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This page was generated on 2024-07-09 17:42 -0400 (Tue, 09 Jul 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.1 (2024-06-14) -- "Race for Your Life" 4709
palomino7Windows Server 2022 Datacenterx644.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 4483
merida1macOS 12.7.4 Montereyx86_644.4.1 (2024-06-14) -- "Race for Your Life" 4512
kjohnson1macOS 13.6.6 Venturaarm644.4.1 (2024-06-14) -- "Race for Your Life" 4461
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: 2024-07-07 14:00 -0400 (Sun, 07 Jul 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  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: 2024-07-08 11:59:47 -0400 (Mon, 08 Jul 2024)
EndedAt: 2024-07-08 12:33:07 -0400 (Mon, 08 Jul 2024)
EllapsedTime: 2000.4 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 4.4.1 (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 12.7.4
* 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     48.404  1.335  60.146
plotDoubletFinderResults   47.053  0.456  52.924
runDoubletFinder           41.852  0.336  48.633
runScDblFinder             37.938  0.641  44.406
importExampleData          28.922  2.872  36.295
plotBatchCorrCompare       14.986  0.217  17.206
plotScdsHybridResults      14.050  0.197  16.991
plotTSCANClusterDEG        13.455  0.259  16.514
plotBcdsResults            12.866  0.368  14.926
plotDecontXResults         12.719  0.141  14.366
plotFindMarkerHeatmap      12.350  0.157  14.908
plotDEGViolin              11.210  0.203  12.430
plotEmptyDropsResults      10.592  0.091  12.377
plotEmptyDropsScatter      10.587  0.090  12.504
detectCellOutlier          10.053  0.236  12.106
runEmptyDrops               9.905  0.075  11.785
runSeuratSCTransform        9.623  0.151  11.625
plotCxdsResults             9.655  0.099  10.460
convertSCEToSeurat          9.324  0.337  11.151
plotDEGRegression           9.428  0.115  10.742
runDecontX                  9.457  0.082  10.949
runUMAP                     8.777  0.102  10.661
plotUMAP                    8.697  0.110  10.285
getFindMarkerTopTable       8.506  0.091   9.590
runFindMarker               8.474  0.098   9.909
plotDEGHeatmap              7.447  0.174   8.473
plotTSCANPseudotimeHeatmap  5.817  0.058   6.636
plotTSCANDimReduceFeatures  5.782  0.057   7.026
plotTSCANClusterPseudo      5.752  0.063   6.945
plotTSCANPseudotimeGenes    5.587  0.059   6.578
plotRunPerCellQCResults     5.491  0.077   6.878
plotTSCANResults            5.410  0.058   6.279
importGeneSetsFromMSigDB    4.907  0.160   5.737
getTSCANResults             4.448  0.073   5.063
runFastMNN                  4.241  0.066   5.047
plotMASTThresholdGenes      4.103  0.070   5.008
getEnrichRResult            0.767  0.062   5.652
* 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 4.4.1 (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 
  0.369   0.119   0.498

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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
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 
486.227  10.956 564.934

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0040.0060.011
SEG0.0050.0040.010
calcEffectSizes0.5020.0590.635
combineSCE3.4640.1494.141
computeZScore0.4670.0190.531
convertSCEToSeurat 9.324 0.33711.151
convertSeuratToSCE1.2000.0121.390
dedupRowNames0.1220.0050.147
detectCellOutlier10.053 0.23612.106
diffAbundanceFET0.1090.0090.137
discreteColorPalette0.0110.0000.013
distinctColors0.0050.0010.007
downSampleCells1.4400.1711.908
downSampleDepth1.2600.0651.546
expData-ANY-character-method0.7150.0120.833
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.8520.0121.001
expData-set0.8420.0140.955
expData0.7990.0640.975
expDataNames-ANY-method0.7980.0810.991
expDataNames0.7400.0120.858
expDeleteDataTag0.0750.0040.088
expSetDataTag0.0600.0060.077
expTaggedData0.0480.0050.057
exportSCE0.0430.0060.054
exportSCEtoAnnData0.1410.0040.164
exportSCEtoFlatFile0.1380.0070.167
featureIndex0.0730.0070.093
generateSimulatedData0.0990.0110.119
getBiomarker0.1190.0110.146
getDEGTopTable2.1720.0602.520
getDiffAbundanceResults0.1060.0050.161
getEnrichRResult0.7670.0625.652
getFindMarkerTopTable8.5060.0919.590
getMSigDBTable0.0070.0060.015
getPathwayResultNames0.0430.0060.053
getSampleSummaryStatsTable0.7510.0110.865
getSoupX0.0000.0000.001
getTSCANResults4.4480.0735.063
getTopHVG2.7330.0603.151
importAnnData0.0020.0010.006
importBUStools0.6400.0090.731
importCellRanger2.6460.0613.079
importCellRangerV2Sample0.6260.0060.697
importCellRangerV3Sample0.9650.0251.104
importDropEst0.7560.0080.835
importExampleData28.922 2.87236.295
importGeneSetsFromCollection1.6900.1472.064
importGeneSetsFromGMT0.1410.0100.172
importGeneSetsFromList0.2940.0090.340
importGeneSetsFromMSigDB4.9070.1605.737
importMitoGeneSet0.1090.0130.138
importOptimus0.0030.0020.006
importSEQC0.6650.0340.805
importSTARsolo0.6830.0200.812
iterateSimulations0.8260.0220.995
listSampleSummaryStatsTables0.9690.0151.103
mergeSCEColData1.1240.0351.334
mouseBrainSubsetSCE0.0690.0080.086
msigdb_table0.0030.0050.009
plotBarcodeRankDropsResults1.9820.0322.342
plotBarcodeRankScatter2.2080.0232.525
plotBatchCorrCompare14.986 0.21717.206
plotBatchVariance0.8290.0801.301
plotBcdsResults12.866 0.36814.926
plotBubble2.4320.0802.693
plotClusterAbundance2.1380.0192.420
plotCxdsResults 9.655 0.09910.460
plotDEGHeatmap7.4470.1748.473
plotDEGRegression 9.428 0.11510.742
plotDEGViolin11.210 0.20312.430
plotDEGVolcano2.4230.0312.632
plotDecontXResults12.719 0.14114.366
plotDimRed0.6690.0130.755
plotDoubletFinderResults47.053 0.45652.924
plotEmptyDropsResults10.592 0.09112.377
plotEmptyDropsScatter10.587 0.09012.504
plotFindMarkerHeatmap12.350 0.15714.908
plotMASTThresholdGenes4.1030.0705.008
plotPCA1.1690.0211.439
plotPathway2.0650.0352.514
plotRunPerCellQCResults5.4910.0776.878
plotSCEBarAssayData0.4330.0130.502
plotSCEBarColData0.3490.0130.438
plotSCEBatchFeatureMean0.5620.0090.687
plotSCEDensity0.5770.0150.709
plotSCEDensityAssayData0.4020.0120.498
plotSCEDensityColData0.5100.0120.617
plotSCEDimReduceColData1.7970.0302.158
plotSCEDimReduceFeatures0.9790.0161.169
plotSCEHeatmap1.6340.0251.968
plotSCEScatter0.8740.0171.059
plotSCEViolin0.5880.0130.717
plotSCEViolinAssayData0.7000.0170.840
plotSCEViolinColData0.5840.0130.699
plotScDblFinderResults48.404 1.33560.146
plotScanpyDotPlot0.0450.0040.064
plotScanpyEmbedding0.0410.0040.051
plotScanpyHVG0.0410.0070.059
plotScanpyHeatmap0.0460.0050.064
plotScanpyMarkerGenes0.0460.0060.059
plotScanpyMarkerGenesDotPlot0.0390.0040.051
plotScanpyMarkerGenesHeatmap0.0440.0050.062
plotScanpyMarkerGenesMatrixPlot0.0480.0050.066
plotScanpyMarkerGenesViolin0.0390.0050.051
plotScanpyMatrixPlot0.0460.0080.077
plotScanpyPCA0.0410.0040.059
plotScanpyPCAGeneRanking0.0470.0060.066
plotScanpyPCAVariance0.0440.0050.061
plotScanpyViolin0.0410.0050.058
plotScdsHybridResults14.050 0.19716.991
plotScrubletResults0.0420.0050.056
plotSeuratElbow0.0420.0060.057
plotSeuratHVG0.0420.0050.052
plotSeuratJackStraw0.0400.0050.060
plotSeuratReduction0.0440.0080.074
plotSoupXResults0.0000.0010.001
plotTSCANClusterDEG13.455 0.25916.514
plotTSCANClusterPseudo5.7520.0636.945
plotTSCANDimReduceFeatures5.7820.0577.026
plotTSCANPseudotimeGenes5.5870.0596.578
plotTSCANPseudotimeHeatmap5.8170.0586.636
plotTSCANResults5.4100.0586.279
plotTSNE1.2850.0191.500
plotTopHVG1.2070.0271.419
plotUMAP 8.697 0.11010.285
readSingleCellMatrix0.0110.0020.013
reportCellQC0.4220.0080.495
reportDropletQC0.0420.0050.055
reportQCTool0.4240.0080.494
retrieveSCEIndex0.0580.0060.075
runBBKNN0.0000.0000.001
runBarcodeRankDrops0.9830.0171.158
runBcds3.8920.0774.567
runCellQC0.4270.0110.508
runClusterSummaryMetrics1.7790.0552.094
runComBatSeq1.0480.0271.331
runCxds1.1160.0181.441
runCxdsBcdsHybrid3.9520.0994.659
runDEAnalysis1.7330.0762.114
runDecontX 9.457 0.08210.949
runDimReduce1.1130.0161.309
runDoubletFinder41.852 0.33648.633
runDropletQC0.0460.0050.056
runEmptyDrops 9.905 0.07511.785
runEnrichR0.6730.0411.783
runFastMNN4.2410.0665.047
runFeatureSelection0.4670.0100.556
runFindMarker8.4740.0989.909
runGSVA2.0340.0612.442
runHarmony0.0880.0030.107
runKMeans1.0670.0191.257
runLimmaBC0.1970.0030.230
runMNNCorrect1.3690.0171.613
runModelGeneVar1.0920.0131.289
runNormalization3.2880.0493.837
runPerCellQC1.2390.0181.356
runSCANORAMA0.0000.0010.001
runSCMerge0.0070.0020.010
runScDblFinder37.938 0.64144.406
runScanpyFindClusters0.0510.0070.072
runScanpyFindHVG0.0560.0060.071
runScanpyFindMarkers0.0460.0040.056
runScanpyNormalizeData0.4620.0080.537
runScanpyPCA0.0430.0040.059
runScanpyScaleData0.0420.0030.053
runScanpyTSNE0.0400.0040.050
runScanpyUMAP0.0400.0050.051
runScranSNN1.7890.0252.078
runScrublet0.0410.0040.058
runSeuratFindClusters0.0450.0060.058
runSeuratFindHVG1.9850.1202.454
runSeuratHeatmap0.0400.0090.058
runSeuratICA0.0450.0060.057
runSeuratJackStraw0.0430.0040.051
runSeuratNormalizeData0.0450.0050.057
runSeuratPCA0.0400.0040.051
runSeuratSCTransform 9.623 0.15111.625
runSeuratScaleData0.0410.0050.056
runSeuratUMAP0.0480.0060.064
runSingleR0.0880.0060.107
runSoupX0.0000.0010.001
runTSCAN3.6840.0564.342
runTSCANClusterDEAnalysis3.9110.0514.600
runTSCANDEG3.7030.0424.181
runTSNE1.7400.0252.029
runUMAP 8.777 0.10210.661
runVAM1.3260.0181.576
runZINBWaVE0.0070.0020.013
sampleSummaryStats0.7050.0110.832
scaterCPM0.2390.0050.287
scaterPCA1.6070.0161.882
scaterlogNormCounts0.5050.0060.618
sce0.0400.0090.051
sctkListGeneSetCollections0.1860.0130.228
sctkPythonInstallConda0.0000.0010.001
sctkPythonInstallVirtualEnv0.0000.0000.001
selectSCTKConda0.0000.0010.001
selectSCTKVirtualEnvironment0.0000.0000.002
setRowNames0.3030.0160.370
setSCTKDisplayRow0.9550.0171.097
singleCellTK0.0000.0010.003
subDiffEx1.1400.0421.368
subsetSCECols0.4160.0110.491
subsetSCERows0.9860.0171.131
summarizeSCE0.1410.0110.163
trimCounts0.3630.0120.429