Back to Multiple platform build/check report for BioC 3.20:   simplified   long
ABCDEFGHIJKLMNOPQR[S]TUVWXYZ

This page was generated on 2024-06-25 11:39 -0400 (Tue, 25 Jun 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 RC (2024-04-16 r86468) -- "Puppy Cup" 4690
lconwaymacOS 12.7.1 Montereyx86_644.4.1 RC (2024-06-06 r86719) -- "Race for Your Life" 4404
kjohnson3macOS 13.6.5 Venturaarm644.4.1 RC (2024-06-06 r86719) -- "Race for Your Life" 4353
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 1939/2242HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.15.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-06-24 14:00 -0400 (Mon, 24 Jun 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
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 lconway

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: 2024-06-24 23:09:00 -0400 (Mon, 24 Jun 2024)
EndedAt: 2024-06-24 23:25:56 -0400 (Mon, 24 Jun 2024)
EllapsedTime: 1015.4 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 RC (2024-06-06 r86719)
* using platform: x86_64-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 Monterey 12.7.1
* 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 37.273  0.256  37.702
runDoubletFinder         32.527  0.213  32.888
plotScDblFinderResults   29.626  0.849  30.664
runScDblFinder           21.151  0.397  21.643
importExampleData        18.697  2.059  21.623
plotBatchCorrCompare     12.074  0.144  12.259
plotScdsHybridResults    10.122  0.165  10.341
plotBcdsResults           8.983  0.212   9.238
plotDecontXResults        8.662  0.082   8.785
runDecontX                7.429  0.054   7.512
plotUMAP                  7.239  0.070   7.329
runUMAP                   7.199  0.061   7.282
detectCellOutlier         6.992  0.157   7.186
plotCxdsResults           7.073  0.076   7.190
plotTSCANClusterDEG       6.376  0.113   6.524
plotEmptyDropsScatter     6.184  0.036   6.267
plotEmptyDropsResults     6.105  0.044   6.185
runSeuratSCTransform      5.883  0.100   6.113
runEmptyDrops             5.931  0.029   5.983
plotDEGViolin             5.256  0.104   5.402
plotFindMarkerHeatmap     5.099  0.040   5.160
convertSCEToSeurat        4.893  0.232   5.151
getEnrichRResult          0.370  0.044   8.582
* 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-x86_64/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 RC (2024-06-06 r86719) -- "Race for Your Life"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-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 
  0.218   0.081   0.292 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.1 RC (2024-06-06 r86719) -- "Race for Your Life"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |==                                                                    |   3%
  |                                                                            
  |====                                                                  |   6%
  |                                                                            
  |======                                                                |   9%
  |                                                                            
  |========                                                              |  12%
  |                                                                            
  |==========                                                            |  15%
  |                                                                            
  |============                                                          |  18%
  |                                                                            
  |==============                                                        |  21%
  |                                                                            
  |================                                                      |  24%
  |                                                                            
  |===================                                                   |  26%
  |                                                                            
  |=====================                                                 |  29%
  |                                                                            
  |=======================                                               |  32%
  |                                                                            
  |=========================                                             |  35%
  |                                                                            
  |===========================                                           |  38%
  |                                                                            
  |=============================                                         |  41%
  |                                                                            
  |===============================                                       |  44%
  |                                                                            
  |=================================                                     |  47%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |=====================================                                 |  53%
  |                                                                            
  |=======================================                               |  56%
  |                                                                            
  |=========================================                             |  59%
  |                                                                            
  |===========================================                           |  62%
  |                                                                            
  |=============================================                         |  65%
  |                                                                            
  |===============================================                       |  68%
  |                                                                            
  |=================================================                     |  71%
  |                                                                            
  |===================================================                   |  74%
  |                                                                            
  |======================================================                |  76%
  |                                                                            
  |========================================================              |  79%
  |                                                                            
  |==========================================================            |  82%
  |                                                                            
  |============================================================          |  85%
  |                                                                            
  |==============================================================        |  88%
  |                                                                            
  |================================================================      |  91%
  |                                                                            
  |==================================================================    |  94%
  |                                                                            
  |====================================================================  |  97%
  |                                                                            
  |======================================================================| 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 2 gene sets.
Estimating ECDFs with Gaussian kernels

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |======================================================================| 100%

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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
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 
289.230   6.808 309.996 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0020.004
SEG0.0020.0030.006
calcEffectSizes0.2070.0220.230
combineSCE1.6920.0561.756
computeZScore0.2630.0090.275
convertSCEToSeurat4.8930.2325.151
convertSeuratToSCE0.5830.0100.595
dedupRowNames0.0640.0060.070
detectCellOutlier6.9920.1577.186
diffAbundanceFET0.0640.0050.068
discreteColorPalette0.0080.0010.009
distinctColors0.0030.0010.003
downSampleCells0.7200.0760.802
downSampleDepth0.6750.0450.725
expData-ANY-character-method0.3360.0080.345
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.4400.0100.453
expData-set0.3980.0100.410
expData0.4070.0250.435
expDataNames-ANY-method0.3890.0290.421
expDataNames0.3670.0080.376
expDeleteDataTag0.0370.0040.041
expSetDataTag0.0300.0040.033
expTaggedData0.0310.0030.033
exportSCE0.0300.0040.034
exportSCEtoAnnData0.0880.0020.090
exportSCEtoFlatFile0.0910.0030.094
featureIndex0.0460.0050.053
generateSimulatedData0.0620.0050.068
getBiomarker0.0690.0040.073
getDEGTopTable0.9740.0431.023
getDiffAbundanceResults0.0580.0040.062
getEnrichRResult0.3700.0448.582
getFindMarkerTopTable4.0660.0674.160
getMSigDBTable0.0040.0030.007
getPathwayResultNames0.0270.0040.031
getSampleSummaryStatsTable0.3910.0070.400
getSoupX0.0000.0000.001
getTSCANResults2.1400.0542.209
getTopHVG1.3990.0201.427
importAnnData0.0020.0000.002
importBUStools0.3050.0050.313
importCellRanger1.3000.0411.356
importCellRangerV2Sample0.2780.0030.283
importCellRangerV3Sample0.4470.0170.471
importDropEst0.3510.0050.358
importExampleData18.697 2.05921.623
importGeneSetsFromCollection0.8130.1380.965
importGeneSetsFromGMT0.0800.0070.087
importGeneSetsFromList0.1470.0060.153
importGeneSetsFromMSigDB2.5750.1082.697
importMitoGeneSet0.0520.0080.060
importOptimus0.0010.0010.002
importSEQC0.3250.0200.348
importSTARsolo0.2960.0050.304
iterateSimulations0.4010.0090.413
listSampleSummaryStatsTables0.5260.0170.553
mergeSCEColData0.5710.0210.598
mouseBrainSubsetSCE0.0360.0050.040
msigdb_table0.0020.0020.003
plotBarcodeRankDropsResults0.9890.0231.017
plotBarcodeRankScatter1.0050.0161.027
plotBatchCorrCompare12.074 0.14412.259
plotBatchVariance0.3400.0260.367
plotBcdsResults8.9830.2129.238
plotBubble1.2530.0461.307
plotClusterAbundance1.0010.0101.015
plotCxdsResults7.0730.0767.190
plotDEGHeatmap3.2550.1153.392
plotDEGRegression3.8710.0543.941
plotDEGViolin5.2560.1045.402
plotDEGVolcano1.2370.0171.262
plotDecontXResults8.6620.0828.785
plotDimRed0.3290.0070.340
plotDoubletFinderResults37.273 0.25637.702
plotEmptyDropsResults6.1050.0446.185
plotEmptyDropsScatter6.1840.0366.267
plotFindMarkerHeatmap5.0990.0405.160
plotMASTThresholdGenes1.8390.0401.892
plotPCA0.6130.0110.628
plotPathway0.9970.0151.020
plotRunPerCellQCResults2.6500.0292.694
plotSCEBarAssayData0.2380.0090.249
plotSCEBarColData0.1770.0060.184
plotSCEBatchFeatureMean0.2630.0040.269
plotSCEDensity0.2930.0080.302
plotSCEDensityAssayData0.2150.0050.222
plotSCEDensityColData0.2550.0080.266
plotSCEDimReduceColData0.8610.0140.880
plotSCEDimReduceFeatures0.4740.0100.486
plotSCEHeatmap0.7900.0110.803
plotSCEScatter0.4260.0100.440
plotSCEViolin0.3100.0090.320
plotSCEViolinAssayData0.3460.0080.358
plotSCEViolinColData0.2440.0070.253
plotScDblFinderResults29.626 0.84930.664
plotScanpyDotPlot0.0340.0050.039
plotScanpyEmbedding0.0280.0040.033
plotScanpyHVG0.0290.0040.032
plotScanpyHeatmap0.0280.0030.033
plotScanpyMarkerGenes0.0340.0030.037
plotScanpyMarkerGenesDotPlot0.0330.0030.036
plotScanpyMarkerGenesHeatmap0.0290.0040.034
plotScanpyMarkerGenesMatrixPlot0.0280.0050.038
plotScanpyMarkerGenesViolin0.0300.0030.033
plotScanpyMatrixPlot0.0320.0030.034
plotScanpyPCA0.0310.0030.033
plotScanpyPCAGeneRanking0.0290.0030.032
plotScanpyPCAVariance0.0290.0020.031
plotScanpyViolin0.0330.0040.036
plotScdsHybridResults10.122 0.16510.341
plotScrubletResults0.0290.0040.034
plotSeuratElbow0.0300.0020.032
plotSeuratHVG0.0270.0030.030
plotSeuratJackStraw0.0270.0030.030
plotSeuratReduction0.0300.0020.033
plotSoupXResults0.0010.0000.000
plotTSCANClusterDEG6.3760.1136.524
plotTSCANClusterPseudo2.8170.0342.865
plotTSCANDimReduceFeatures2.6440.0282.683
plotTSCANPseudotimeGenes2.5000.0252.533
plotTSCANPseudotimeHeatmap2.8140.0312.855
plotTSCANResults2.5480.0312.590
plotTSNE0.6330.0110.646
plotTopHVG0.5910.0130.609
plotUMAP7.2390.0707.329
readSingleCellMatrix0.0060.0010.007
reportCellQC0.2020.0050.208
reportDropletQC0.0240.0040.028
reportQCTool0.1940.0050.199
retrieveSCEIndex0.0370.0030.040
runBBKNN000
runBarcodeRankDrops0.4680.0080.475
runBcds2.0780.0552.140
runCellQC0.2080.0060.216
runClusterSummaryMetrics0.8870.0430.935
runComBatSeq0.5080.0180.529
runCxds0.5480.0090.558
runCxdsBcdsHybrid2.1240.0472.176
runDEAnalysis0.9120.0420.960
runDecontX7.4290.0547.512
runDimReduce0.5370.0080.546
runDoubletFinder32.527 0.21332.888
runDropletQC0.0280.0040.032
runEmptyDrops5.9310.0295.983
runEnrichR0.2860.0311.794
runFastMNN1.9750.0422.028
runFeatureSelection0.2460.0060.253
runFindMarker4.0510.0604.126
runGSVA0.9780.0421.024
runHarmony0.0460.0020.047
runKMeans0.5430.0110.557
runLimmaBC0.0970.0030.101
runMNNCorrect0.6710.0110.685
runModelGeneVar0.5390.0080.549
runNormalization2.4320.0402.483
runPerCellQC0.5910.0110.605
runSCANORAMA0.0000.0000.001
runSCMerge0.0050.0010.006
runScDblFinder21.151 0.39721.643
runScanpyFindClusters0.0290.0040.033
runScanpyFindHVG0.0290.0030.032
runScanpyFindMarkers0.0290.0050.034
runScanpyNormalizeData0.2340.0090.244
runScanpyPCA0.0290.0030.032
runScanpyScaleData0.0260.0020.029
runScanpyTSNE0.0300.0030.034
runScanpyUMAP0.0270.0030.030
runScranSNN0.8980.0210.930
runScrublet0.0320.0040.036
runSeuratFindClusters0.0310.0040.034
runSeuratFindHVG0.9160.0891.009
runSeuratHeatmap0.0260.0070.034
runSeuratICA0.0280.0040.033
runSeuratJackStraw0.0310.0040.035
runSeuratNormalizeData0.0270.0020.029
runSeuratPCA0.0290.0020.031
runSeuratSCTransform5.8830.1006.113
runSeuratScaleData0.0300.0040.034
runSeuratUMAP0.0250.0050.031
runSingleR0.0390.0020.041
runSoupX0.0000.0000.001
runTSCAN1.8650.0321.909
runTSCANClusterDEAnalysis1.9220.0251.958
runTSCANDEG1.8100.0241.842
runTSNE0.9290.0160.950
runUMAP7.1990.0617.282
runVAM0.6460.0090.657
runZINBWaVE0.0040.0010.006
sampleSummaryStats0.3470.0070.356
scaterCPM0.1470.0010.150
scaterPCA0.7880.0130.805
scaterlogNormCounts0.2910.0030.296
sce0.0270.0050.032
sctkListGeneSetCollections0.0830.0060.090
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv0.0000.0000.001
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.1710.0150.188
setSCTKDisplayRow0.4400.0070.448
singleCellTK0.0010.0000.001
subDiffEx0.6040.0220.631
subsetSCECols0.2240.0060.232
subsetSCERows0.4960.0100.508
summarizeSCE0.0840.0040.089
trimCounts0.2290.0080.238