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

This page was generated on 2024-06-11 14:43 -0400 (Tue, 11 Jun 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 (2024-04-24) -- "Puppy Cup" 4757
palomino3Windows Server 2022 Datacenterx644.4.0 (2024-04-24 ucrt) -- "Puppy Cup" 4491
lconwaymacOS 12.7.1 Montereyx86_644.4.0 (2024-04-24) -- "Puppy Cup" 4522
kjohnson3macOS 13.6.5 Venturaarm644.4.0 (2024-04-24) -- "Puppy Cup" 4468
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: 2024-06-09 14:00 -0400 (Sun, 09 Jun 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
palomino3Windows Server 2022 Datacenter / x64  OK    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: 2.14.0
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: 2024-06-09 23:07:32 -0400 (Sun, 09 Jun 2024)
EndedAt: 2024-06-09 23:24:43 -0400 (Sun, 09 Jun 2024)
EllapsedTime: 1031.2 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 38.179  0.226  38.528
plotScDblFinderResults   32.524  0.930  33.593
runDoubletFinder         32.493  0.459  33.170
runScDblFinder           22.467  0.474  23.036
importExampleData        18.820  2.250  21.648
plotBatchCorrCompare     12.048  0.181  12.273
plotScdsHybridResults    10.086  0.166  10.279
plotBcdsResults           8.867  0.220   9.122
plotDecontXResults        8.918  0.089   9.046
runUMAP                   7.561  0.077   7.658
plotUMAP                  7.342  0.107   7.485
plotCxdsResults           7.213  0.065   7.294
runDecontX                7.184  0.039   7.236
plotTSCANClusterDEG       6.986  0.152   7.165
detectCellOutlier         6.775  0.144   6.949
plotEmptyDropsScatter     6.510  0.024   6.553
runEmptyDrops             6.421  0.065   6.517
plotEmptyDropsResults     6.414  0.021   6.456
runSeuratSCTransform      6.272  0.134   6.438
plotFindMarkerHeatmap     5.992  0.042   6.056
plotDEGViolin             5.668  0.111   5.805
getEnrichRResult          0.396  0.047  22.090
* 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   0.085   0.320 

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 
277.547   6.786 302.107 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0020.006
SEG0.0030.0030.006
calcEffectSizes0.2110.0190.231
combineSCE1.3710.0441.418
computeZScore0.2220.0120.236
convertSCEToSeurat4.6020.2614.874
convertSeuratToSCE0.5460.0100.557
dedupRowNames0.0550.0040.060
detectCellOutlier6.7750.1446.949
diffAbundanceFET0.0550.0040.059
discreteColorPalette0.0070.0000.007
distinctColors0.0030.0000.003
downSampleCells0.7600.0720.835
downSampleDepth0.6280.0420.673
expData-ANY-character-method0.3590.0060.366
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.4580.0080.468
expData-set0.4470.0090.458
expData0.4180.0250.445
expDataNames-ANY-method0.4050.0300.435
expDataNames0.3340.0080.346
expDeleteDataTag0.0450.0050.050
expSetDataTag0.0310.0040.035
expTaggedData0.0360.0030.039
exportSCE0.0280.0040.031
exportSCEtoAnnData0.0950.0020.098
exportSCEtoFlatFile0.1000.0020.102
featureIndex0.0420.0050.047
generateSimulatedData0.0600.0050.067
getBiomarker0.0770.0060.082
getDEGTopTable1.1050.0461.155
getDiffAbundanceResults0.0630.0040.066
getEnrichRResult 0.396 0.04722.090
getFindMarkerTopTable4.6140.0744.700
getMSigDBTable0.0050.0040.009
getPathwayResultNames0.0390.0070.046
getSampleSummaryStatsTable0.4450.0080.454
getSoupX000
getTSCANResults2.4630.0622.533
getTopHVG1.6020.0211.626
importAnnData0.0020.0010.003
importBUStools0.4370.0070.447
importCellRanger1.6320.0491.695
importCellRangerV2Sample0.3590.0050.364
importCellRangerV3Sample0.5650.0180.587
importDropEst0.4350.0060.442
importExampleData18.820 2.25021.648
importGeneSetsFromCollection0.8650.1381.019
importGeneSetsFromGMT0.0840.0050.091
importGeneSetsFromList0.1630.0050.169
importGeneSetsFromMSigDB2.8190.1202.960
importMitoGeneSet0.0470.0070.054
importOptimus0.0020.0000.002
importSEQC0.3390.0270.368
importSTARsolo0.3600.0050.366
iterateSimulations0.4390.0120.453
listSampleSummaryStatsTables0.5800.0090.591
mergeSCEColData0.6110.0230.641
mouseBrainSubsetSCE0.0470.0060.053
msigdb_table0.0010.0020.004
plotBarcodeRankDropsResults1.1250.0221.150
plotBarcodeRankScatter1.1100.0141.130
plotBatchCorrCompare12.048 0.18112.273
plotBatchVariance0.3910.0240.418
plotBcdsResults8.8670.2209.122
plotBubble1.2960.0471.349
plotClusterAbundance1.1080.0101.122
plotCxdsResults7.2130.0657.294
plotDEGHeatmap3.6400.1253.785
plotDEGRegression4.6740.0664.761
plotDEGViolin5.6680.1115.805
plotDEGVolcano1.2710.0181.294
plotDecontXResults8.9180.0899.046
plotDimRed0.3980.0080.407
plotDoubletFinderResults38.179 0.22638.528
plotEmptyDropsResults6.4140.0216.456
plotEmptyDropsScatter6.5100.0246.553
plotFindMarkerHeatmap5.9920.0426.056
plotMASTThresholdGenes1.8810.0341.920
plotPCA0.6030.0120.619
plotPathway1.0490.0151.068
plotRunPerCellQCResults2.8840.0342.932
plotSCEBarAssayData0.2270.0070.235
plotSCEBarColData0.1930.0060.201
plotSCEBatchFeatureMean0.3570.0040.362
plotSCEDensity0.2760.0080.286
plotSCEDensityAssayData0.2100.0070.217
plotSCEDensityColData0.2700.0050.276
plotSCEDimReduceColData0.9370.0150.957
plotSCEDimReduceFeatures0.4780.0080.487
plotSCEHeatmap0.7960.0090.807
plotSCEScatter0.4210.0080.431
plotSCEViolin0.3280.0070.338
plotSCEViolinAssayData0.3440.0070.353
plotSCEViolinColData0.3020.0070.310
plotScDblFinderResults32.524 0.93033.593
plotScanpyDotPlot0.0250.0030.029
plotScanpyEmbedding0.0290.0040.032
plotScanpyHVG0.0260.0040.030
plotScanpyHeatmap0.0260.0020.028
plotScanpyMarkerGenes0.0340.0030.037
plotScanpyMarkerGenesDotPlot0.0250.0020.028
plotScanpyMarkerGenesHeatmap0.0250.0020.027
plotScanpyMarkerGenesMatrixPlot0.0310.0030.035
plotScanpyMarkerGenesViolin0.0320.0040.037
plotScanpyMatrixPlot0.0280.0040.032
plotScanpyPCA0.0290.0050.035
plotScanpyPCAGeneRanking0.0280.0020.030
plotScanpyPCAVariance0.0290.0020.032
plotScanpyViolin0.0270.0030.030
plotScdsHybridResults10.086 0.16610.279
plotScrubletResults0.0250.0030.028
plotSeuratElbow0.0260.0020.029
plotSeuratHVG0.0310.0020.034
plotSeuratJackStraw0.0290.0040.033
plotSeuratReduction0.0250.0030.029
plotSoupXResults000
plotTSCANClusterDEG6.9860.1527.165
plotTSCANClusterPseudo3.1090.0373.158
plotTSCANDimReduceFeatures3.0900.0383.146
plotTSCANPseudotimeGenes2.8930.0322.937
plotTSCANPseudotimeHeatmap3.1660.0403.219
plotTSCANResults3.0620.0443.120
plotTSNE0.7180.0150.737
plotTopHVG0.7060.0160.725
plotUMAP7.3420.1077.485
readSingleCellMatrix0.0080.0020.017
reportCellQC0.2680.0090.279
reportDropletQC0.0290.0050.034
reportQCTool0.2160.0080.227
retrieveSCEIndex0.0400.0050.047
runBBKNN000
runBarcodeRankDrops0.4530.0100.465
runBcds2.3140.0552.382
runCellQC0.2540.0100.270
runClusterSummaryMetrics1.0030.0611.077
runComBatSeq0.5970.0200.621
runCxds0.6140.0100.627
runCxdsBcdsHybrid2.3900.0712.470
runDEAnalysis0.8720.0330.907
runDecontX7.1840.0397.236
runDimReduce0.5030.0080.512
runDoubletFinder32.493 0.45933.170
runDropletQC0.0310.0050.036
runEmptyDrops6.4210.0656.517
runEnrichR0.3530.0412.436
runFastMNN2.2310.0582.299
runFeatureSelection0.2580.0060.265
runFindMarker4.4350.0814.535
runGSVA1.1060.0551.169
runHarmony0.0410.0020.045
runKMeans0.5810.0130.597
runLimmaBC0.1010.0020.104
runMNNCorrect0.7500.0170.771
runModelGeneVar0.5800.0090.592
runNormalization2.5050.0642.594
runPerCellQC0.6810.0170.702
runSCANORAMA000
runSCMerge0.0040.0020.006
runScDblFinder22.467 0.47423.036
runScanpyFindClusters0.0300.0050.035
runScanpyFindHVG0.0280.0030.031
runScanpyFindMarkers0.0320.0030.035
runScanpyNormalizeData0.2680.0080.276
runScanpyPCA0.0320.0030.035
runScanpyScaleData0.0310.0030.035
runScanpyTSNE0.0280.0030.030
runScanpyUMAP0.0320.0030.035
runScranSNN0.9510.0190.972
runScrublet0.0340.0040.038
runSeuratFindClusters0.0290.0030.032
runSeuratFindHVG1.0110.1141.129
runSeuratHeatmap0.0240.0040.028
runSeuratICA0.0290.0050.034
runSeuratJackStraw0.0300.0040.033
runSeuratNormalizeData0.0310.0050.036
runSeuratPCA0.0320.0030.036
runSeuratSCTransform6.2720.1346.438
runSeuratScaleData0.0310.0050.035
runSeuratUMAP0.0310.0050.036
runSingleR0.0490.0030.052
runSoupX0.0000.0010.001
runTSCAN1.9460.0381.993
runTSCANClusterDEAnalysis2.0110.0282.045
runTSCANDEG2.1040.0292.139
runTSNE1.0220.0201.047
runUMAP7.5610.0777.658
runVAM0.7090.0110.721
runZINBWaVE0.0050.0020.007
sampleSummaryStats0.4010.0090.413
scaterCPM0.1580.0020.161
scaterPCA0.8640.0150.881
scaterlogNormCounts0.3020.0040.306
sce0.0290.0050.035
sctkListGeneSetCollections0.1070.0080.116
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda0.0000.0010.000
selectSCTKVirtualEnvironment0.0010.0000.001
setRowNames0.2070.0190.227
setSCTKDisplayRow0.5130.0090.525
singleCellTK0.0010.0000.001
subDiffEx0.6650.0280.695
subsetSCECols0.2350.0090.246
subsetSCERows0.5640.0130.580
summarizeSCE0.0710.0110.082
trimCounts0.2230.0070.230