Back to Multiple platform build/check report for BioC 3.19:   simplified   long
ABCDEFGHIJKLMNOPQR[S]TUVWXYZ

This page was generated on 2024-05-30 11:35:55 -0400 (Thu, 30 May 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 (2024-04-24) -- "Puppy Cup" 4753
lconwaymacOS 12.7.1 Montereyx86_644.4.0 (2024-04-24) -- "Puppy Cup" 4518
Click on any hostname to see more info about the system (e.g. compilers)      (*) as reported by 'uname -p', except on Windows and Mac OS X

Package 1992/2300HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.14.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-05-29 14:00:10 -0400 (Wed, 29 May 2024)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: RELEASE_3_19
git_last_commit: cd29b84
git_last_commit_date: 2024-04-30 11:06:02 -0400 (Tue, 30 Apr 2024)
nebbiolo1Linux (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 / arm64see weekly results here

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: 2.14.0
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.14.0.tar.gz
StartedAt: 2024-05-29 23:49:47 -0400 (Wed, 29 May 2024)
EndedAt: 2024-05-30 00:07:14 -0400 (Thu, 30 May 2024)
EllapsedTime: 1047.0 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.14.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.19-bioc/meat/singleCellTK.Rcheck’
* using R version 4.4.0 (2024-04-24)
* 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 ‘2.14.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... NOTE
  installed size is  6.8Mb
  sub-directories of 1Mb or more:
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) dedupRowNames.Rd:10: Lost braces
    10 | \item{x}{A matrix like or /linkS4class{SingleCellExperiment} object, on which
       |                                       ^
checkRd: (-1) dedupRowNames.Rd:14: Lost braces
    14 | /linkS4class{SingleCellExperiment} object. When set to \code{TRUE}, will
       |             ^
checkRd: (-1) dedupRowNames.Rd:22: Lost braces
    22 | By default, a matrix or /linkS4class{SingleCellExperiment} object
       |                                     ^
checkRd: (-1) dedupRowNames.Rd:24: Lost braces
    24 | When \code{x} is a /linkS4class{SingleCellExperiment} and \code{as.rowData}
       |                                ^
checkRd: (-1) plotBubble.Rd:42: Lost braces
    42 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runClusterSummaryMetrics.Rd:27: Lost braces
    27 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runEmptyDrops.Rd:66: Lost braces
    66 | provided \\linkS4class{SingleCellExperiment} object.
       |                       ^
checkRd: (-1) runSCMerge.Rd:44: Lost braces
    44 | construct pseudo-replicates. The length of code{kmeansK} needs to be the same
       |                                                ^
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
plotDoubletFinderResults 37.675  0.394  38.355
runDoubletFinder         33.194  0.305  33.711
plotScDblFinderResults   31.367  1.078  32.751
runScDblFinder           22.404  0.518  23.084
importExampleData        19.645  2.258  22.595
plotBatchCorrCompare     11.561  0.216  11.879
plotScdsHybridResults     9.902  0.215  10.197
plotBcdsResults           8.577  0.274   8.934
plotDecontXResults        8.510  0.142   8.742
detectCellOutlier         7.371  0.193   7.631
runDecontX                7.418  0.085   7.561
plotCxdsResults           7.275  0.127   7.478
plotUMAP                  7.136  0.106   7.289
runUMAP                   6.894  0.084   7.025
plotTSCANClusterDEG       6.591  0.158   6.819
plotEmptyDropsScatter     6.529  0.080   6.679
plotEmptyDropsResults     6.487  0.054   6.580
runEmptyDrops             6.174  0.055   6.275
runSeuratSCTransform      6.027  0.137   6.217
plotFindMarkerHeatmap     5.404  0.074   5.533
convertSCEToSeurat        4.862  0.275   5.188
plotDEGViolin             4.986  0.114   5.148
getEnrichRResult          0.359  0.052   9.100
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 3 NOTEs
See
  ‘/Users/biocbuild/bbs-3.19-bioc/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 4.4.0 (2024-04-24) -- "Puppy Cup"
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 
  0.234   0.087   0.307 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.0 (2024-04-24) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: 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 
291.963   8.313 311.402 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0020.0020.005
SEG0.0020.0030.006
calcEffectSizes0.2300.0250.260
combineSCE1.7930.0691.881
computeZScore0.2680.0120.283
convertSCEToSeurat4.8620.2755.188
convertSeuratToSCE0.5970.0140.616
dedupRowNames0.0740.0100.095
detectCellOutlier7.3710.1937.631
diffAbundanceFET0.0840.0050.090
discreteColorPalette0.0080.0010.009
distinctColors0.0040.0010.005
downSampleCells0.7640.0800.850
downSampleDepth0.6490.0520.707
expData-ANY-character-method0.3790.0120.400
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.4370.0110.452
expData-set0.4230.0110.439
expData0.3980.0320.434
expDataNames-ANY-method0.4210.0380.462
expDataNames0.3880.0120.405
expDeleteDataTag0.0490.0040.053
expSetDataTag0.0320.0030.035
expTaggedData0.0360.0040.041
exportSCE0.0310.0060.037
exportSCEtoAnnData0.0990.0040.103
exportSCEtoFlatFile0.0950.0030.100
featureIndex0.0480.0050.053
generateSimulatedData0.0670.0060.074
getBiomarker0.0660.0070.073
getDEGTopTable1.0660.0541.136
getDiffAbundanceResults0.0690.0050.075
getEnrichRResult0.3590.0529.100
getFindMarkerTopTable4.3130.0854.435
getMSigDBTable0.0050.0040.008
getPathwayResultNames0.0300.0060.035
getSampleSummaryStatsTable0.3700.0070.379
getSoupX000
getTSCANResults2.3640.0712.458
getTopHVG1.3940.0301.438
importAnnData0.0020.0010.002
importBUStools0.3150.0060.324
importCellRanger1.4580.0521.531
importCellRangerV2Sample0.3390.0060.349
importCellRangerV3Sample0.5210.0200.549
importDropEst0.3820.0070.393
importExampleData19.645 2.25822.595
importGeneSetsFromCollection0.9110.1521.083
importGeneSetsFromGMT0.0750.0070.087
importGeneSetsFromList0.1620.0060.169
importGeneSetsFromMSigDB2.7840.1352.948
importMitoGeneSet0.0690.0080.077
importOptimus0.0020.0010.003
importSEQC0.3370.0250.367
importSTARsolo0.3050.0100.321
iterateSimulations0.4540.0140.472
listSampleSummaryStatsTables0.5570.0170.580
mergeSCEColData0.6020.0270.640
mouseBrainSubsetSCE0.0440.0050.050
msigdb_table0.0020.0030.004
plotBarcodeRankDropsResults1.0260.0281.060
plotBarcodeRankScatter1.0140.0171.041
plotBatchCorrCompare11.561 0.21611.879
plotBatchVariance0.3760.0290.409
plotBcdsResults8.5770.2748.934
plotBubble1.3210.0611.395
plotClusterAbundance1.0070.0141.031
plotCxdsResults7.2750.1277.478
plotDEGHeatmap3.5930.1373.766
plotDEGRegression4.3260.0944.463
plotDEGViolin4.9860.1145.148
plotDEGVolcano1.2770.0261.315
plotDecontXResults8.5100.1428.742
plotDimRed0.3380.0090.351
plotDoubletFinderResults37.675 0.39438.355
plotEmptyDropsResults6.4870.0546.580
plotEmptyDropsScatter6.5290.0806.679
plotFindMarkerHeatmap5.4040.0745.533
plotMASTThresholdGenes1.8440.0501.913
plotPCA0.6050.0130.623
plotPathway1.0830.0231.118
plotRunPerCellQCResults2.6160.0392.678
plotSCEBarAssayData0.2410.0090.253
plotSCEBarColData0.1870.0080.198
plotSCEBatchFeatureMean0.2670.0050.275
plotSCEDensity0.3310.0090.342
plotSCEDensityAssayData0.2130.0070.222
plotSCEDensityColData0.2590.0080.270
plotSCEDimReduceColData0.9250.0200.953
plotSCEDimReduceFeatures0.5330.0150.553
plotSCEHeatmap0.8380.0150.859
plotSCEScatter0.4560.0110.472
plotSCEViolin0.3090.0080.322
plotSCEViolinAssayData0.3660.0120.383
plotSCEViolinColData0.2920.0080.303
plotScDblFinderResults31.367 1.07832.751
plotScanpyDotPlot0.0350.0030.039
plotScanpyEmbedding0.0240.0030.028
plotScanpyHVG0.0300.0040.034
plotScanpyHeatmap0.0290.0040.033
plotScanpyMarkerGenes0.0340.0040.037
plotScanpyMarkerGenesDotPlot0.0340.0050.039
plotScanpyMarkerGenesHeatmap0.0320.0070.041
plotScanpyMarkerGenesMatrixPlot0.0330.0070.042
plotScanpyMarkerGenesViolin0.0370.0050.042
plotScanpyMatrixPlot0.0300.0050.035
plotScanpyPCA0.0260.0040.031
plotScanpyPCAGeneRanking0.0300.0040.035
plotScanpyPCAVariance0.0300.0040.035
plotScanpyViolin0.0300.0030.032
plotScdsHybridResults 9.902 0.21510.197
plotScrubletResults0.0490.0040.054
plotSeuratElbow0.0290.0050.035
plotSeuratHVG0.0290.0050.034
plotSeuratJackStraw0.0370.0050.042
plotSeuratReduction0.0310.0040.036
plotSoupXResults000
plotTSCANClusterDEG6.5910.1586.819
plotTSCANClusterPseudo2.8410.0492.916
plotTSCANDimReduceFeatures2.7900.0522.876
plotTSCANPseudotimeGenes2.5960.0432.663
plotTSCANPseudotimeHeatmap2.9560.0523.034
plotTSCANResults2.7090.0532.790
plotTSNE0.6280.0160.649
plotTopHVG0.5940.0180.617
plotUMAP7.1360.1067.289
readSingleCellMatrix0.0060.0010.007
reportCellQC0.2180.0100.228
reportDropletQC0.0220.0030.026
reportQCTool0.2020.0050.208
retrieveSCEIndex0.0370.0060.043
runBBKNN000
runBarcodeRankDrops0.5130.0100.527
runBcds2.1840.0742.277
runCellQC0.2310.0100.245
runClusterSummaryMetrics0.9320.0480.988
runComBatSeq0.5460.0210.575
runCxds0.5830.0140.601
runCxdsBcdsHybrid2.2010.0662.292
runDEAnalysis0.9180.0480.976
runDecontX7.4180.0857.561
runDimReduce0.5570.0100.571
runDoubletFinder33.194 0.30533.711
runDropletQC0.0320.0040.037
runEmptyDrops6.1740.0556.275
runEnrichR0.3440.0411.988
runFastMNN1.8980.0511.963
runFeatureSelection0.2610.0080.271
runFindMarker4.2860.0874.405
runGSVA1.0220.0531.089
runHarmony0.0380.0010.039
runKMeans0.5490.0140.568
runLimmaBC0.1010.0020.104
runMNNCorrect0.6770.0140.695
runModelGeneVar0.5650.0100.577
runNormalization2.4340.0472.499
runPerCellQC0.6200.0160.642
runSCANORAMA0.0000.0010.001
runSCMerge0.0040.0020.008
runScDblFinder22.404 0.51823.084
runScanpyFindClusters0.0280.0020.030
runScanpyFindHVG0.0270.0060.034
runScanpyFindMarkers0.0310.0050.037
runScanpyNormalizeData0.2490.0090.259
runScanpyPCA0.0260.0040.030
runScanpyScaleData0.0270.0040.031
runScanpyTSNE0.0370.0070.045
runScanpyUMAP0.0290.0060.037
runScranSNN0.9490.0290.989
runScrublet0.0310.0040.036
runSeuratFindClusters0.0270.0030.031
runSeuratFindHVG0.9470.1251.092
runSeuratHeatmap0.0260.0030.030
runSeuratICA0.0310.0060.038
runSeuratJackStraw0.0300.0080.038
runSeuratNormalizeData0.0310.0050.037
runSeuratPCA0.0320.0040.036
runSeuratSCTransform6.0270.1376.217
runSeuratScaleData0.0270.0060.034
runSeuratUMAP0.0360.0040.040
runSingleR0.0430.0030.046
runSoupX0.0000.0010.000
runTSCAN1.7790.0391.837
runTSCANClusterDEAnalysis1.8560.0301.897
runTSCANDEG1.9300.0331.978
runTSNE0.9750.0211.005
runUMAP6.8940.0847.025
runVAM0.6020.0110.616
runZINBWaVE0.0060.0020.007
sampleSummaryStats0.3320.0090.342
scaterCPM0.1450.0030.147
scaterPCA0.7140.0140.733
scaterlogNormCounts0.2690.0080.279
sce0.0280.0060.034
sctkListGeneSetCollections0.1010.0060.108
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv0.0000.0000.001
selectSCTKConda0.0000.0010.001
selectSCTKVirtualEnvironment0.0000.0010.000
setRowNames0.1960.0220.220
setSCTKDisplayRow0.4930.0120.508
singleCellTK0.0000.0010.001
subDiffEx0.5990.0310.635
subsetSCECols0.2300.0120.247
subsetSCERows0.5090.0110.522
summarizeSCE0.0810.0070.089
trimCounts0.2240.0090.234