Back to Mac ARM64 build report for BioC 3.20
ABCDEFGHIJKLMNOPQR[S]TUVWXYZ

This page was generated on 2024-05-13 11:32:14 -0400 (Mon, 13 May 2024).

HostnameOSArch (*)R versionInstalled pkgs
kjohnson1macOS 13.6.6 Venturaarm644.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" 4378
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 1931/2230HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.15.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-05-11 09:00:03 -0400 (Sat, 11 May 2024)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: 4d7a515
git_last_commit_date: 2024-04-30 11:06:02 -0400 (Tue, 30 Apr 2024)
kjohnson1macOS 13.6.6 Ventura / arm64  OK    OK    OK    OK  UNNEEDED, same version is already published

CHECK results for singleCellTK on kjohnson1


To the developers/maintainers of the singleCellTK package:
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results


Summary

Package: singleCellTK
Version: 2.15.0
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.15.0.tar.gz
StartedAt: 2024-05-13 03:25:29 -0400 (Mon, 13 May 2024)
EndedAt: 2024-05-13 03:43:02 -0400 (Mon, 13 May 2024)
EllapsedTime: 1052.7 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.15.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.20-bioc-mac-arm64/meat/singleCellTK.Rcheck’
* using R version 4.4.0 Patched (2024-04-24 r86482)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Ventura 13.6.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.15.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... NOTE
  installed size is  6.9Mb
  sub-directories of 1Mb or more:
    R         1.1Mb
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) dedupRowNames.Rd:10: Lost braces
    10 | \item{x}{A matrix like or /linkS4class{SingleCellExperiment} object, on which
       |                                       ^
checkRd: (-1) dedupRowNames.Rd:14: Lost braces
    14 | /linkS4class{SingleCellExperiment} object. When set to \code{TRUE}, will
       |             ^
checkRd: (-1) dedupRowNames.Rd:22: Lost braces
    22 | By default, a matrix or /linkS4class{SingleCellExperiment} object
       |                                     ^
checkRd: (-1) dedupRowNames.Rd:24: Lost braces
    24 | When \code{x} is a /linkS4class{SingleCellExperiment} and \code{as.rowData}
       |                                ^
checkRd: (-1) plotBubble.Rd:42: Lost braces
    42 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runClusterSummaryMetrics.Rd:27: Lost braces
    27 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runEmptyDrops.Rd:66: Lost braces
    66 | provided \\linkS4class{SingleCellExperiment} object.
       |                       ^
checkRd: (-1) runSCMerge.Rd:44: Lost braces
    44 | construct pseudo-replicates. The length of code{kmeansK} needs to be the same
       |                                                ^
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
plotDoubletFinderResults 43.925  0.213  44.343
runDoubletFinder         39.632  0.168  39.924
plotScDblFinderResults   36.791  0.683  37.789
runScDblFinder           27.244  0.467  27.907
importExampleData        22.545  1.583  26.574
plotBatchCorrCompare     13.906  0.108  14.088
plotScdsHybridResults    11.132  0.163  11.367
plotBcdsResults           9.668  0.183   9.883
plotDecontXResults        9.683  0.058   9.773
runDecontX                8.957  0.050   9.038
plotUMAP                  8.535  0.056   8.630
runUMAP                   8.210  0.060   8.338
detectCellOutlier         7.988  0.118   8.164
plotCxdsResults           8.004  0.057   8.088
runSeuratSCTransform      6.784  0.088   6.915
plotEmptyDropsResults     6.639  0.025   6.684
plotEmptyDropsScatter     6.600  0.025   6.646
runEmptyDrops             6.325  0.021   6.356
plotTSCANClusterDEG       5.309  0.138   5.474
convertSCEToSeurat        4.894  0.183   5.099
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 3 NOTEs
See
  ‘/Users/biocbuild/bbs-3.20-bioc-mac-arm64/meat/singleCellTK.Rcheck/00check.log’
for details.


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library’
* installing *source* package ‘singleCellTK’ ...
** using staged installation
** R
** data
** exec
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (singleCellTK)

Tests output

singleCellTK.Rcheck/tests/spelling.Rout


R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
NULL
> 
> proc.time()
   user  system elapsed 
  0.218   0.064   0.275 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(singleCellTK)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

The following objects are masked from 'package:matrixStats':

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
    union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:utils':

    findMatches

The following objects are masked from 'package:base':

    I, expand.grid, unname

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

The following objects are masked from 'package:matrixStats':

    anyMissing, rowMedians

Loading required package: SingleCellExperiment
Loading required package: DelayedArray
Loading required package: Matrix

Attaching package: 'Matrix'

The following object is masked from 'package:S4Vectors':

    expand

Loading required package: S4Arrays
Loading required package: abind

Attaching package: 'S4Arrays'

The following object is masked from 'package:abind':

    abind

The following object is masked from 'package:base':

    rowsum

Loading required package: SparseArray

Attaching package: 'DelayedArray'

The following objects are masked from 'package:base':

    apply, scale, sweep


Attaching package: 'singleCellTK'

The following object is masked from 'package:BiocGenerics':

    plotPCA

> 
> test_check("singleCellTK")
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 0 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 1 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   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 
303.687   5.689 317.220 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0030.006
SEG0.0040.0040.007
calcEffectSizes0.2150.0180.236
combineSCE1.5090.0471.561
computeZScore0.3230.0090.332
convertSCEToSeurat4.8940.1835.099
convertSeuratToSCE0.5220.0100.534
dedupRowNames0.0730.0040.077
detectCellOutlier7.9880.1188.164
diffAbundanceFET0.0770.0040.082
discreteColorPalette0.0090.0010.010
distinctColors0.0030.0000.003
downSampleCells0.8150.0720.893
downSampleDepth0.6590.0370.699
expData-ANY-character-method0.3350.0070.343
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3750.0080.384
expData-set0.3600.0060.369
expData0.3600.0230.384
expDataNames-ANY-method0.3510.0260.379
expDataNames0.3230.0080.332
expDeleteDataTag0.0490.0030.053
expSetDataTag0.0410.0020.043
expTaggedData0.0380.0030.041
exportSCE0.0330.0050.039
exportSCEtoAnnData0.1400.0030.143
exportSCEtoFlatFile0.1370.0030.140
featureIndex0.0470.0050.052
generateSimulatedData0.0680.0060.075
getBiomarker0.0860.0060.104
getDEGTopTable0.9460.0330.990
getDiffAbundanceResults0.0700.0050.077
getEnrichRResult0.3860.0374.540
getFindMarkerTopTable3.4340.0573.514
getMSigDBTable0.0040.0040.009
getPathwayResultNames0.0380.0040.043
getSampleSummaryStatsTable0.3600.0070.367
getSoupX0.0000.0010.000
getTSCANResults2.0030.0432.059
getTopHVG1.3630.0221.388
importAnnData0.0020.0010.003
importBUStools0.2910.0060.299
importCellRanger1.3090.0421.362
importCellRangerV2Sample0.2600.0030.267
importCellRangerV3Sample0.4360.0160.454
importDropEst0.3560.0050.362
importExampleData22.545 1.58326.574
importGeneSetsFromCollection0.7690.0790.864
importGeneSetsFromGMT0.0930.0080.100
importGeneSetsFromList0.1350.0070.142
importGeneSetsFromMSigDB3.2170.1063.333
importMitoGeneSet0.0690.0110.080
importOptimus0.0020.0010.003
importSEQC0.3390.0130.353
importSTARsolo0.2790.0070.293
iterateSimulations0.4040.0110.417
listSampleSummaryStatsTables0.5150.0090.526
mergeSCEColData0.5360.0250.562
mouseBrainSubsetSCE0.0530.0060.061
msigdb_table0.0020.0030.005
plotBarcodeRankDropsResults0.9800.0201.005
plotBarcodeRankScatter0.9530.0130.968
plotBatchCorrCompare13.906 0.10814.088
plotBatchVariance0.3660.0200.390
plotBcdsResults9.6680.1839.883
plotBubble1.2020.0381.244
plotClusterAbundance0.9000.0080.910
plotCxdsResults8.0040.0578.088
plotDEGHeatmap3.2010.0933.324
plotDEGRegression3.8690.0553.941
plotDEGViolin4.2740.0904.409
plotDEGVolcano1.0030.0131.041
plotDecontXResults9.6830.0589.773
plotDimRed0.3180.0100.329
plotDoubletFinderResults43.925 0.21344.343
plotEmptyDropsResults6.6390.0256.684
plotEmptyDropsScatter6.6000.0256.646
plotFindMarkerHeatmap4.7690.0354.813
plotMASTThresholdGenes1.6500.0351.691
plotPCA0.5180.0130.537
plotPathway0.9060.0130.924
plotRunPerCellQCResults2.2830.0222.340
plotSCEBarAssayData0.2400.0070.252
plotSCEBarColData0.1660.0080.174
plotSCEBatchFeatureMean0.2310.0030.235
plotSCEDensity0.2890.0100.300
plotSCEDensityAssayData0.1900.0080.199
plotSCEDensityColData0.2430.0090.253
plotSCEDimReduceColData0.7780.0160.796
plotSCEDimReduceFeatures0.4620.0130.476
plotSCEHeatmap0.7840.0130.804
plotSCEScatter0.4100.0130.429
plotSCEViolin0.2650.0080.274
plotSCEViolinAssayData0.3300.0090.340
plotSCEViolinColData0.2650.0090.276
plotScDblFinderResults36.791 0.68337.789
plotScanpyDotPlot0.0370.0060.041
plotScanpyEmbedding0.0380.0030.041
plotScanpyHVG0.0370.0090.045
plotScanpyHeatmap0.0350.0010.037
plotScanpyMarkerGenes0.0370.0060.043
plotScanpyMarkerGenesDotPlot0.0370.0020.040
plotScanpyMarkerGenesHeatmap0.0350.0050.040
plotScanpyMarkerGenesMatrixPlot0.0360.0100.049
plotScanpyMarkerGenesViolin0.0370.0100.047
plotScanpyMatrixPlot0.0360.0060.041
plotScanpyPCA0.0370.0040.041
plotScanpyPCAGeneRanking0.0370.0070.043
plotScanpyPCAVariance0.0360.0050.041
plotScanpyViolin0.0370.0040.041
plotScdsHybridResults11.132 0.16311.367
plotScrubletResults0.0370.0070.044
plotSeuratElbow0.0400.0080.047
plotSeuratHVG0.0360.0060.042
plotSeuratJackStraw0.0350.0060.042
plotSeuratReduction0.0360.0060.042
plotSoupXResults0.0010.0000.000
plotTSCANClusterDEG5.3090.1385.474
plotTSCANClusterPseudo2.4220.0312.460
plotTSCANDimReduceFeatures2.4130.0262.465
plotTSCANPseudotimeGenes2.2960.0292.330
plotTSCANPseudotimeHeatmap2.5020.0292.537
plotTSCANResults2.3030.0342.350
plotTSNE0.5820.0120.596
plotTopHVG0.5560.0130.571
plotUMAP8.5350.0568.630
readSingleCellMatrix0.0060.0010.010
reportCellQC0.2060.0070.214
reportDropletQC0.0340.0060.041
reportQCTool0.1970.0040.202
retrieveSCEIndex0.0420.0070.048
runBBKNN0.0010.0010.000
runBarcodeRankDrops0.4850.0070.494
runBcds2.0180.0852.111
runCellQC0.2020.0110.212
runClusterSummaryMetrics0.8250.0290.854
runComBatSeq0.5290.0130.543
runCxds0.5520.0160.569
runCxdsBcdsHybrid2.1490.0932.247
runDEAnalysis0.8540.0250.881
runDecontX8.9570.0509.038
runDimReduce0.5080.0160.525
runDoubletFinder39.632 0.16839.924
runDropletQC0.0380.0050.043
runEmptyDrops6.3250.0216.356
runEnrichR0.3370.0294.010
runFastMNN1.5860.0411.637
runFeatureSelection0.2560.0120.270
runFindMarker3.7930.0633.950
runGSVA0.9430.0340.984
runHarmony0.0410.0020.042
runKMeans0.4840.0190.504
runLimmaBC0.0840.0020.085
runMNNCorrect0.6670.0140.685
runModelGeneVar0.4990.0140.515
runNormalization2.9150.0452.969
runPerCellQC0.5560.0170.576
runSCANORAMA0.0000.0000.001
runSCMerge0.0050.0010.007
runScDblFinder27.244 0.46727.907
runScanpyFindClusters0.0380.0020.040
runScanpyFindHVG0.0350.0030.039
runScanpyFindMarkers0.0360.0020.038
runScanpyNormalizeData0.2110.0040.215
runScanpyPCA0.0360.0050.040
runScanpyScaleData0.0350.0040.039
runScanpyTSNE0.0360.0040.039
runScanpyUMAP0.0360.0040.040
runScranSNN0.8140.0230.839
runScrublet0.0350.0020.036
runSeuratFindClusters0.0370.0030.040
runSeuratFindHVG0.8900.0640.961
runSeuratHeatmap0.0350.0060.043
runSeuratICA0.0340.0050.040
runSeuratJackStraw0.0330.0050.039
runSeuratNormalizeData0.0380.0040.043
runSeuratPCA0.0390.0040.044
runSeuratSCTransform6.7840.0886.915
runSeuratScaleData0.0380.0010.039
runSeuratUMAP0.0340.0100.045
runSingleR0.0410.0040.044
runSoupX000
runTSCAN1.6400.0371.685
runTSCANClusterDEAnalysis1.2090.0251.299
runTSCANDEG1.7080.0281.804
runTSNE1.1300.0251.164
runUMAP8.2100.0608.338
runVAM0.5600.0130.574
runZINBWaVE0.0050.0010.006
sampleSummaryStats0.3200.0050.326
scaterCPM0.1770.0040.183
scaterPCA0.7000.0080.711
scaterlogNormCounts0.3120.0050.317
sce0.0350.0070.042
sctkListGeneSetCollections0.0960.0090.105
sctkPythonInstallConda0.0000.0000.001
sctkPythonInstallVirtualEnv0.0000.0000.001
selectSCTKConda0.0000.0000.001
selectSCTKVirtualEnvironment0.0000.0000.001
setRowNames0.1760.0120.189
setSCTKDisplayRow0.4290.0160.446
singleCellTK0.0000.0010.000
subDiffEx0.5550.0300.589
subsetSCECols0.2060.0090.218
subsetSCERows0.4420.0100.454
summarizeSCE0.0860.0070.093
trimCounts0.2680.0180.287