Back to Multiple platform build/check report for BioC 3.19:   simplified   long
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This page was generated on 2024-06-18 18:00 -0400 (Tue, 18 Jun 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 (2024-04-24) -- "Puppy Cup" 4758
palomino3Windows Server 2022 Datacenterx644.4.0 (2024-04-24 ucrt) -- "Puppy Cup" 4492
merida1macOS 12.7.4 Montereyx86_644.4.0 (2024-04-24) -- "Puppy Cup" 4464
kjohnson1macOS 13.6.6 Venturaarm644.4.0 (2024-04-24) -- "Puppy Cup" 4464
Click on any hostname to see more info about the system (e.g. compilers)      (*) as reported by 'uname -p', except on Windows and Mac OS X

Package 1992/2300HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.14.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-06-16 14:00 -0400 (Sun, 16 Jun 2024)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: RELEASE_3_19
git_last_commit: cd29b84
git_last_commit_date: 2024-04-30 11:06:02 -0400 (Tue, 30 Apr 2024)
nebbiolo1Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino3Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
merida1macOS 12.7.4 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson1macOS 13.6.6 Ventura / arm64  OK    OK    OK    OK  UNNEEDED, same version is already published

CHECK results for singleCellTK on merida1

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

raw results


Summary

Package: singleCellTK
Version: 2.14.0
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.14.0.tar.gz
StartedAt: 2024-06-17 12:19:34 -0400 (Mon, 17 Jun 2024)
EndedAt: 2024-06-17 12:50:30 -0400 (Mon, 17 Jun 2024)
EllapsedTime: 1855.9 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

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


* using log directory ‘/Users/biocbuild/bbs-3.19-bioc/meat/singleCellTK.Rcheck’
* using R version 4.4.0 (2024-04-24)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.4
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.14.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... NOTE
  installed size is  6.8Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    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
plotScDblFinderResults     48.935  1.249  52.501
plotDoubletFinderResults   47.519  0.322  49.199
runDoubletFinder           41.904  0.277  44.652
importExampleData          28.536  2.959  34.615
runScDblFinder             19.985  0.463  21.482
plotBatchCorrCompare       14.942  0.221  16.254
plotScdsHybridResults      13.920  0.183  15.368
plotTSCANClusterDEG        13.059  0.203  14.605
plotBcdsResults            12.778  0.395  13.912
plotFindMarkerHeatmap      12.315  0.085  12.921
plotDecontXResults         12.196  0.115  12.766
plotDEGViolin              11.016  0.196  11.987
plotEmptyDropsScatter      10.524  0.057  10.821
plotEmptyDropsResults      10.471  0.054  10.824
plotCxdsResults            10.068  0.103  10.629
runEmptyDrops               9.926  0.061  10.450
detectCellOutlier           9.566  0.230  10.740
runSeuratSCTransform        9.648  0.135  10.448
runDecontX                  9.453  0.101  10.747
convertSCEToSeurat          9.170  0.336  10.222
plotDEGRegression           9.366  0.129   9.763
plotUMAP                    8.953  0.086   9.763
runUMAP                     8.751  0.078   9.210
runFindMarker               8.431  0.094   8.977
getFindMarkerTopTable       8.388  0.108   9.115
plotDEGHeatmap              7.433  0.165   9.137
plotTSCANPseudotimeHeatmap  5.845  0.063   6.694
plotTSCANClusterPseudo      5.675  0.061   6.245
plotTSCANDimReduceFeatures  5.605  0.046   6.110
plotTSCANPseudotimeGenes    5.450  0.053   5.937
plotTSCANResults            5.447  0.054   5.940
plotRunPerCellQCResults     5.416  0.051   5.760
importGeneSetsFromMSigDB    4.908  0.170   5.363
runEnrichR                  0.682  0.042   7.993
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

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

Installation output

singleCellTK.Rcheck/00install.out

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


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

Tests output

singleCellTK.Rcheck/tests/spelling.Rout


R version 4.4.0 (2024-04-24) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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

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

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

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
NULL
> 
> proc.time()
   user  system elapsed 
  0.360   0.122   0.445

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.0 (2024-04-24) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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

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

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

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

Attaching package: 'MatrixGenerics'

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

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

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

Attaching package: 'BiocGenerics'

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

    IQR, mad, sd, var, xtabs

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

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

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

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

    findMatches

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

    I, expand.grid, unname

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

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


Attaching package: 'Biobase'

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

    rowMedians

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

    anyMissing, rowMedians

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

Attaching package: 'Matrix'

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

    expand

Loading required package: S4Arrays
Loading required package: abind

Attaching package: 'S4Arrays'

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

    abind

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

    rowsum

Loading required package: SparseArray

Attaching package: 'DelayedArray'

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

    apply, scale, sweep


Attaching package: 'singleCellTK'

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

    plotPCA

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

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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%
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

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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

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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
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%
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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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'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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**************************************************|
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 21 | SKIP 0 | PASS 224 ]

[ FAIL 0 | WARN 21 | SKIP 0 | PASS 224 ]
> 
> proc.time()
   user  system elapsed 
474.260  10.669 528.562

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0040.0050.011
SEG0.0040.0040.010
calcEffectSizes0.5000.0570.599
combineSCE3.4270.1343.775
computeZScore0.4570.0210.523
convertSCEToSeurat 9.170 0.33610.222
convertSeuratToSCE1.2020.0371.389
dedupRowNames0.1330.0140.239
detectCellOutlier 9.566 0.23010.740
diffAbundanceFET0.1020.0090.120
discreteColorPalette0.0110.0010.013
distinctColors0.0040.0010.005
downSampleCells1.4430.1721.743
downSampleDepth1.2630.0691.431
expData-ANY-character-method0.7530.0140.827
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.7960.0120.865
expData-set0.8740.0280.994
expData0.8200.0830.988
expDataNames-ANY-method0.7850.0850.943
expDataNames0.7240.0120.792
expDeleteDataTag0.0660.0050.075
expSetDataTag0.0510.0050.061
expTaggedData0.0490.0050.059
exportSCE0.0430.0060.054
exportSCEtoAnnData0.1390.0050.155
exportSCEtoFlatFile0.1410.0040.161
featureIndex0.0750.0060.087
generateSimulatedData0.0990.0130.121
getBiomarker0.1110.0100.129
getDEGTopTable2.0270.0592.249
getDiffAbundanceResults0.0920.0060.107
getEnrichRResult0.7430.0602.168
getFindMarkerTopTable8.3880.1089.115
getMSigDBTable0.0080.0080.016
getPathwayResultNames0.0420.0060.050
getSampleSummaryStatsTable0.7890.0130.859
getSoupX0.0010.0010.000
getTSCANResults4.3410.0834.740
getTopHVG2.6520.0342.901
importAnnData0.0030.0010.006
importBUStools0.6670.0110.739
importCellRanger2.7150.0722.998
importCellRangerV2Sample0.6620.0080.723
importCellRangerV3Sample0.9860.0271.130
importDropEst0.7730.0080.829
importExampleData28.536 2.95934.615
importGeneSetsFromCollection1.7170.1491.961
importGeneSetsFromGMT0.1320.0120.175
importGeneSetsFromList0.2700.0100.364
importGeneSetsFromMSigDB4.9080.1705.363
importMitoGeneSet0.1160.0140.141
importOptimus0.0030.0010.006
importSEQC0.6600.0310.754
importSTARsolo0.6400.0110.716
iterateSimulations0.8040.0160.885
listSampleSummaryStatsTables0.9610.0151.040
mergeSCEColData1.0660.0341.180
mouseBrainSubsetSCE0.0700.0080.087
msigdb_table0.0030.0050.007
plotBarcodeRankDropsResults1.9340.0322.128
plotBarcodeRankScatter2.1600.0222.388
plotBatchCorrCompare14.942 0.22116.254
plotBatchVariance0.8060.0590.900
plotBcdsResults12.778 0.39513.912
plotBubble2.4840.0882.699
plotClusterAbundance2.1850.0172.298
plotCxdsResults10.068 0.10310.629
plotDEGHeatmap7.4330.1659.137
plotDEGRegression9.3660.1299.763
plotDEGViolin11.016 0.19611.987
plotDEGVolcano2.3310.0282.421
plotDecontXResults12.196 0.11512.766
plotDimRed0.6380.0080.660
plotDoubletFinderResults47.519 0.32249.199
plotEmptyDropsResults10.471 0.05410.824
plotEmptyDropsScatter10.524 0.05710.821
plotFindMarkerHeatmap12.315 0.08512.921
plotMASTThresholdGenes4.0940.0634.365
plotPCA1.2280.0221.309
plotPathway2.1070.0442.283
plotRunPerCellQCResults5.4160.0515.760
plotSCEBarAssayData0.4220.0110.471
plotSCEBarColData0.3360.0090.369
plotSCEBatchFeatureMean0.5350.0060.557
plotSCEDensity0.5430.0140.580
plotSCEDensityAssayData0.3910.0110.418
plotSCEDensityColData0.5080.0120.542
plotSCEDimReduceColData1.7170.0191.760
plotSCEDimReduceFeatures0.9360.0130.957
plotSCEHeatmap1.5760.0171.666
plotSCEScatter0.8280.0150.881
plotSCEViolin0.6030.0130.668
plotSCEViolinAssayData0.6520.0130.681
plotSCEViolinColData0.5370.0110.563
plotScDblFinderResults48.935 1.24952.501
plotScanpyDotPlot0.0450.0050.054
plotScanpyEmbedding0.0420.0040.055
plotScanpyHVG0.0410.0070.071
plotScanpyHeatmap0.0420.0060.062
plotScanpyMarkerGenes0.0460.0140.088
plotScanpyMarkerGenesDotPlot0.0400.0090.066
plotScanpyMarkerGenesHeatmap0.0430.0080.057
plotScanpyMarkerGenesMatrixPlot0.0520.0040.058
plotScanpyMarkerGenesViolin0.0440.0040.054
plotScanpyMatrixPlot0.0430.0040.053
plotScanpyPCA0.0420.0040.052
plotScanpyPCAGeneRanking0.0440.0050.054
plotScanpyPCAVariance0.0410.0060.052
plotScanpyViolin0.0440.0050.053
plotScdsHybridResults13.920 0.18315.368
plotScrubletResults0.0430.0050.064
plotSeuratElbow0.0470.0030.055
plotSeuratHVG0.0480.0040.056
plotSeuratJackStraw0.0490.0070.062
plotSeuratReduction0.0480.0050.058
plotSoupXResults0.0000.0010.001
plotTSCANClusterDEG13.059 0.20314.605
plotTSCANClusterPseudo5.6750.0616.245
plotTSCANDimReduceFeatures5.6050.0466.110
plotTSCANPseudotimeGenes5.4500.0535.937
plotTSCANPseudotimeHeatmap5.8450.0636.694
plotTSCANResults5.4470.0545.940
plotTSNE1.2730.0191.396
plotTopHVG1.2120.0301.360
plotUMAP8.9530.0869.763
readSingleCellMatrix0.0100.0020.012
reportCellQC0.4390.0080.480
reportDropletQC0.0430.0030.048
reportQCTool0.4350.0070.470
retrieveSCEIndex0.0600.0050.070
runBBKNN0.0000.0010.001
runBarcodeRankDrops0.9690.0131.055
runBcds3.9040.0684.297
runCellQC0.4170.0140.457
runClusterSummaryMetrics1.7650.0501.911
runComBatSeq1.0580.0331.262
runCxds1.1290.0141.187
runCxdsBcdsHybrid3.9290.0664.227
runDEAnalysis1.7050.0401.875
runDecontX 9.453 0.10110.747
runDimReduce1.1080.0121.197
runDoubletFinder41.904 0.27744.652
runDropletQC0.0490.0050.055
runEmptyDrops 9.926 0.06110.450
runEnrichR0.6820.0427.993
runFastMNN4.1470.0854.527
runFeatureSelection0.4740.0090.541
runFindMarker8.4310.0948.977
runGSVA2.0120.0582.193
runHarmony0.0900.0020.103
runKMeans1.0980.0221.282
runLimmaBC0.1950.0030.207
runMNNCorrect1.3600.0191.438
runModelGeneVar1.0980.0151.155
runNormalization3.2490.0403.501
runPerCellQC1.2160.0201.277
runSCANORAMA0.0000.0010.001
runSCMerge0.0070.0020.009
runScDblFinder19.985 0.46321.482
runScanpyFindClusters0.0440.0050.051
runScanpyFindHVG0.0390.0050.045
runScanpyFindMarkers0.0450.0040.050
runScanpyNormalizeData0.4670.0070.489
runScanpyPCA0.0440.0040.048
runScanpyScaleData0.0420.0080.050
runScanpyTSNE0.0430.0030.048
runScanpyUMAP0.0420.0050.048
runScranSNN1.8420.0251.949
runScrublet0.0400.0050.046
runSeuratFindClusters0.0410.0030.046
runSeuratFindHVG1.8810.0722.025
runSeuratHeatmap0.0490.0070.059
runSeuratICA0.0460.0080.056
runSeuratJackStraw0.0410.0040.047
runSeuratNormalizeData0.0490.0030.054
runSeuratPCA0.0490.0050.055
runSeuratSCTransform 9.648 0.13510.448
runSeuratScaleData0.0400.0030.045
runSeuratUMAP0.0430.0050.051
runSingleR0.0860.0050.094
runSoupX0.0000.0010.001
runTSCAN3.6570.0433.892
runTSCANClusterDEAnalysis3.8660.0404.099
runTSCANDEG3.7240.0353.920
runTSNE1.7530.0201.845
runUMAP8.7510.0789.210
runVAM1.3130.0161.377
runZINBWaVE0.0070.0020.009
sampleSummaryStats0.7070.0120.747
scaterCPM0.2350.0040.248
scaterPCA1.5920.0161.691
scaterlogNormCounts0.5050.0070.543
sce0.0400.0080.050
sctkListGeneSetCollections0.1720.0110.194
sctkPythonInstallConda0.0010.0000.001
sctkPythonInstallVirtualEnv0.0000.0010.000
selectSCTKConda0.0000.0010.000
selectSCTKVirtualEnvironment0.0000.0000.001
setRowNames0.3000.0150.325
setSCTKDisplayRow0.9260.0120.967
singleCellTK0.0010.0010.002
subDiffEx1.1140.0341.187
subsetSCECols0.4190.0140.452
subsetSCERows0.9830.0141.037
summarizeSCE0.1360.0080.148
trimCounts0.3580.0080.384