Back to Multiple platform build/check report for BioC 3.20:   simplified   long
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This page was generated on 2024-09-27 12:31 -0400 (Fri, 27 Sep 2024).

HostnameOSArch (*)R versionInstalled pkgs
teran2Linux (Ubuntu 24.04.1 LTS)x86_644.4.1 (2024-06-14) -- "Race for Your Life" 4451
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.4.1 (2024-06-14) -- "Race for Your Life" 4417
palomino8Windows Server 2022 Datacenterx644.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 4456
lconwaymacOS 12.7.1 Montereyx86_644.4.1 (2024-06-14) -- "Race for Your Life" 4489
kjohnson3macOS 13.6.5 Venturaarm644.4.1 (2024-06-14) -- "Race for Your Life" 4436
kunpeng2Linux (openEuler 22.03 LTS-SP1)aarch644.4.1 (2024-06-14) -- "Race for Your Life" 4435
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 1955/2262HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.15.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-09-26 13:40 -0400 (Thu, 26 Sep 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)
teran2Linux (Ubuntu 24.04.1 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
nebbiolo2Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    ERROR  
palomino8Windows 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
kunpeng2Linux (openEuler 22.03 LTS-SP1) / aarch64  OK    OK    OK  


CHECK results for singleCellTK on lconway

To the developers/maintainers of the singleCellTK package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/singleCellTK.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results


Summary

Package: singleCellTK
Version: 2.15.0
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.15.0.tar.gz
StartedAt: 2024-09-26 23:53:29 -0400 (Thu, 26 Sep 2024)
EndedAt: 2024-09-27 00:10:55 -0400 (Fri, 27 Sep 2024)
EllapsedTime: 1046.5 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

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


* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/singleCellTK.Rcheck’
* using R version 4.4.1 (2024-06-14)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.15.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... NOTE
  installed size is  6.8Mb
  sub-directories of 1Mb or more:
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) dedupRowNames.Rd:10: Lost braces
    10 | \item{x}{A matrix like or /linkS4class{SingleCellExperiment} object, on which
       |                                       ^
checkRd: (-1) dedupRowNames.Rd:14: Lost braces
    14 | /linkS4class{SingleCellExperiment} object. When set to \code{TRUE}, will
       |             ^
checkRd: (-1) dedupRowNames.Rd:22: Lost braces
    22 | By default, a matrix or /linkS4class{SingleCellExperiment} object
       |                                     ^
checkRd: (-1) dedupRowNames.Rd:24: Lost braces
    24 | When \code{x} is a /linkS4class{SingleCellExperiment} and \code{as.rowData}
       |                                ^
checkRd: (-1) plotBubble.Rd:42: Lost braces
    42 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runClusterSummaryMetrics.Rd:27: Lost braces
    27 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runEmptyDrops.Rd:66: Lost braces
    66 | provided \\linkS4class{SingleCellExperiment} object.
       |                       ^
checkRd: (-1) runSCMerge.Rd:44: Lost braces
    44 | construct pseudo-replicates. The length of code{kmeansK} needs to be the same
       |                                                ^
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
plotDoubletFinderResults 36.682  0.283  37.162
runDoubletFinder         32.552  0.210  32.895
plotScDblFinderResults   30.822  0.788  31.794
importExampleData        23.573  2.388  27.163
runScDblFinder           20.218  0.397  20.736
plotBatchCorrCompare     11.963  0.165  12.190
plotScdsHybridResults    10.071  0.176  10.298
plotBcdsResults           8.728  0.210   8.977
plotDecontXResults        8.714  0.107   8.879
runUMAP                   7.322  0.086   7.441
plotCxdsResults           7.205  0.076   7.310
runDecontX                7.043  0.053   7.131
plotUMAP                  6.856  0.057   6.941
plotEmptyDropsScatter     6.716  0.045   6.806
plotEmptyDropsResults     6.623  0.045   6.707
plotTSCANClusterDEG       6.355  0.060   6.444
runSeuratSCTransform      6.159  0.133   6.326
detectCellOutlier         6.050  0.204   6.286
runEmptyDrops             6.078  0.029   6.127
plotFindMarkerHeatmap     5.750  0.051   5.842
plotDEGViolin             5.410  0.116   5.383
convertSCEToSeurat        5.015  0.236   5.291
getEnrichRResult          0.358  0.041  12.421
runEnrichR                0.311  0.035   8.944
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

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


Installation output

singleCellTK.Rcheck/00install.out

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


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library’
* installing *source* package ‘singleCellTK’ ...
** using staged installation
** R
** data
** exec
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (singleCellTK)

Tests output

singleCellTK.Rcheck/tests/spelling.Rout


R version 4.4.1 (2024-06-14) -- "Race for Your Life"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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

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

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

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
NULL
> 
> proc.time()
   user  system elapsed 
  0.221   0.089   0.289 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.1 (2024-06-14) -- "Race for Your Life"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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

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

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

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

Attaching package: 'MatrixGenerics'

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

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

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

Attaching package: 'BiocGenerics'

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

    IQR, mad, sd, var, xtabs

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

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

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

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

    findMatches

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

    I, expand.grid, unname

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

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


Attaching package: 'Biobase'

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

    rowMedians

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

    anyMissing, rowMedians

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

Attaching package: 'Matrix'

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

    expand

Loading required package: S4Arrays
Loading required package: abind

Attaching package: 'S4Arrays'

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

    abind

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

    rowsum

Loading required package: SparseArray

Attaching package: 'DelayedArray'

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

    apply, scale, sweep


Attaching package: 'singleCellTK'

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

    plotPCA

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

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

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

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

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

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

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

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

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

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 20 | SKIP 0 | PASS 224 ]

[ FAIL 0 | WARN 20 | SKIP 0 | PASS 224 ]
> 
> proc.time()
   user  system elapsed 
295.009   7.419 314.304 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0030.006
SEG0.0030.0030.006
calcEffectSizes0.1940.0090.207
combineSCE2.4400.0662.528
computeZScore0.2750.0150.294
convertSCEToSeurat5.0150.2365.291
convertSeuratToSCE0.5780.0170.598
dedupRowNames0.0690.0070.077
detectCellOutlier6.0500.2046.286
diffAbundanceFET0.0650.0050.071
discreteColorPalette0.0080.0010.008
distinctColors0.0020.0000.002
downSampleCells0.7370.0750.817
downSampleDepth0.5700.0220.600
expData-ANY-character-method0.3530.0070.362
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3850.0060.393
expData-set0.3960.0090.408
expData0.3550.0240.382
expDataNames-ANY-method0.3820.0110.394
expDataNames0.3440.0090.354
expDeleteDataTag0.0390.0030.041
expSetDataTag0.0320.0040.036
expTaggedData0.0360.0040.039
exportSCE0.0250.0040.029
exportSCEtoAnnData0.0870.0030.097
exportSCEtoFlatFile0.0970.0030.101
featureIndex0.0460.0030.049
generateSimulatedData0.0620.0070.070
getBiomarker0.0720.0050.077
getDEGTopTable1.0210.0451.071
getDiffAbundanceResults0.0600.0040.068
getEnrichRResult 0.358 0.04112.421
getFindMarkerTopTable3.9470.0604.033
getMSigDBTable0.0040.0040.009
getPathwayResultNames0.0300.0060.047
getSampleSummaryStatsTable0.4110.0070.424
getSoupX0.0000.0000.001
getTSCANResults2.1060.0532.175
getTopHVG1.3890.0231.421
importAnnData0.0010.0010.002
importBUStools0.3180.0060.327
importCellRanger1.3000.0411.361
importCellRangerV2Sample0.2930.0030.298
importCellRangerV3Sample0.5350.0200.563
importDropEst0.3680.0050.376
importExampleData23.573 2.38827.163
importGeneSetsFromCollection0.8460.1370.992
importGeneSetsFromGMT0.0750.0070.082
importGeneSetsFromList0.1600.0060.166
importGeneSetsFromMSigDB2.7920.1152.929
importMitoGeneSet0.0590.0060.066
importOptimus0.0020.0010.003
importSEQC0.2920.0050.300
importSTARsolo0.3160.0050.327
iterateSimulations0.4350.0170.455
listSampleSummaryStatsTables0.4700.0080.482
mergeSCEColData0.6170.0260.655
mouseBrainSubsetSCE0.0410.0040.045
msigdb_table0.0010.0020.004
plotBarcodeRankDropsResults0.9770.0181.002
plotBarcodeRankScatter1.0500.0131.068
plotBatchCorrCompare11.963 0.16512.190
plotBatchVariance0.3730.0350.423
plotBcdsResults8.7280.2108.977
plotBubble1.2690.0621.346
plotClusterAbundance0.9570.0110.971
plotCxdsResults7.2050.0767.310
plotDEGHeatmap3.5590.1313.716
plotDEGRegression4.4630.0794.393
plotDEGViolin5.4100.1165.383
plotDEGVolcano1.2410.0171.268
plotDecontXResults8.7140.1078.879
plotDimRed0.3150.0090.326
plotDoubletFinderResults36.682 0.28337.162
plotEmptyDropsResults6.6230.0456.707
plotEmptyDropsScatter6.7160.0456.806
plotFindMarkerHeatmap5.7500.0515.842
plotMASTThresholdGenes1.9030.0391.954
plotPCA0.6190.0110.634
plotPathway1.0440.0161.067
plotRunPerCellQCResults2.6650.0292.708
plotSCEBarAssayData0.2270.0070.233
plotSCEBarColData0.2730.0110.285
plotSCEBatchFeatureMean0.2470.0050.252
plotSCEDensity0.2610.0070.268
plotSCEDensityAssayData0.2080.0070.218
plotSCEDensityColData0.2520.0070.259
plotSCEDimReduceColData0.8810.0150.902
plotSCEDimReduceFeatures0.5260.0090.539
plotSCEHeatmap0.8110.0100.825
plotSCEScatter0.4450.0090.455
plotSCEViolin0.2830.0080.292
plotSCEViolinAssayData0.3480.0070.357
plotSCEViolinColData0.2940.0080.305
plotScDblFinderResults30.822 0.78831.794
plotScanpyDotPlot0.0300.0040.034
plotScanpyEmbedding0.0290.0020.032
plotScanpyHVG0.0260.0020.028
plotScanpyHeatmap0.0290.0040.033
plotScanpyMarkerGenes0.0270.0050.032
plotScanpyMarkerGenesDotPlot0.0270.0030.030
plotScanpyMarkerGenesHeatmap0.0250.0030.028
plotScanpyMarkerGenesMatrixPlot0.0310.0030.034
plotScanpyMarkerGenesViolin0.0290.0040.034
plotScanpyMatrixPlot0.0300.0040.034
plotScanpyPCA0.0270.0040.031
plotScanpyPCAGeneRanking0.0300.0030.033
plotScanpyPCAVariance0.0310.0030.034
plotScanpyViolin0.0300.0040.035
plotScdsHybridResults10.071 0.17610.298
plotScrubletResults0.0300.0030.033
plotSeuratElbow0.0280.0040.032
plotSeuratHVG0.0370.0040.040
plotSeuratJackStraw0.0330.0040.037
plotSeuratReduction0.0290.0060.036
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plotTSCANClusterPseudo2.7200.0312.762
plotTSCANDimReduceFeatures2.6710.0312.714
plotTSCANPseudotimeGenes2.6120.0292.649
plotTSCANPseudotimeHeatmap2.8590.0272.894
plotTSCANResults2.5820.0272.618
plotTSNE0.5820.0130.597
plotTopHVG0.6140.0150.634
plotUMAP6.8560.0576.941
readSingleCellMatrix0.0070.0010.008
reportCellQC0.2120.0060.219
reportDropletQC0.0250.0060.032
reportQCTool0.2150.0080.226
retrieveSCEIndex0.0390.0040.043
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runBarcodeRankDrops0.4800.0100.495
runBcds2.0860.0562.155
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runClusterSummaryMetrics0.7580.0120.773
runComBatSeq0.4880.0160.508
runCxds0.5800.0300.613
runCxdsBcdsHybrid2.1240.0632.204
runDEAnalysis0.8650.0350.904
runDecontX7.0430.0537.131
runDimReduce0.4950.0120.509
runDoubletFinder32.552 0.21032.895
runDropletQC0.0290.0040.033
runEmptyDrops6.0780.0296.127
runEnrichR0.3110.0358.944
runFastMNN2.1310.0462.191
runFeatureSelection0.2400.0070.248
runFindMarker4.0400.0614.121
runGSVA1.8550.0511.913
runHarmony0.0430.0010.045
runKMeans0.5240.0130.541
runLimmaBC0.0930.0010.094
runMNNCorrect0.6680.0070.677
runModelGeneVar0.5330.0080.543
runNormalization2.3380.0332.376
runPerCellQC0.6200.0110.632
runSCANORAMA0.0000.0010.001
runSCMerge0.0030.0010.004
runScDblFinder20.218 0.39720.736
runScanpyFindClusters0.0260.0050.030
runScanpyFindHVG0.0240.0040.027
runScanpyFindMarkers0.0300.0030.034
runScanpyNormalizeData0.2370.0080.246
runScanpyPCA0.0320.0060.038
runScanpyScaleData0.0310.0040.034
runScanpyTSNE0.0320.0040.036
runScanpyUMAP0.0270.0030.030
runScranSNN0.8810.0210.905
runScrublet0.0260.0050.031
runSeuratFindClusters0.0270.0040.031
runSeuratFindHVG0.9780.1401.129
runSeuratHeatmap0.0280.0050.034
runSeuratICA0.0250.0030.028
runSeuratJackStraw0.0290.0050.035
runSeuratNormalizeData0.0270.0050.032
runSeuratPCA0.0270.0040.031
runSeuratSCTransform6.1590.1336.326
runSeuratScaleData0.0330.0040.038
runSeuratUMAP0.0290.0040.034
runSingleR0.0410.0030.044
runSoupX0.0000.0000.001
runTSCAN1.6950.0291.730
runTSCANClusterDEAnalysis1.8690.0341.912
runTSCANDEG1.8400.0291.879
runTSNE0.9000.0170.926
runUMAP7.3220.0867.441
runVAM0.6010.0100.611
runZINBWaVE0.0050.0010.006
sampleSummaryStats0.3390.0070.346
scaterCPM0.1400.0090.151
scaterPCA0.7070.0140.723
scaterlogNormCounts0.2690.0110.281
sce0.0270.0060.033
sctkListGeneSetCollections0.0870.0090.098
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.1040.0050.109
setSCTKDisplayRow0.4320.0260.460
singleCellTK0.0000.0000.001
subDiffEx0.6520.0370.693
subsetSCECols0.2060.0090.223
subsetSCERows0.4580.0090.468
summarizeSCE0.0830.0060.089
trimCounts0.2160.0180.234