Back to Multiple platform build/check report for BioC 3.19:   simplified   long
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This page was generated on 2024-07-03 10:22 -0400 (Wed, 03 Jul 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 (2024-04-24) -- "Puppy Cup" 4757
palomino7Windows Server 2022 Datacenterx644.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 4480
merida1macOS 12.7.4 Montereyx86_644.4.0 (2024-04-24) -- "Puppy Cup" 4511
kjohnson1macOS 13.6.6 Venturaarm644.4.0 (2024-04-24) -- "Puppy Cup" 4469
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: 2024-06-30 14:00 -0400 (Sun, 30 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
palomino7Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  
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: 2024-07-01 12:06:59 -0400 (Mon, 01 Jul 2024)
EndedAt: 2024-07-01 12:40:14 -0400 (Mon, 01 Jul 2024)
EllapsedTime: 1995.1 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     51.298  1.325  63.379
plotDoubletFinderResults   47.378  0.392  52.821
runDoubletFinder           41.809  0.282  47.202
runScDblFinder             33.606  0.538  38.087
importExampleData          27.568  2.908  35.996
plotBatchCorrCompare       15.519  0.228  18.155
plotScdsHybridResults      13.892  0.181  16.313
plotTSCANClusterDEG        13.383  0.194  15.909
plotBcdsResults            12.805  0.422  15.602
plotDecontXResults         12.546  0.147  14.880
plotFindMarkerHeatmap      12.294  0.112  14.339
plotDEGViolin              11.230  0.197  12.966
plotEmptyDropsScatter      10.610  0.080  12.148
plotEmptyDropsResults      10.564  0.070  11.970
detectCellOutlier           9.728  0.257  12.166
runEmptyDrops               9.892  0.061  11.245
runSeuratSCTransform        9.727  0.143  11.680
plotCxdsResults             9.625  0.110  11.073
convertSCEToSeurat          9.264  0.391  12.333
runDecontX                  9.479  0.085  10.863
plotDEGRegression           9.426  0.123  10.769
getFindMarkerTopTable       8.580  0.094  10.035
runUMAP                     8.573  0.086   9.798
plotUMAP                    8.513  0.099  10.140
runFindMarker               8.478  0.085   9.653
plotDEGHeatmap              7.510  0.178  10.202
plotTSCANPseudotimeHeatmap  5.864  0.059   7.025
plotTSCANClusterPseudo      5.817  0.063   7.049
plotTSCANDimReduceFeatures  5.808  0.060   7.061
plotTSCANPseudotimeGenes    5.507  0.054   6.498
plotTSCANResults            5.456  0.056   6.447
plotRunPerCellQCResults     5.392  0.064   6.472
importGeneSetsFromMSigDB    4.899  0.211   6.097
getTSCANResults             4.582  0.109   5.526
plotMASTThresholdGenes      4.170  0.062   5.127
getEnrichRResult            0.766  0.062  12.595
* 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 
  0.364   0.121   0.458

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
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 
482.783  11.235 563.651

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0040.0040.014
SEG0.0040.0040.011
calcEffectSizes0.5030.0580.652
combineSCE3.5150.1464.376
computeZScore0.4620.0220.584
convertSCEToSeurat 9.264 0.39112.333
convertSeuratToSCE1.1960.0171.396
dedupRowNames0.1340.0100.170
detectCellOutlier 9.728 0.25712.166
diffAbundanceFET0.1170.0080.148
discreteColorPalette0.0130.0010.017
distinctColors0.0050.0010.005
downSampleCells1.4540.1711.910
downSampleDepth1.2910.0691.610
expData-ANY-character-method0.7760.0130.916
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.8380.0151.008
expData-set0.8740.0171.053
expData0.8060.0671.013
expDataNames-ANY-method0.8000.0841.050
expDataNames0.7380.0130.878
expDeleteDataTag0.0680.0040.081
expSetDataTag0.0480.0040.060
expTaggedData0.0490.0050.064
exportSCE0.0410.0070.055
exportSCEtoAnnData0.1420.0070.186
exportSCEtoFlatFile0.1400.0090.189
featureIndex0.0710.0100.096
generateSimulatedData0.0980.0100.128
getBiomarker0.1140.0090.145
getDEGTopTable2.1410.0672.568
getDiffAbundanceResults0.1020.0070.138
getEnrichRResult 0.766 0.06212.595
getFindMarkerTopTable 8.580 0.09410.035
getMSigDBTable0.0080.0080.019
getPathwayResultNames0.0440.0070.060
getSampleSummaryStatsTable0.7540.0100.868
getSoupX000
getTSCANResults4.5820.1095.526
getTopHVG2.7760.0353.291
importAnnData0.0020.0020.006
importBUStools0.6910.0100.810
importCellRanger2.6730.0653.319
importCellRangerV2Sample0.6370.0090.767
importCellRangerV3Sample0.9630.0251.154
importDropEst0.7410.0070.872
importExampleData27.568 2.90835.996
importGeneSetsFromCollection1.6950.1462.144
importGeneSetsFromGMT0.1360.0110.170
importGeneSetsFromList0.2900.0100.335
importGeneSetsFromMSigDB4.8990.2116.097
importMitoGeneSet0.1080.0130.128
importOptimus0.0030.0010.003
importSEQC0.6420.0260.768
importSTARsolo0.6680.0090.786
iterateSimulations0.8210.0160.981
listSampleSummaryStatsTables1.0120.0151.345
mergeSCEColData1.1200.0371.582
mouseBrainSubsetSCE0.0670.0090.095
msigdb_table0.0030.0040.007
plotBarcodeRankDropsResults2.0070.0402.494
plotBarcodeRankScatter2.1870.0262.777
plotBatchCorrCompare15.519 0.22818.155
plotBatchVariance0.8090.0711.085
plotBcdsResults12.805 0.42215.602
plotBubble2.4710.0832.905
plotClusterAbundance2.1580.0242.448
plotCxdsResults 9.625 0.11011.073
plotDEGHeatmap 7.510 0.17810.202
plotDEGRegression 9.426 0.12310.769
plotDEGViolin11.230 0.19712.966
plotDEGVolcano2.4320.0322.849
plotDecontXResults12.546 0.14714.880
plotDimRed0.6870.0240.855
plotDoubletFinderResults47.378 0.39252.821
plotEmptyDropsResults10.564 0.07011.970
plotEmptyDropsScatter10.610 0.08012.148
plotFindMarkerHeatmap12.294 0.11214.339
plotMASTThresholdGenes4.1700.0625.127
plotPCA1.1770.0351.428
plotPathway2.1220.0302.578
plotRunPerCellQCResults5.3920.0646.472
plotSCEBarAssayData0.4390.0130.558
plotSCEBarColData0.3460.0090.415
plotSCEBatchFeatureMean0.5460.0070.686
plotSCEDensity0.5800.0140.719
plotSCEDensityAssayData0.4000.0120.489
plotSCEDensityColData0.5140.0140.634
plotSCEDimReduceColData1.7890.0292.399
plotSCEDimReduceFeatures0.9980.0191.216
plotSCEHeatmap1.6200.0181.966
plotSCEScatter0.8720.0161.071
plotSCEViolin0.5990.0190.730
plotSCEViolinAssayData0.6980.0130.868
plotSCEViolinColData0.5870.0160.716
plotScDblFinderResults51.298 1.32563.379
plotScanpyDotPlot0.0430.0080.497
plotScanpyEmbedding0.0390.0060.048
plotScanpyHVG0.0420.0030.046
plotScanpyHeatmap0.0380.0050.044
plotScanpyMarkerGenes0.0430.0050.055
plotScanpyMarkerGenesDotPlot0.0420.0060.048
plotScanpyMarkerGenesHeatmap0.0430.0050.049
plotScanpyMarkerGenesMatrixPlot0.0410.0050.049
plotScanpyMarkerGenesViolin0.0390.0040.044
plotScanpyMatrixPlot0.0410.0030.050
plotScanpyPCA0.0420.0050.050
plotScanpyPCAGeneRanking0.0430.0030.047
plotScanpyPCAVariance0.0410.0030.045
plotScanpyViolin0.0390.0050.049
plotScdsHybridResults13.892 0.18116.313
plotScrubletResults0.0430.0040.054
plotSeuratElbow0.0420.0040.056
plotSeuratHVG0.0430.0040.055
plotSeuratJackStraw0.0450.0050.060
plotSeuratReduction0.0420.0050.054
plotSoupXResults0.0000.0010.001
plotTSCANClusterDEG13.383 0.19415.909
plotTSCANClusterPseudo5.8170.0637.049
plotTSCANDimReduceFeatures5.8080.0607.061
plotTSCANPseudotimeGenes5.5070.0546.498
plotTSCANPseudotimeHeatmap5.8640.0597.025
plotTSCANResults5.4560.0566.447
plotTSNE1.2830.0221.518
plotTopHVG1.2000.0251.411
plotUMAP 8.513 0.09910.140
readSingleCellMatrix0.0100.0020.013
reportCellQC0.4040.0080.471
reportDropletQC0.0400.0040.049
reportQCTool0.4050.0110.476
retrieveSCEIndex0.0570.0050.072
runBBKNN0.0000.0010.001
runBarcodeRankDrops0.9830.0131.162
runBcds3.8650.0594.553
runCellQC0.4060.0080.474
runClusterSummaryMetrics1.7500.0582.092
runComBatSeq1.0320.0301.292
runCxds1.0810.0151.243
runCxdsBcdsHybrid3.9580.0694.535
runDEAnalysis1.6830.0411.929
runDecontX 9.479 0.08510.863
runDimReduce1.1050.0151.253
runDoubletFinder41.809 0.28247.202
runDropletQC0.0440.0050.056
runEmptyDrops 9.892 0.06111.245
runEnrichR0.6920.0421.910
runFastMNN4.1870.0714.841
runFeatureSelection0.4720.0100.537
runFindMarker8.4780.0859.653
runGSVA1.9950.0582.344
runHarmony0.0850.0020.095
runKMeans1.0860.0181.236
runLimmaBC0.1920.0020.217
runMNNCorrect1.3620.0241.555
runModelGeneVar1.0590.0151.200
runNormalization3.3010.0373.746
runPerCellQC1.2230.0191.341
runSCANORAMA0.0000.0000.001
runSCMerge0.0070.0020.009
runScDblFinder33.606 0.53838.087
runScanpyFindClusters0.0420.0070.056
runScanpyFindHVG0.0430.0050.052
runScanpyFindMarkers0.0440.0060.056
runScanpyNormalizeData0.4620.0080.516
runScanpyPCA0.0520.0050.064
runScanpyScaleData0.0490.0050.063
runScanpyTSNE0.0480.0070.066
runScanpyUMAP0.0430.0050.056
runScranSNN1.8360.0232.093
runScrublet0.0470.0050.059
runSeuratFindClusters0.0400.0060.049
runSeuratFindHVG1.9190.1152.271
runSeuratHeatmap0.0400.0050.049
runSeuratICA0.0420.0040.050
runSeuratJackStraw0.0400.0040.050
runSeuratNormalizeData0.0430.0030.053
runSeuratPCA0.0420.0050.055
runSeuratSCTransform 9.727 0.14311.680
runSeuratScaleData0.0440.0050.054
runSeuratUMAP0.0400.0050.051
runSingleR0.0870.0050.103
runSoupX0.0000.0010.001
runTSCAN3.7130.0524.249
runTSCANClusterDEAnalysis3.9240.0394.504
runTSCANDEG3.7500.0444.258
runTSNE1.7570.0211.995
runUMAP8.5730.0869.798
runVAM1.3290.0201.570
runZINBWaVE0.0070.0020.010
sampleSummaryStats0.6900.0110.790
scaterCPM0.2370.0040.267
scaterPCA1.5930.0201.806
scaterlogNormCounts0.5010.0070.574
sce0.0470.0080.065
sctkListGeneSetCollections0.1760.0160.214
sctkPythonInstallConda0.0010.0000.001
sctkPythonInstallVirtualEnv0.0000.0010.000
selectSCTKConda0.0010.0000.000
selectSCTKVirtualEnvironment0.0000.0010.000
setRowNames0.2940.0140.332
setSCTKDisplayRow0.9380.0121.061
singleCellTK0.0000.0010.001
subDiffEx1.1240.0361.283
subsetSCECols0.4220.0160.491
subsetSCERows0.9570.0151.090
summarizeSCE0.1350.0110.159
trimCounts0.3550.0120.409