Back to Mac ARM64 build report for BioC 3.19
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This page was generated on 2024-05-07 11:32:45 -0400 (Tue, 07 May 2024).

HostnameOSArch (*)R versionInstalled pkgs
kjohnson3macOS 13.6.5 Venturaarm644.4.0 (2024-04-24) -- "Puppy Cup" 4461
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: 2024-05-06 14:00:02 -0400 (Mon, 06 May 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)
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:
- 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: 2024-05-07 01:01:27 -0400 (Tue, 07 May 2024)
EndedAt: 2024-05-07 01:06:49 -0400 (Tue, 07 May 2024)
EllapsedTime: 321.5 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-mac-arm64/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 13.6.5
* 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  6.8Mb
  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 13.378  0.057  13.452
plotScDblFinderResults   12.618  0.211  12.835
runDoubletFinder         12.741  0.033  12.784
runScDblFinder            8.142  0.119   8.263
importExampleData         6.671  0.466   7.598
* 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-mac-arm64/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 
  0.071   0.017   0.086 

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':

    anyDuplicated, aperm, append, as.data.frame, basename, cbind,
    colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
    get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    Position, rank, rbind, Reduce, 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':

    expand.grid, I, 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 
 90.707   1.764  94.113 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0010.0010.002
SEG0.0010.0010.002
calcEffectSizes0.0560.0020.057
combineSCE0.6460.0210.667
computeZScore0.0910.0020.093
convertSCEToSeurat1.4390.0501.489
convertSeuratToSCE0.1310.0050.136
dedupRowNames0.0190.0020.021
detectCellOutlier2.0960.0382.135
diffAbundanceFET0.0220.0010.023
discreteColorPalette0.0020.0000.003
distinctColors0.0010.0000.001
downSampleCells0.2260.0170.244
downSampleDepth0.1580.0090.168
expData-ANY-character-method0.0720.0010.073
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.0850.0010.086
expData-set0.0900.0020.091
expData0.0760.0010.077
expDataNames-ANY-method0.0760.0010.077
expDataNames0.0810.0010.082
expDeleteDataTag0.0150.0000.015
expSetDataTag0.0100.0010.011
expTaggedData0.0110.0000.011
exportSCE0.0100.0010.011
exportSCEtoAnnData0.0420.0010.043
exportSCEtoFlatFile0.0410.0020.042
featureIndex0.0140.0010.015
generateSimulatedData0.0200.0020.021
getBiomarker0.0210.0010.022
getDEGTopTable0.2520.0080.261
getDiffAbundanceResults0.0180.0010.019
getEnrichRResult0.1240.0161.703
getFindMarkerTopTable0.9780.0110.990
getMSigDBTable0.0010.0020.003
getPathwayResultNames0.0110.0020.013
getSampleSummaryStatsTable0.1020.0010.104
getSoupX000
getTSCANResults0.5580.0120.569
getTopHVG0.3510.0060.356
importAnnData0.0000.0000.001
importBUStools0.0710.0010.072
importCellRanger0.3060.0090.316
importCellRangerV2Sample0.0680.0010.069
importCellRangerV3Sample0.1080.0040.112
importDropEst0.1020.0000.103
importExampleData6.6710.4667.598
importGeneSetsFromCollection0.2510.0230.273
importGeneSetsFromGMT0.0390.0050.045
importGeneSetsFromList0.0340.0020.036
importGeneSetsFromMSigDB0.8840.0250.911
importMitoGeneSet0.0190.0020.022
importOptimus0.0010.0000.001
importSEQC0.0720.0010.073
importSTARsolo0.0710.0010.073
iterateSimulations0.1050.0040.108
listSampleSummaryStatsTables0.1320.0070.139
mergeSCEColData0.1490.0100.160
mouseBrainSubsetSCE0.0160.0010.018
msigdb_table0.0000.0010.002
plotBarcodeRankDropsResults0.2680.0040.271
plotBarcodeRankScatter0.2400.0030.243
plotBatchCorrCompare4.3820.0394.433
plotBatchVariance0.0960.0080.104
plotBcdsResults3.0650.0683.140
plotBubble0.3120.0130.324
plotClusterAbundance0.2500.0010.252
plotCxdsResults2.5100.0272.538
plotDEGHeatmap0.9040.0290.940
plotDEGRegression1.0700.0131.083
plotDEGViolin1.2480.0361.283
plotDEGVolcano0.3350.0050.341
plotDecontXResults2.9750.0233.000
plotDimRed0.0840.0010.086
plotDoubletFinderResults13.378 0.05713.452
plotEmptyDropsResults2.1160.0042.120
plotEmptyDropsScatter2.0860.0042.090
plotFindMarkerHeatmap1.2820.0101.292
plotMASTThresholdGenes0.4580.0120.469
plotPCA0.1380.0030.142
plotPathway0.2240.0030.229
plotRunPerCellQCResults0.6270.0080.636
plotSCEBarAssayData0.0690.0040.072
plotSCEBarColData0.0450.0030.048
plotSCEBatchFeatureMean0.0590.0010.059
plotSCEDensity0.0650.0030.068
plotSCEDensityAssayData0.0520.0030.055
plotSCEDensityColData0.0640.0030.066
plotSCEDimReduceColData0.2100.0050.216
plotSCEDimReduceFeatures0.1010.0050.106
plotSCEHeatmap0.1710.0030.174
plotSCEScatter0.1150.0040.119
plotSCEViolin0.0710.0030.074
plotSCEViolinAssayData0.0740.0030.077
plotSCEViolinColData0.0710.0040.075
plotScDblFinderResults12.618 0.21112.835
plotScanpyDotPlot0.0110.0010.012
plotScanpyEmbedding0.0110.0010.011
plotScanpyHVG0.0100.0010.010
plotScanpyHeatmap0.0100.0010.010
plotScanpyMarkerGenes0.0100.0010.010
plotScanpyMarkerGenesDotPlot0.0100.0010.010
plotScanpyMarkerGenesHeatmap0.0100.0010.010
plotScanpyMarkerGenesMatrixPlot0.0110.0010.011
plotScanpyMarkerGenesViolin0.0100.0010.012
plotScanpyMatrixPlot0.0110.0010.011
plotScanpyPCA0.0100.0010.011
plotScanpyPCAGeneRanking0.0100.0010.011
plotScanpyPCAVariance0.0100.0010.011
plotScanpyViolin0.0100.0010.011
plotScdsHybridResults3.4200.0753.495
plotScrubletResults0.0120.0010.013
plotSeuratElbow0.0120.0000.012
plotSeuratHVG0.0110.0000.012
plotSeuratJackStraw0.0110.0000.012
plotSeuratReduction0.0100.0000.011
plotSoupXResults000
plotTSCANClusterDEG1.4640.0291.493
plotTSCANClusterPseudo0.6100.0070.617
plotTSCANDimReduceFeatures0.5860.0080.595
plotTSCANPseudotimeGenes0.5690.0070.576
plotTSCANPseudotimeHeatmap0.6440.0090.653
plotTSCANResults0.5780.0080.587
plotTSNE0.1530.0030.157
plotTopHVG0.1530.0030.155
plotUMAP2.6980.0212.719
readSingleCellMatrix0.0020.0010.003
reportCellQC0.0530.0010.055
reportDropletQC0.0110.0010.013
reportQCTool0.0530.0020.054
retrieveSCEIndex0.0140.0010.016
runBBKNN000
runBarcodeRankDrops0.1250.0020.127
runBcds0.6430.0230.665
runCellQC0.0540.0010.055
runClusterSummaryMetrics0.1840.0080.192
runComBatSeq0.1700.0080.179
runCxds0.1400.0030.142
runCxdsBcdsHybrid0.6080.0180.627
runDEAnalysis0.1900.0020.193
runDecontX2.7470.0142.762
runDimReduce0.1280.0020.130
runDoubletFinder12.741 0.03312.784
runDropletQC0.0120.0020.015
runEmptyDrops2.0180.0022.020
runEnrichR0.1150.0141.599
runFastMNN0.5220.0300.553
runFeatureSelection0.0760.0020.078
runFindMarker0.9300.0120.942
runGSVA0.3060.0110.316
runHarmony0.0110.0000.011
runKMeans0.1310.0030.135
runLimmaBC0.0210.0010.021
runMNNCorrect0.1550.0010.156
runModelGeneVar0.1350.0020.137
runNormalization0.8570.0200.876
runPerCellQC0.1320.0030.135
runSCANORAMA000
runSCMerge0.0020.0000.002
runScDblFinder8.1420.1198.263
runScanpyFindClusters0.0120.0020.013
runScanpyFindHVG0.0110.0010.012
runScanpyFindMarkers0.0110.0010.013
runScanpyNormalizeData0.0590.0020.060
runScanpyPCA0.0110.0010.013
runScanpyScaleData0.0120.0010.013
runScanpyTSNE0.0120.0010.014
runScanpyUMAP0.0120.0020.014
runScranSNN0.2230.0150.237
runScrublet0.0130.0020.015
runSeuratFindClusters0.0120.0010.012
runSeuratFindHVG0.2390.0250.264
runSeuratHeatmap0.0120.0020.014
runSeuratICA0.0110.0020.012
runSeuratJackStraw0.0110.0010.013
runSeuratNormalizeData0.0110.0010.012
runSeuratPCA0.0110.0010.012
runSeuratSCTransform2.0460.0362.084
runSeuratScaleData0.0110.0000.012
runSeuratUMAP0.0110.0010.011
runSingleR0.0100.0010.011
runSoupX0.0000.0000.001
runTSCAN0.4040.0180.422
runTSCANClusterDEAnalysis0.4760.0240.500
runTSCANDEG0.4560.0180.476
runTSNE0.3260.0140.341
runUMAP2.7110.0182.731
runVAM0.1440.0020.146
runZINBWaVE0.0020.0010.002
sampleSummaryStats0.0840.0010.086
scaterCPM0.0570.0020.058
scaterPCA0.1700.0020.173
scaterlogNormCounts0.0830.0020.086
sce0.0100.0030.013
sctkListGeneSetCollections0.0240.0010.025
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0250.0030.028
setSCTKDisplayRow0.1320.0070.139
singleCellTK000
subDiffEx0.1400.0050.145
subsetSCECols0.0480.0020.050
subsetSCERows0.1090.0020.112
summarizeSCE0.0250.0010.027
trimCounts0.0820.0080.091