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

This page was generated on 2024-05-22 11:35:16 -0400 (Wed, 22 May 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 (2024-04-24) -- "Puppy Cup" 4751
palomino3Windows Server 2022 Datacenterx644.4.0 (2024-04-24 ucrt) -- "Puppy Cup" 4485
lconwaymacOS 12.7.1 Montereyx86_644.4.0 (2024-04-24) -- "Puppy Cup" 3444
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: 2024-05-21 14:00:15 -0400 (Tue, 21 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
palomino3Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
lconwaymacOS 12.7.1 Monterey / x86_64  ERROR    ERROR  skippedskipped
kjohnson3macOS 13.6.5 Ventura / arm64see weekly results here

CHECK results for singleCellTK on nebbiolo1


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: /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings singleCellTK_2.14.0.tar.gz
StartedAt: 2024-05-22 03:07:43 -0400 (Wed, 22 May 2024)
EndedAt: 2024-05-22 03:23:26 -0400 (Wed, 22 May 2024)
EllapsedTime: 942.9 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings singleCellTK_2.14.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.19-bioc/meat/singleCellTK.Rcheck’
* using R version 4.4.0 (2024-04-24)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
    GNU Fortran (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
* running under: Ubuntu 22.04.4 LTS
* using session charset: UTF-8
* 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  5.6Mb
  sub-directories of 1Mb or more:
    shiny   2.3Mb
* 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 loading without being on the library search path ... 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 33.619  0.292  33.908
runDoubletFinder         31.955  0.172  32.127
runSeuratSCTransform     30.603  0.792  31.395
plotScDblFinderResults   29.808  0.708  30.513
runScDblFinder           19.270  0.544  19.815
importExampleData        14.057  0.992  15.620
plotBatchCorrCompare     11.832  0.156  11.981
plotScdsHybridResults    10.642  0.136   9.839
plotBcdsResults           8.369  0.192   7.660
plotDecontXResults        7.668  0.140   7.807
runDecontX                7.693  0.072   7.765
runUMAP                   7.418  0.232   7.647
plotUMAP                  6.823  0.016   6.837
plotCxdsResults           6.753  0.056   6.806
plotEmptyDropsScatter     6.611  0.024   6.636
plotEmptyDropsResults     6.583  0.008   6.591
runEmptyDrops             6.413  0.012   6.425
detectCellOutlier         5.683  0.308   5.992
plotTSCANClusterDEG       5.068  0.024   5.092
getEnrichRResult          0.497  0.025   5.831
* 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 re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 3 NOTEs
See
  ‘/home/biocbuild/bbs-3.19-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.19-bioc/R/site-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-pc-linux-gnu

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 
  0.179   0.021   0.187 

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-pc-linux-gnu

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 
279.045   7.503 289.426 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0000.003
SEG0.0030.0000.003
calcEffectSizes0.1640.0000.165
combineSCE1.4610.0521.512
computeZScore1.0560.1041.160
convertSCEToSeurat4.3590.1244.484
convertSeuratToSCE0.4720.0160.488
dedupRowNames0.0590.0000.059
detectCellOutlier5.6830.3085.992
diffAbundanceFET0.0640.0000.064
discreteColorPalette0.0080.0000.007
distinctColors0.0000.0030.003
downSampleCells0.6930.0440.738
downSampleDepth0.5080.0000.507
expData-ANY-character-method0.30.00.3
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3440.0080.352
expData-set0.3660.0040.370
expData0.3210.0280.349
expDataNames-ANY-method0.2810.0000.281
expDataNames0.2880.0040.292
expDeleteDataTag0.0370.0000.037
expSetDataTag0.0280.0000.027
expTaggedData0.0290.0000.029
exportSCE0.0240.0000.025
exportSCEtoAnnData0.0880.0120.100
exportSCEtoFlatFile0.0910.0080.099
featureIndex0.0370.0040.041
generateSimulatedData0.0550.0000.055
getBiomarker0.0640.0000.063
getDEGTopTable0.8630.0480.910
getDiffAbundanceResults0.0520.0000.053
getEnrichRResult0.4970.0255.831
getFindMarkerTopTable3.2300.0523.282
getMSigDBTable0.0040.0000.004
getPathwayResultNames0.0240.0000.024
getSampleSummaryStatsTable0.2860.0000.287
getSoupX0.0000.0000.001
getTSCANResults1.7190.0241.744
getTopHVG1.0530.0161.069
importAnnData0.0020.0000.002
importBUStools0.2370.0080.245
importCellRanger1.0800.0241.105
importCellRangerV2Sample0.2230.0040.227
importCellRangerV3Sample0.3430.0080.351
importDropEst0.2970.0080.305
importExampleData14.057 0.99215.620
importGeneSetsFromCollection0.6760.0860.762
importGeneSetsFromGMT0.0680.0000.068
importGeneSetsFromList0.1180.0040.121
importGeneSetsFromMSigDB2.4370.1522.590
importMitoGeneSet0.0540.0000.054
importOptimus0.0020.0000.002
importSEQC0.2090.0440.253
importSTARsolo0.2560.0280.284
iterateSimulations0.3600.0040.363
listSampleSummaryStatsTables0.3600.0160.375
mergeSCEColData0.4370.0280.464
mouseBrainSubsetSCE0.0370.0000.038
msigdb_table0.0020.0000.001
plotBarcodeRankDropsResults0.8880.0040.891
plotBarcodeRankScatter0.7890.0040.793
plotBatchCorrCompare11.832 0.15611.981
plotBatchVariance0.3030.0000.303
plotBcdsResults8.3690.1927.660
plotBubble0.9080.0160.924
plotClusterAbundance0.8670.0040.871
plotCxdsResults6.7530.0566.806
plotDEGHeatmap2.8450.0402.886
plotDEGRegression3.4060.0283.427
plotDEGViolin4.2680.0404.303
plotDEGVolcano0.9560.0040.961
plotDecontXResults7.6680.1407.807
plotDimRed0.2740.0040.278
plotDoubletFinderResults33.619 0.29233.908
plotEmptyDropsResults6.5830.0086.591
plotEmptyDropsScatter6.6110.0246.636
plotFindMarkerHeatmap4.2330.0524.285
plotMASTThresholdGenes1.5410.0121.554
plotPCA0.4760.0000.476
plotPathway0.8590.0040.863
plotRunPerCellQCResults2.0890.0082.096
plotSCEBarAssayData0.1850.0000.185
plotSCEBarColData0.1400.0000.139
plotSCEBatchFeatureMean0.2120.0000.212
plotSCEDensity0.2190.0000.220
plotSCEDensityAssayData0.2250.0000.224
plotSCEDensityColData0.2110.0000.211
plotSCEDimReduceColData0.7010.0080.709
plotSCEDimReduceFeatures0.3850.0000.385
plotSCEHeatmap0.6140.0040.618
plotSCEScatter0.3820.0000.382
plotSCEViolin0.2410.0000.240
plotSCEViolinAssayData0.250.000.25
plotSCEViolinColData0.2330.0000.232
plotScDblFinderResults29.808 0.70830.513
plotScanpyDotPlot0.0260.0000.026
plotScanpyEmbedding0.0210.0040.026
plotScanpyHVG0.0250.0000.024
plotScanpyHeatmap0.0250.0000.024
plotScanpyMarkerGenes0.0210.0040.025
plotScanpyMarkerGenesDotPlot0.0240.0000.024
plotScanpyMarkerGenesHeatmap0.0250.0000.025
plotScanpyMarkerGenesMatrixPlot0.0240.0000.024
plotScanpyMarkerGenesViolin0.0240.0000.024
plotScanpyMatrixPlot0.0250.0000.025
plotScanpyPCA0.0250.0000.025
plotScanpyPCAGeneRanking0.0270.0000.026
plotScanpyPCAVariance0.0240.0000.024
plotScanpyViolin0.0240.0000.024
plotScdsHybridResults10.642 0.136 9.839
plotScrubletResults0.0260.0000.026
plotSeuratElbow0.0240.0000.024
plotSeuratHVG0.0250.0000.025
plotSeuratJackStraw0.0240.0000.024
plotSeuratReduction0.0240.0000.024
plotSoupXResults000
plotTSCANClusterDEG5.0680.0245.092
plotTSCANClusterPseudo2.190.022.21
plotTSCANDimReduceFeatures2.2050.0002.205
plotTSCANPseudotimeGenes1.9860.0081.994
plotTSCANPseudotimeHeatmap2.2760.0642.340
plotTSCANResults1.9420.0001.942
plotTSNE0.4980.0040.501
plotTopHVG0.5200.0030.523
plotUMAP6.8230.0166.837
readSingleCellMatrix0.0050.0000.005
reportCellQC0.1610.0000.162
reportDropletQC0.0240.0000.024
reportQCTool0.1610.0040.165
retrieveSCEIndex0.030.000.03
runBBKNN0.0000.0010.000
runBarcodeRankDrops0.3920.0030.395
runBcds2.3580.0281.472
runCellQC0.1680.0080.176
runClusterSummaryMetrics0.7070.0120.719
runComBatSeq0.4550.0000.455
runCxds0.4620.0000.461
runCxdsBcdsHybrid2.4180.0161.522
runDEAnalysis0.7120.0080.719
runDecontX7.6930.0727.765
runDimReduce0.4210.0000.422
runDoubletFinder31.955 0.17232.127
runDropletQC0.0270.0000.026
runEmptyDrops6.4130.0126.425
runEnrichR0.4740.0361.834
runFastMNN1.7480.1081.856
runFeatureSelection0.2030.0000.203
runFindMarker3.2890.1843.473
runGSVA0.9010.1481.049
runHarmony0.0380.0040.042
runKMeans0.4250.0360.461
runLimmaBC0.0730.0080.081
runMNNCorrect0.5220.0600.582
runModelGeneVar0.4400.0360.476
runNormalization2.4320.5242.956
runPerCellQC0.4910.0320.523
runSCANORAMA000
runSCMerge0.0040.0000.005
runScDblFinder19.270 0.54419.815
runScanpyFindClusters0.0260.0000.026
runScanpyFindHVG0.0240.0000.024
runScanpyFindMarkers0.0240.0000.024
runScanpyNormalizeData0.1850.0160.200
runScanpyPCA0.0240.0000.024
runScanpyScaleData0.0230.0000.024
runScanpyTSNE0.0240.0000.024
runScanpyUMAP0.0240.0000.024
runScranSNN0.7120.0680.781
runScrublet0.0240.0000.024
runSeuratFindClusters0.0230.0000.024
runSeuratFindHVG0.8200.0480.868
runSeuratHeatmap0.0250.0000.025
runSeuratICA0.0190.0040.023
runSeuratJackStraw0.0230.0000.023
runSeuratNormalizeData0.0230.0000.023
runSeuratPCA0.0240.0000.023
runSeuratSCTransform30.603 0.79231.395
runSeuratScaleData0.0250.0000.025
runSeuratUMAP0.0210.0040.025
runSingleR0.0350.0000.035
runSoupX000
runTSCAN1.4480.0361.484
runTSCANClusterDEAnalysis1.5660.0281.594
runTSCANDEG1.4550.0321.488
runTSNE0.8720.0040.876
runUMAP7.4180.2327.647
runVAM0.50.00.5
runZINBWaVE0.0040.0000.004
sampleSummaryStats0.2790.0040.283
scaterCPM0.1240.0160.140
scaterPCA0.6230.0040.627
scaterlogNormCounts0.2490.0120.261
sce0.0240.0000.024
sctkListGeneSetCollections0.0720.0040.076
sctkPythonInstallConda0.0010.0000.001
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0830.0000.083
setSCTKDisplayRow0.4360.0040.440
singleCellTK000
subDiffEx0.4940.0160.510
subsetSCECols0.1750.0040.179
subsetSCERows0.4040.0120.416
summarizeSCE0.070.000.07
trimCounts0.1990.0160.215