Back to Multiple platform build/check report for BioC 3.20:   simplified   long
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This page was generated on 2024-07-24 11:42 -0400 (Wed, 24 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" 4284
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: 2024-07-23 14:00 -0400 (Tue, 23 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: 2024-07-24 01:00:43 -0400 (Wed, 24 Jul 2024)
EndedAt: 2024-07-24 01:06:10 -0400 (Wed, 24 Jul 2024)
EllapsedTime: 326.5 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_2.15.0.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 13.786  0.036  13.841
plotScDblFinderResults   12.781  0.182  12.975
runDoubletFinder         12.325  0.033  12.358
importExampleData         6.456  0.463   7.423
runScDblFinder            5.116  0.093   5.217
* 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 
  0.068   0.017   0.084 

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
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
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  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
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  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 390
Number of edges: 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 
 95.493   1.810  98.980 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0010.0010.002
SEG0.0010.0010.002
calcEffectSizes0.0620.0020.065
combineSCE0.4270.0080.434
computeZScore0.3880.0020.391
convertSCEToSeurat1.4990.0551.553
convertSeuratToSCE0.1410.0040.146
dedupRowNames0.0200.0010.022
detectCellOutlier2.0910.0402.135
diffAbundanceFET0.0250.0010.026
discreteColorPalette0.0020.0000.002
distinctColors0.0010.0000.000
downSampleCells0.2410.0230.263
downSampleDepth0.1740.0180.191
expData-ANY-character-method0.0860.0020.089
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.0960.0010.098
expData-set0.0940.0020.096
expData0.0930.0060.100
expDataNames-ANY-method0.0760.0020.077
expDataNames0.0790.0020.081
expDeleteDataTag0.0160.0010.016
expSetDataTag0.0100.0010.011
expTaggedData0.0110.0010.012
exportSCE0.0100.0020.011
exportSCEtoAnnData0.0430.0010.044
exportSCEtoFlatFile0.0450.0010.046
featureIndex0.0150.0020.016
generateSimulatedData0.0220.0010.023
getBiomarker0.0230.0020.024
getDEGTopTable0.2650.0080.273
getDiffAbundanceResults0.0220.0000.022
getEnrichRResult0.1280.0161.618
getFindMarkerTopTable0.9520.0120.965
getMSigDBTable0.0010.0020.003
getPathwayResultNames0.0120.0010.014
getSampleSummaryStatsTable0.0950.0020.097
getSoupX000
getTSCANResults0.6100.0140.623
getTopHVG0.3490.0060.355
importAnnData000
importBUStools0.0880.0020.090
importCellRanger0.3090.0100.320
importCellRangerV2Sample0.0700.0010.071
importCellRangerV3Sample0.1110.0050.116
importDropEst0.1100.0010.112
importExampleData6.4560.4637.423
importGeneSetsFromCollection0.2690.0250.293
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importGeneSetsFromList0.0410.0020.044
importGeneSetsFromMSigDB1.4340.0371.471
importMitoGeneSet0.0170.0030.020
importOptimus0.0010.0000.001
importSEQC0.0690.0050.074
importSTARsolo0.0680.0060.074
iterateSimulations0.1170.0080.126
listSampleSummaryStatsTables0.1190.0030.123
mergeSCEColData0.1250.0060.131
mouseBrainSubsetSCE0.0150.0030.019
msigdb_table0.0010.0010.001
plotBarcodeRankDropsResults0.2620.0080.270
plotBarcodeRankScatter0.2380.0020.241
plotBatchCorrCompare4.3730.0354.428
plotBatchVariance0.0960.0100.107
plotBcdsResults3.0670.0513.118
plotBubble0.3130.0070.321
plotClusterAbundance0.2760.0030.279
plotCxdsResults2.4850.0242.514
plotDEGHeatmap0.8750.0250.903
plotDEGRegression0.9920.0161.008
plotDEGViolin1.2410.0391.280
plotDEGVolcano0.3350.0030.339
plotDecontXResults2.9580.0162.975
plotDimRed0.0940.0020.097
plotDoubletFinderResults13.786 0.03613.841
plotEmptyDropsResults2.1320.0042.137
plotEmptyDropsScatter2.1380.0052.142
plotFindMarkerHeatmap1.3820.0081.389
plotMASTThresholdGenes0.4630.0080.470
plotPCA0.1640.0030.167
plotPathway0.2380.0030.243
plotRunPerCellQCResults0.6660.0060.673
plotSCEBarAssayData0.0700.0020.072
plotSCEBarColData0.0660.0030.068
plotSCEBatchFeatureMean0.0640.0010.065
plotSCEDensity0.0700.0020.073
plotSCEDensityAssayData0.0580.0020.060
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plotSCEDimReduceColData0.2320.0050.237
plotSCEDimReduceFeatures0.1120.0030.116
plotSCEHeatmap0.1990.0020.201
plotSCEScatter0.1050.0040.108
plotSCEViolin0.0790.0030.081
plotSCEViolinAssayData0.0830.0020.086
plotSCEViolinColData0.0920.0020.094
plotScDblFinderResults12.781 0.18212.975
plotScanpyDotPlot0.0130.0000.014
plotScanpyEmbedding0.0130.0010.014
plotScanpyHVG0.0120.0010.013
plotScanpyHeatmap0.0120.0010.013
plotScanpyMarkerGenes0.0120.0010.013
plotScanpyMarkerGenesDotPlot0.0120.0020.013
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plotScanpyMarkerGenesMatrixPlot0.0130.0010.013
plotScanpyMarkerGenesViolin0.0120.0010.012
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plotScanpyPCAGeneRanking0.0110.0000.011
plotScanpyPCAVariance0.0100.0010.010
plotScanpyViolin0.0110.0000.012
plotScdsHybridResults3.5020.0673.570
plotScrubletResults0.0130.0010.014
plotSeuratElbow0.0120.0010.012
plotSeuratHVG0.0110.0010.013
plotSeuratJackStraw0.0110.0010.012
plotSeuratReduction0.0110.0010.012
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plotTSCANClusterPseudo0.6300.0070.636
plotTSCANDimReduceFeatures0.6460.0050.652
plotTSCANPseudotimeGenes0.5880.0050.594
plotTSCANPseudotimeHeatmap0.7090.0070.715
plotTSCANResults0.5800.0050.585
plotTSNE0.1640.0030.167
plotTopHVG0.1530.0040.158
plotUMAP2.8060.0242.828
readSingleCellMatrix0.0020.0000.002
reportCellQC0.0560.0040.060
reportDropletQC0.0120.0010.014
reportQCTool0.0530.0010.054
retrieveSCEIndex0.0130.0010.014
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runBcds0.6410.0350.676
runCellQC0.0550.0040.060
runClusterSummaryMetrics0.2190.0150.234
runComBatSeq0.1620.0050.166
runCxds0.1460.0040.150
runCxdsBcdsHybrid0.6530.0190.673
runDEAnalysis0.2060.0020.207
runDecontX2.8430.0222.868
runDimReduce0.1370.0030.139
runDoubletFinder12.325 0.03312.358
runDropletQC0.0120.0010.012
runEmptyDrops2.0340.0032.038
runEnrichR0.1140.0171.520
runFastMNN0.5450.0260.570
runFeatureSelection0.0770.0020.079
runFindMarker0.9490.0110.960
runGSVA0.3000.0100.309
runHarmony0.0110.0000.011
runKMeans0.1300.0040.134
runLimmaBC0.0220.0010.023
runMNNCorrect0.1580.0020.161
runModelGeneVar0.1340.0020.136
runNormalization0.9510.0110.967
runPerCellQC0.1460.0030.149
runSCANORAMA000
runSCMerge0.0020.0000.002
runScDblFinder5.1160.0935.217
runScanpyFindClusters0.0120.0000.013
runScanpyFindHVG0.0110.0010.011
runScanpyFindMarkers0.0120.0000.012
runScanpyNormalizeData0.0600.0020.062
runScanpyPCA0.0120.0020.014
runScanpyScaleData0.0110.0010.012
runScanpyTSNE0.0110.0020.013
runScanpyUMAP0.0110.0020.013
runScranSNN0.2290.0150.244
runScrublet0.0120.0010.013
runSeuratFindClusters0.0120.0000.013
runSeuratFindHVG0.2480.0140.264
runSeuratHeatmap0.0120.0000.013
runSeuratICA0.0130.0000.013
runSeuratJackStraw0.0110.0000.012
runSeuratNormalizeData0.0110.0010.012
runSeuratPCA0.0120.0000.012
runSeuratSCTransform2.1590.0322.191
runSeuratScaleData0.0120.0010.013
runSeuratUMAP0.0110.0010.011
runSingleR0.0110.0010.012
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runTSCAN0.4300.0150.446
runTSCANClusterDEAnalysis0.4730.0060.485
runTSCANDEG0.4340.0050.439
runTSNE0.3310.0160.348
runUMAP2.8000.0232.832
runVAM0.1560.0070.162
runZINBWaVE0.0020.0000.002
sampleSummaryStats0.0880.0030.092
scaterCPM0.0600.0010.062
scaterPCA0.1910.0090.199
scaterlogNormCounts0.0970.0070.103
sce0.0120.0010.014
sctkListGeneSetCollections0.0260.0020.029
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0300.0020.032
setSCTKDisplayRow0.1500.0070.157
singleCellTK000
subDiffEx0.1560.0070.164
subsetSCECols0.0530.0030.057
subsetSCERows0.1180.0040.122
summarizeSCE0.0260.0020.028
trimCounts0.0790.0040.083