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This page was generated on 2024-06-11 15:42 -0400 (Tue, 11 Jun 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 RC (2024-04-16 r86468) -- "Puppy Cup" 4679
palomino4Windows Server 2022 Datacenterx644.4.0 RC (2024-04-16 r86468 ucrt) -- "Puppy Cup" 4414
merida1macOS 12.7.4 Montereyx86_644.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" 4441
kjohnson1macOS 13.6.6 Venturaarm644.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" 4394
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 1937/2239HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.15.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-06-09 14:00 -0400 (Sun, 09 Jun 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)
nebbiolo2Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino4Windows 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: 2.15.0
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: 2024-06-10 10:11:17 -0400 (Mon, 10 Jun 2024)
EndedAt: 2024-06-10 10:42:46 -0400 (Mon, 10 Jun 2024)
EllapsedTime: 1889.5 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 4.4.0 Patched (2024-04-24 r86482)
* 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 ‘2.15.0’
* 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:
    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     50.245  1.124  58.454
plotDoubletFinderResults   47.457  0.308  50.881
runDoubletFinder           41.867  0.373  47.182
runScDblFinder             35.162  0.557  40.229
importExampleData          29.207  2.800  35.629
plotBatchCorrCompare       14.700  0.254  15.531
plotScdsHybridResults      13.824  0.174  15.707
plotTSCANClusterDEG        13.210  0.179  14.901
plotBcdsResults            12.214  0.454  14.577
plotDecontXResults         12.020  0.157  12.816
plotFindMarkerHeatmap      11.937  0.079  12.918
plotDEGViolin              11.037  0.203  12.465
plotEmptyDropsResults      10.569  0.077  11.354
plotEmptyDropsScatter      10.470  0.050  11.015
runEmptyDrops               9.897  0.074  11.286
runDecontX                  9.589  0.145  10.643
detectCellOutlier           9.504  0.180  10.523
runSeuratSCTransform        9.379  0.133  10.837
convertSCEToSeurat          9.168  0.315  10.096
plotDEGRegression           9.233  0.150  11.207
plotCxdsResults             8.950  0.157   9.898
runFindMarker               8.530  0.107   9.815
getFindMarkerTopTable       8.488  0.118   9.491
plotUMAP                    8.453  0.106   9.250
runUMAP                     8.334  0.070   9.440
plotDEGHeatmap              7.311  0.161   8.392
plotTSCANDimReduceFeatures  5.774  0.058   6.639
plotTSCANPseudotimeHeatmap  5.718  0.060   6.567
plotTSCANClusterPseudo      5.670  0.045   6.442
plotTSCANPseudotimeGenes    5.391  0.049   6.171
plotRunPerCellQCResults     5.371  0.046   5.756
plotTSCANResults            5.334  0.049   6.096
importGeneSetsFromMSigDB    4.934  0.181   5.569
* 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 4.4.0 Patched (2024-04-24 r86482) -- "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 
  0.363   0.115   0.482

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: 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
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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 
485.305  10.055 546.774

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0050.0050.010
SEG0.0040.0050.009
calcEffectSizes0.5000.0570.602
combineSCE3.4760.1404.162
computeZScore0.4640.0200.556
convertSCEToSeurat 9.168 0.31510.096
convertSeuratToSCE1.1990.0181.317
dedupRowNames0.1240.0070.139
detectCellOutlier 9.504 0.18010.523
diffAbundanceFET0.1030.0070.123
discreteColorPalette0.0110.0010.015
distinctColors0.0040.0000.004
downSampleCells1.4350.1701.746
downSampleDepth1.2470.0631.429
expData-ANY-character-method0.7330.0090.802
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.8370.0100.929
expData-set0.8470.0240.977
expData0.8020.0720.984
expDataNames-ANY-method0.8070.0770.949
expDataNames0.7090.0100.774
expDeleteDataTag0.0640.0030.075
expSetDataTag0.0440.0030.050
expTaggedData0.0510.0020.058
exportSCE0.0420.0070.051
exportSCEtoAnnData0.1390.0040.157
exportSCEtoFlatFile0.1400.0040.155
featureIndex0.0790.0060.089
generateSimulatedData0.0950.0080.110
getBiomarker0.1150.0080.136
getDEGTopTable2.0630.0562.391
getDiffAbundanceResults0.0970.0050.143
getEnrichRResult0.7330.0582.416
getFindMarkerTopTable8.4880.1189.491
getMSigDBTable0.0080.0060.018
getPathwayResultNames0.0470.0080.060
getSampleSummaryStatsTable0.7530.0090.841
getSoupX0.0000.0010.000
getTSCANResults4.2700.0644.704
getTopHVG2.6540.0262.898
importAnnData0.0020.0010.003
importBUStools0.6540.0070.724
importCellRanger2.7560.0653.135
importCellRangerV2Sample0.6500.0060.729
importCellRangerV3Sample0.9820.0261.099
importDropEst0.7490.0080.817
importExampleData29.207 2.80035.629
importGeneSetsFromCollection1.7340.1482.086
importGeneSetsFromGMT0.1460.0120.179
importGeneSetsFromList0.3050.0110.343
importGeneSetsFromMSigDB4.9340.1815.569
importMitoGeneSet0.1190.0180.154
importOptimus0.0030.0010.006
importSEQC0.6950.0400.804
importSTARsolo0.6740.0150.764
iterateSimulations0.8380.0200.953
listSampleSummaryStatsTables1.0060.0141.105
mergeSCEColData1.1380.0321.266
mouseBrainSubsetSCE0.0720.0060.086
msigdb_table0.0030.0050.008
plotBarcodeRankDropsResults2.0240.0282.235
plotBarcodeRankScatter2.1690.0212.392
plotBatchCorrCompare14.700 0.25415.531
plotBatchVariance0.7820.0711.011
plotBcdsResults12.214 0.45414.577
plotBubble2.1830.0882.586
plotClusterAbundance2.0070.0222.142
plotCxdsResults8.9500.1579.898
plotDEGHeatmap7.3110.1618.392
plotDEGRegression 9.233 0.15011.207
plotDEGViolin11.037 0.20312.465
plotDEGVolcano2.3400.0272.471
plotDecontXResults12.020 0.15712.816
plotDimRed0.6670.0110.748
plotDoubletFinderResults47.457 0.30850.881
plotEmptyDropsResults10.569 0.07711.354
plotEmptyDropsScatter10.470 0.05011.015
plotFindMarkerHeatmap11.937 0.07912.918
plotMASTThresholdGenes4.0380.0534.289
plotPCA1.1930.0201.280
plotPathway2.0780.0272.207
plotRunPerCellQCResults5.3710.0465.756
plotSCEBarAssayData0.4390.0130.541
plotSCEBarColData0.3450.0100.393
plotSCEBatchFeatureMean0.5530.0080.668
plotSCEDensity0.5730.0140.616
plotSCEDensityAssayData0.3890.0110.426
plotSCEDensityColData0.5020.0130.523
plotSCEDimReduceColData1.7480.0191.797
plotSCEDimReduceFeatures0.9520.0180.990
plotSCEHeatmap1.6320.0201.771
plotSCEScatter0.8550.0160.942
plotSCEViolin0.5910.0130.659
plotSCEViolinAssayData0.6760.0160.756
plotSCEViolinColData0.5750.0140.640
plotScDblFinderResults50.245 1.12458.454
plotScanpyDotPlot0.0440.0080.057
plotScanpyEmbedding0.0500.0060.062
plotScanpyHVG0.0490.0040.061
plotScanpyHeatmap0.0440.0070.061
plotScanpyMarkerGenes0.0400.0090.101
plotScanpyMarkerGenesDotPlot0.0400.0050.061
plotScanpyMarkerGenesHeatmap0.0420.0060.054
plotScanpyMarkerGenesMatrixPlot0.0420.0040.053
plotScanpyMarkerGenesViolin0.0400.0040.046
plotScanpyMatrixPlot0.0410.0050.049
plotScanpyPCA0.0410.0050.048
plotScanpyPCAGeneRanking0.0400.0050.048
plotScanpyPCAVariance0.0390.0040.049
plotScanpyViolin0.0410.0070.057
plotScdsHybridResults13.824 0.17415.707
plotScrubletResults0.0470.0050.058
plotSeuratElbow0.0440.0050.056
plotSeuratHVG0.0450.0070.062
plotSeuratJackStraw0.0490.0060.065
plotSeuratReduction0.0430.0050.058
plotSoupXResults0.0000.0010.001
plotTSCANClusterDEG13.210 0.17914.901
plotTSCANClusterPseudo5.6700.0456.442
plotTSCANDimReduceFeatures5.7740.0586.639
plotTSCANPseudotimeGenes5.3910.0496.171
plotTSCANPseudotimeHeatmap5.7180.0606.567
plotTSCANResults5.3340.0496.096
plotTSNE1.2470.0181.458
plotTopHVG1.1870.0241.338
plotUMAP8.4530.1069.250
readSingleCellMatrix0.0100.0020.011
reportCellQC0.4220.0100.659
reportDropletQC0.0470.0070.064
reportQCTool0.4190.0100.454
retrieveSCEIndex0.0540.0050.064
runBBKNN0.0000.0010.002
runBarcodeRankDrops0.9640.0181.012
runBcds3.8760.1054.273
runCellQC0.4290.0200.478
runClusterSummaryMetrics1.7810.0731.925
runComBatSeq1.0380.0351.697
runCxds1.0870.0221.152
runCxdsBcdsHybrid3.8570.0974.093
runDEAnalysis1.7270.0561.861
runDecontX 9.589 0.14510.643
runDimReduce1.1070.0221.254
runDoubletFinder41.867 0.37347.182
runDropletQC0.0460.0080.063
runEmptyDrops 9.897 0.07411.286
runEnrichR0.6780.0422.049
runFastMNN4.2600.0734.864
runFeatureSelection0.4810.0140.563
runFindMarker8.5300.1079.815
runGSVA2.0700.0592.391
runHarmony0.0860.0020.100
runKMeans1.0720.0211.255
runLimmaBC0.1940.0040.233
runMNNCorrect1.3430.0181.550
runModelGeneVar1.1300.0151.318
runNormalization3.2760.0413.731
runPerCellQC1.2140.0191.387
runSCANORAMA000
runSCMerge0.0070.0020.012
runScDblFinder35.162 0.55740.229
runScanpyFindClusters0.0450.0060.062
runScanpyFindHVG0.0430.0050.054
runScanpyFindMarkers0.0390.0040.048
runScanpyNormalizeData0.4630.0070.525
runScanpyPCA0.0440.0040.053
runScanpyScaleData0.0410.0060.052
runScanpyTSNE0.0450.0060.062
runScanpyUMAP0.0470.0050.057
runScranSNN1.8170.0252.057
runScrublet0.0420.0030.047
runSeuratFindClusters0.0490.0040.058
runSeuratFindHVG1.9120.1032.321
runSeuratHeatmap0.0440.0040.054
runSeuratICA0.0390.0040.051
runSeuratJackStraw0.0400.0040.049
runSeuratNormalizeData0.0450.0080.061
runSeuratPCA0.0470.0060.058
runSeuratSCTransform 9.379 0.13310.837
runSeuratScaleData0.0440.0080.058
runSeuratUMAP0.0440.0060.054
runSingleR0.0900.0060.124
runSoupX0.0000.0000.001
runTSCAN3.6010.0514.099
runTSCANClusterDEAnalysis3.8730.0404.462
runTSCANDEG3.6850.0354.187
runTSNE1.7150.0221.967
runUMAP8.3340.0709.440
runVAM1.3150.0141.515
runZINBWaVE0.0070.0010.009
sampleSummaryStats0.7260.0120.826
scaterCPM0.2360.0040.267
scaterPCA1.5690.0191.798
scaterlogNormCounts0.4990.0100.583
sce0.0440.0090.059
sctkListGeneSetCollections0.1760.0100.208
sctkPythonInstallConda0.0000.0010.000
sctkPythonInstallVirtualEnv000
selectSCTKConda0.0010.0010.001
selectSCTKVirtualEnvironment000
setRowNames0.3060.0170.371
setSCTKDisplayRow0.9200.0161.056
singleCellTK0.0000.0010.001
subDiffEx1.1100.0341.286
subsetSCECols0.4170.0120.475
subsetSCERows0.9620.0181.101
summarizeSCE0.1370.0070.156
trimCounts0.3630.0130.424