0030.027
getDEGTopTable0.2600.0110.271
getDiffAbundanceResults0.0210.0010.022
getEnrichRResult0.1250.0171.731
getFindMarkerTopTable0.9320.0100.943
getMSigDBTable0.0020.0020.004
getPathwayResultNames0.0110.0010.013
getSampleSummaryStatsTable0.0870.0020.089
getSoupX000
getTSCANResults0.5360.0120.548
getTopHVG0.3290.0070.335
importAnnData0.0010.0000.001
importBUStools0.0740.0020.076
importCellRanger0.3050.0110.319
importCellRangerV2Sample0.0730.0010.074
importCellRangerV3Sample0.1160.0040.121
importDropEst0.0940.0010.096
importExampleData6.5130.4837.477
importGeneSetsFromCollection0.2620.0250.288
importGeneSetsFromGMT0.0260.0030.029
importGeneSetsFromList0.0400.0020.042
importGeneSetsFromMSigDB0.9270.0370.964
importMitoGeneSet0.0190.0030.022
importOptimus0.0000.0000.001
importSEQC0.0750.0010.076
importSTARsolo0.0760.0010.077
iterateSimulations0.1080.0040.112
listSampleSummaryStatsTables0.1240.0020.126
mergeSCEColData0.1640.0060.170
mouseBrainSubsetSCE0.0190.0040.023
msigdb_table0.0010.0010.002
plotBarcodeRankDropsResults0.2530.0060.259
plotBarcodeRankScatter0.2640.0060.273
plotBatchCorrCompare4.7290.0304.757
plotBatchVariance0.1070.0080.115
plotBcdsResults3.1200.0623.184
plotBubble0.2690.0030.273
plotClusterAbundance0.2950.0100.309
plotCxdsResults2.5300.0212.560
plotDEGHeatmap0.9200.0260.947
plotDEGRegression1.0710.0151.088
plotDEGViolin1.2690.0411.320
plotDEGVolcano0.3410.0050.347
plotDecontXResults3.2590.0203.289
plotDimRed0.0900.0020.092
plotDoubletFinderResults13.806 0.04213.853
plotEmptyDropsResults2.1390.0042.144
plotEmptyDropsScatter2.1270.0042.132
plotFindMarkerHeatmap1.3030.0111.317
plotMASTThresholdGenes0.4280.0090.437
plotPCA0.1700.0040.174
plotPathway0.2240.0070.231
plotRunPerCellQCResults0.6620.0060.668
plotSCEBarAssayData0.0890.0020.092
plotSCEBarColData0.0510.0020.052
plotSCEBatchFeatureMean0.0650.0010.066
plotSCEDensity0.0750.0040.078
plotSCEDensityAssayData0.0580.0030.060
plotSCEDensityColData0.0710.0030.074
plotSCEDimReduceColData0.2320.0050.237
plotSCEDimReduceFeatures0.1140.0040.117
plotSCEHeatmap0.2070.0030.210
plotSCEScatter0.1070.0040.110
plotSCEViolin0.0780.0030.081
plotSCEViolinAssayData0.0830.0040.087
plotSCEViolinColData0.0940.0020.096
plotScDblFinderResults12.160 0.18012.359
plotScanpyDotPlot0.0120.0010.013
plotScanpyEmbedding0.0110.0000.012
plotScanpyHVG0.0110.0010.011
plotScanpyHeatmap0.0110.0000.011
plotScanpyMarkerGenes0.0110.0000.012
plotScanpyMarkerGenesDotPlot0.0110.0010.011
plotScanpyMarkerGenesHeatmap0.0110.0000.011
plotScanpyMarkerGenesMatrixPlot0.0110.0010.011
plotScanpyMarkerGenesViolin0.0110.0010.011
plotScanpyMatrixPlot0.0110.0000.011
plotScanpyPCA0.0110.0010.011
plotScanpyPCAGeneRanking0.0110.0000.011
plotScanpyPCAVariance0.0110.0010.011
plotScanpyViolin0.0110.0010.011
plotScdsHybridResults3.5530.0993.658
plotScrubletResults0.0110.0000.013
plotSeuratElbow0.0100.0010.012
plotSeuratHVG0.0110.0010.012
plotSeuratJackStraw0.0110.0010.012
plotSeuratReduction0.0120.0010.013
plotSoupXResults0.0000.0000.001
plotTSCANClusterDEG1.4920.0351.534
plotTSCANClusterPseudo0.6360.0060.642
plotTSCANDimReduceFeatures0.6380.0070.645
plotTSCANPseudotimeGenes0.6090.0060.615
plotTSCANPseudotimeHeatmap0.6290.0070.635
plotTSCANResults0.5500.0060.556
plotTSNE0.1470.0040.150
plotTopHVG0.1600.0030.164
plotUMAP2.7550.0282.789
readSingleCellMatrix0.0020.0000.002
reportCellQC0.0530.0020.055
reportDropletQC0.0120.0010.012
reportQCTool0.0530.0010.055
retrieveSCEIndex0.0140.0020.017
runBBKNN000
runBarcodeRankDrops0.1270.0030.134
runBcds0.5970.0270.625
runCellQC0.0500.0040.054
runClusterSummaryMetrics0.1980.0190.218
runComBatSeq0.1480.0050.152
runCxds0.1310.0030.133
runCxdsBcdsHybrid0.5980.0190.616
runDEAnalysis0.1840.0030.187
runDecontX2.6510.0242.675
runDimReduce0.1220.0030.126
runDoubletFinder12.494 0.04412.540
runDropletQC0.0120.0010.013
runEmptyDrops2.0540.0032.058
runEnrichR0.1120.0151.468
runFastMNN0.5450.0290.574
runFeatureSelection0.0770.0010.079
runFindMarker0.9110.0110.925
runGSVA0.2910.0100.302
runHarmony0.0100.0010.011
runKMeans0.1260.0050.131
runLimmaBC0.0220.0010.022
runMNNCorrect0.1590.0020.161
runModelGeneVar0.1310.0030.135
runNormalization0.8490.0200.869
runPerCellQC0.1390.0020.142
runSCANORAMA000
runSCMerge0.0010.0000.002
runScDblFinder8.4780.1218.609
runScanpyFindClusters0.0110.0010.012
runScanpyFindHVG0.0110.0010.012
runScanpyFindMarkers0.0110.0000.011
runScanpyNormalizeData0.0580.0010.059
runScanpyPCA0.0110.0020.013
runScanpyScaleData0.0120.0010.013
runScanpyTSNE0.0120.0020.015
runScanpyUMAP0.0110.0010.012
runScranSNN0.2140.0140.227
runScrublet0.0120.0020.014
runSeuratFindClusters0.0110.0020.013
runSeuratFindHVG0.2380.0200.258
runSeuratHeatmap0.0110.0010.012
runSeuratICA0.0100.0000.011
runSeuratJackStraw0.0110.0010.013
runSeuratNormalizeData0.0110.0010.013
runSeuratPCA0.0110.0000.012
runSeuratSCTransform2.1050.0432.153
runSeuratScaleData0.0110.0020.013
runSeuratUMAP0.0110.0010.012
runSingleR0.0110.0010.012
runSoupX000
runTSCAN0.4100.0140.424
runTSCANClusterDEAnalysis0.4550.0110.467
runTSCANDEG0.4350.0070.441
runTSNE0.3110.0120.323
runUMAP2.6690.0242.701
runVAM0.1590.0020.162
runZINBWaVE0.0020.0000.002
sampleSummaryStats0.0870.0020.088
scaterCPM0.0600.0020.061
scaterPCA0.1990.0170.215
scaterlogNormCounts0.0980.0050.102
sce0.0120.0020.014
sctkListGeneSetCollections0.0290.0030.032
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0310.0020.033
setSCTKDisplayRow0.1470.0100.157
singleCellTK000
subDiffEx0.1620.0100.173
subsetSCECols0.0550.0050.060
subsetSCERows0.1210.0050.127
summarizeSCE0.0270.0020.029
trimCounts0.0840.0090.092