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This page was generated on 2024-05-20 11:32:20 -0400 (Mon, 20 May 2024).

HostnameOSArch (*)R versionInstalled pkgs
kjohnson1macOS 13.6.6 Venturaarm644.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" 4381
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 1932/2233HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.15.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-05-18 09:00:01 -0400 (Sat, 18 May 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)
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:
- 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: 2024-05-20 03:03:52 -0400 (Mon, 20 May 2024)
EndedAt: 2024-05-20 03:21:26 -0400 (Mon, 20 May 2024)
EllapsedTime: 1053.6 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-mac-arm64/meat/singleCellTK.Rcheck’
* using R version 4.4.0 Patched (2024-04-24 r86482)
* 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 ‘2.15.0’
* 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 43.311  0.196  43.725
runDoubletFinder         39.289  0.176  39.587
plotScDblFinderResults   38.772  0.678  39.832
runScDblFinder           26.179  0.458  26.831
importExampleData        22.281  1.538  26.196
plotBatchCorrCompare     14.078  0.101  14.229
plotScdsHybridResults    11.151  0.166  11.359
plotBcdsResults           9.726  0.161   9.981
plotDecontXResults        9.710  0.058   9.793
runDecontX                8.821  0.045   8.907
plotUMAP                  7.983  0.055   8.097
plotCxdsResults           7.947  0.051   8.019
detectCellOutlier         7.771  0.117   7.925
runUMAP                   7.821  0.059   7.922
runSeuratSCTransform      6.762  0.077   6.864
plotEmptyDropsResults     6.640  0.027   6.702
plotEmptyDropsScatter     6.610  0.027   6.652
runEmptyDrops             6.348  0.022   6.383
plotTSCANClusterDEG       5.757  0.100   5.880
convertSCEToSeurat        4.854  0.182   5.052
* 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-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 Patched (2024-04-24 r86482) -- "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   0.063   0.308 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.0 Patched (2024-04-24 r86482) -- "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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  |======================================================================| 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
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 
305.511   5.595 318.855 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0040.007
SEG0.0030.0030.006
calcEffectSizes0.2110.0180.229
combineSCE1.4810.0451.533
computeZScore0.3040.0080.312
convertSCEToSeurat4.8540.1825.052
convertSeuratToSCE0.5270.0100.538
dedupRowNames0.0700.0050.074
detectCellOutlier7.7710.1177.925
diffAbundanceFET0.0770.0030.080
discreteColorPalette0.0080.0000.008
distinctColors0.0020.0000.003
downSampleCells0.8170.0690.895
downSampleDepth0.6400.0420.685
expData-ANY-character-method0.3390.0060.345
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3780.0070.386
expData-set0.3620.0070.370
expData0.3410.0220.364
expDataNames-ANY-method0.3580.0240.384
expDataNames0.3110.0070.319
expDeleteDataTag0.0490.0030.052
expSetDataTag0.0320.0030.035
expTaggedData0.0370.0030.041
exportSCE0.0350.0060.041
exportSCEtoAnnData0.1400.0050.145
exportSCEtoFlatFile0.1390.0030.142
featureIndex0.0530.0060.060
generateSimulatedData0.0750.0070.082
getBiomarker0.0800.0060.087
getDEGTopTable0.9130.0320.947
getDiffAbundanceResults0.0700.0030.079
getEnrichRResult0.3600.0384.273
getFindMarkerTopTable3.4700.0513.544
getMSigDBTable0.0060.0040.009
getPathwayResultNames0.0400.0060.045
getSampleSummaryStatsTable0.3680.0090.385
getSoupX0.0000.0010.000
getTSCANResults1.9820.0442.071
getTopHVG1.2950.0181.315
importAnnData0.0020.0000.002
importBUStools0.2840.0040.292
importCellRanger1.2410.0371.282
importCellRangerV2Sample0.2740.0040.278
importCellRangerV3Sample0.4290.0160.446
importDropEst0.3180.0040.323
importExampleData22.281 1.53826.196
importGeneSetsFromCollection0.8620.0790.944
importGeneSetsFromGMT0.0910.0060.098
importGeneSetsFromList0.1370.0060.145
importGeneSetsFromMSigDB3.1640.1123.287
importMitoGeneSet0.0690.0080.078
importOptimus0.0020.0000.002
importSEQC0.3310.0090.342
importSTARsolo0.2720.0040.277
iterateSimulations0.4080.0120.421
listSampleSummaryStatsTables0.5130.0100.524
mergeSCEColData0.5060.0210.529
mouseBrainSubsetSCE0.0530.0060.060
msigdb_table0.0020.0030.004
plotBarcodeRankDropsResults0.9910.0191.013
plotBarcodeRankScatter0.9000.0130.914
plotBatchCorrCompare14.078 0.10114.229
plotBatchVariance0.3500.0210.372
plotBcdsResults9.7260.1619.981
plotBubble1.1440.0341.189
plotClusterAbundance0.8710.0110.889
plotCxdsResults7.9470.0518.019
plotDEGHeatmap3.2130.0923.319
plotDEGRegression3.8760.0543.953
plotDEGViolin4.6280.1014.761
plotDEGVolcano1.2510.0161.270
plotDecontXResults9.7100.0589.793
plotDimRed0.3250.0080.335
plotDoubletFinderResults43.311 0.19643.725
plotEmptyDropsResults6.6400.0276.702
plotEmptyDropsScatter6.6100.0276.652
plotFindMarkerHeatmap4.8170.0354.912
plotMASTThresholdGenes1.6700.0311.714
plotPCA0.5390.0130.555
plotPathway0.8940.0120.911
plotRunPerCellQCResults2.2580.0212.289
plotSCEBarAssayData0.2390.0090.250
plotSCEBarColData0.1730.0060.180
plotSCEBatchFeatureMean0.2310.0030.235
plotSCEDensity0.2940.0100.305
plotSCEDensityAssayData0.1930.0100.205
plotSCEDensityColData0.2350.0110.246
plotSCEDimReduceColData0.7440.0150.764
plotSCEDimReduceFeatures0.4510.0120.465
plotSCEHeatmap0.7210.0120.736
plotSCEScatter0.4390.0140.460
plotSCEViolin0.2660.0100.280
plotSCEViolinAssayData0.3260.0080.335
plotSCEViolinColData0.2620.0100.272
plotScDblFinderResults38.772 0.67839.832
plotScanpyDotPlot0.0360.0040.039
plotScanpyEmbedding0.0350.0030.038
plotScanpyHVG0.0350.0050.039
plotScanpyHeatmap0.0350.0040.038
plotScanpyMarkerGenes0.0360.0020.037
plotScanpyMarkerGenesDotPlot0.0350.0040.039
plotScanpyMarkerGenesHeatmap0.0330.0050.038
plotScanpyMarkerGenesMatrixPlot0.0340.0020.036
plotScanpyMarkerGenesViolin0.0340.0020.036
plotScanpyMatrixPlot0.0340.0030.037
plotScanpyPCA0.0340.0030.038
plotScanpyPCAGeneRanking0.0360.0050.041
plotScanpyPCAVariance0.0360.0020.039
plotScanpyViolin0.0360.0030.039
plotScdsHybridResults11.151 0.16611.359
plotScrubletResults0.0380.0020.040
plotSeuratElbow0.0340.0020.037
plotSeuratHVG0.0340.0020.037
plotSeuratJackStraw0.0380.0040.043
plotSeuratReduction0.0330.0040.038
plotSoupXResults000
plotTSCANClusterDEG5.7570.1005.880
plotTSCANClusterPseudo1.9300.0271.980
plotTSCANDimReduceFeatures2.4350.0252.473
plotTSCANPseudotimeGenes2.2800.0302.325
plotTSCANPseudotimeHeatmap2.5210.0282.573
plotTSCANResults2.3370.0292.376
plotTSNE0.5610.0130.578
plotTopHVG0.5750.0190.596
plotUMAP7.9830.0558.097
readSingleCellMatrix0.0060.0010.006
reportCellQC0.1780.0120.190
reportDropletQC0.0390.0090.048
reportQCTool0.2030.0110.215
retrieveSCEIndex0.0420.0050.049
runBBKNN0.0000.0000.001
runBarcodeRankDrops0.4490.0080.461
runBcds1.6530.0911.766
runCellQC0.1990.0050.205
runClusterSummaryMetrics0.8020.0290.834
runComBatSeq0.5300.0130.546
runCxds0.5440.0110.557
runCxdsBcdsHybrid2.1170.1042.228
runDEAnalysis0.8400.0250.868
runDecontX8.8210.0458.907
runDimReduce0.5170.0110.533
runDoubletFinder39.289 0.17639.587
runDropletQC0.0350.0040.038
runEmptyDrops6.3480.0226.383
runEnrichR0.3420.0284.453
runFastMNN1.5880.0331.635
runFeatureSelection0.2610.0060.268
runFindMarker3.8760.0613.950
runGSVA0.9800.0331.028
runHarmony0.0440.0020.046
runKMeans0.5020.0140.516
runLimmaBC0.0830.0010.085
runMNNCorrect0.6830.0140.700
runModelGeneVar0.5050.0140.534
runNormalization2.8930.0402.951
runPerCellQC0.5520.0130.567
runSCANORAMA0.0000.0000.001
runSCMerge0.0060.0010.006
runScDblFinder26.179 0.45826.831
runScanpyFindClusters0.0330.0030.037
runScanpyFindHVG0.0360.0040.040
runScanpyFindMarkers0.0350.0040.040
runScanpyNormalizeData0.2290.0060.236
runScanpyPCA0.0370.0020.040
runScanpyScaleData0.0360.0010.037
runScanpyTSNE0.0360.0060.043
runScanpyUMAP0.0340.0050.039
runScranSNN0.8190.0160.837
runScrublet0.0340.0020.037
runSeuratFindClusters0.0360.0020.038
runSeuratFindHVG0.8980.0540.954
runSeuratHeatmap0.0380.0050.044
runSeuratICA0.0440.0040.047
runSeuratJackStraw0.0350.0050.039
runSeuratNormalizeData0.0370.0060.042
runSeuratPCA0.0360.0060.043
runSeuratSCTransform6.7620.0776.864
runSeuratScaleData0.0400.0030.043
runSeuratUMAP0.0390.0090.048
runSingleR0.0390.0040.043
runSoupX000
runTSCAN1.6890.0231.715
runTSCANClusterDEAnalysis1.6850.0211.714
runTSCANDEG1.6760.0221.704
runTSNE1.1050.0181.130
runUMAP7.8210.0597.922
runVAM0.5510.0110.572
runZINBWaVE0.0060.0020.022
sampleSummaryStats0.3050.0080.316
scaterCPM0.1810.0060.190
scaterPCA0.6900.0230.718
scaterlogNormCounts0.3170.0130.330
sce0.0330.0100.044
sctkListGeneSetCollections0.0930.0110.104
sctkPythonInstallConda0.0000.0000.001
sctkPythonInstallVirtualEnv0.0000.0000.001
selectSCTKConda0.0010.0000.000
selectSCTKVirtualEnvironment0.0000.0000.001
setRowNames0.1730.0180.195
setSCTKDisplayRow0.4210.0100.433
singleCellTK000
subDiffEx0.5440.0380.592
subsetSCECols0.2060.0080.218
subsetSCERows0.4430.0130.465
summarizeSCE0.0940.0080.102
trimCounts0.2660.0150.284