Back to Multiple platform build/check report for BioC 3.19:   simplified   long
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This page was generated on 2024-07-19 17:41 -0400 (Fri, 19 Jul 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.1 (2024-06-14) -- "Race for Your Life" 4742
palomino7Windows Server 2022 Datacenterx644.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 4484
merida1macOS 12.7.5 Montereyx86_644.4.1 (2024-06-14) -- "Race for Your Life" 4513
kjohnson1macOS 13.6.6 Venturaarm644.4.1 (2024-06-14) -- "Race for Your Life" 4462
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: 2024-07-17 14:00 -0400 (Wed, 17 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  
merida1macOS 12.7.5 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson1macOS 13.6.6 Ventura / arm64  OK    OK    OK    NA  


CHECK results for singleCellTK on kjohnson1

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: 2024-07-19 11:13:49 -0400 (Fri, 19 Jul 2024)
EndedAt: 2024-07-19 11:32:31 -0400 (Fri, 19 Jul 2024)
EllapsedTime: 1122.5 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_2.14.0.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: aarch64-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 Ventura 13.6.6
* 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:
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) dedupRowNames.Rd:10: Lost braces
    10 | \item{x}{A matrix like or /linkS4class{SingleCellExperiment} object, on which
       |                                       ^
checkRd: (-1) dedupRowNames.Rd:14: Lost braces
    14 | /linkS4class{SingleCellExperiment} object. When set to \code{TRUE}, will
       |             ^
checkRd: (-1) dedupRowNames.Rd:22: Lost braces
    22 | By default, a matrix or /linkS4class{SingleCellExperiment} object
       |                                     ^
checkRd: (-1) dedupRowNames.Rd:24: Lost braces
    24 | When \code{x} is a /linkS4class{SingleCellExperiment} and \code{as.rowData}
       |                                ^
checkRd: (-1) plotBubble.Rd:42: Lost braces
    42 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runClusterSummaryMetrics.Rd:27: Lost braces
    27 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runEmptyDrops.Rd:66: Lost braces
    66 | provided \\linkS4class{SingleCellExperiment} object.
       |                       ^
checkRd: (-1) runSCMerge.Rd:44: Lost braces
    44 | construct pseudo-replicates. The length of code{kmeansK} needs to be the same
       |                                                ^
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
plotDoubletFinderResults 45.185  0.271  46.308
runDoubletFinder         40.379  0.227  41.319
plotScDblFinderResults   37.992  0.743  39.357
importExampleData        24.395  1.641  28.594
runScDblFinder           25.037  0.478  25.962
plotBatchCorrCompare     14.399  0.130  14.721
plotScdsHybridResults    11.044  0.155  11.560
plotBcdsResults          10.075  0.194  10.495
plotDecontXResults       10.023  0.071  10.291
runDecontX                9.086  0.054   9.253
plotUMAP                  8.782  0.074   8.980
runUMAP                   8.688  0.065   8.884
plotCxdsResults           8.352  0.076   8.591
detectCellOutlier         8.178  0.130   8.407
runSeuratSCTransform      7.021  0.092   7.282
plotEmptyDropsScatter     6.627  0.034   7.020
runEmptyDrops             6.542  0.030   6.648
plotEmptyDropsResults     6.452  0.033   6.702
plotTSCANClusterDEG       5.842  0.104   6.049
convertSCEToSeurat        5.146  0.199   5.509
plotFindMarkerHeatmap     5.171  0.045   5.452
getEnrichRResult          0.374  0.040  11.261
* 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-arm64/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: aarch64-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 
  0.226   0.074   0.320 

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: aarch64-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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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 
313.496   6.054 338.501 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0040.007
SEG0.0040.0030.007
calcEffectSizes0.2280.0190.252
combineSCE1.5780.0591.681
computeZScore0.3270.0130.364
convertSCEToSeurat5.1460.1995.509
convertSeuratToSCE0.5440.0090.559
dedupRowNames0.0720.0030.075
detectCellOutlier8.1780.1308.407
diffAbundanceFET0.0810.0040.086
discreteColorPalette0.0080.0010.009
distinctColors0.0030.0000.003
downSampleCells0.8420.0740.935
downSampleDepth0.6710.0370.715
expData-ANY-character-method0.3480.0100.369
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3940.0090.412
expData-set0.3700.0070.379
expData0.3520.0230.377
expDataNames-ANY-method0.3810.0280.414
expDataNames0.3350.0080.348
expDeleteDataTag0.0500.0030.057
expSetDataTag0.0400.0040.045
expTaggedData0.0360.0040.041
exportSCE0.0380.0050.047
exportSCEtoAnnData0.1430.0030.158
exportSCEtoFlatFile0.1360.0040.141
featureIndex0.0520.0050.056
generateSimulatedData0.0750.0050.081
getBiomarker0.0750.0060.082
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getDiffAbundanceResults0.0690.0040.073
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getFindMarkerTopTable3.8820.0694.029
getMSigDBTable0.0060.0050.010
getPathwayResultNames0.0380.0050.043
getSampleSummaryStatsTable0.3630.0070.375
getSoupX0.0000.0010.000
getTSCANResults2.1220.0492.188
getTopHVG1.4020.0221.440
importAnnData0.0010.0010.002
importBUStools0.2800.0050.292
importCellRanger1.3700.0441.436
importCellRangerV2Sample0.2820.0040.289
importCellRangerV3Sample0.4510.0180.475
importDropEst0.3420.0050.350
importExampleData24.395 1.64128.594
importGeneSetsFromCollection0.8930.0810.986
importGeneSetsFromGMT0.0860.0070.095
importGeneSetsFromList0.1600.0070.168
importGeneSetsFromMSigDB3.2470.1413.425
importMitoGeneSet0.0740.0100.086
importOptimus0.0020.0000.003
importSEQC0.3650.0130.383
importSTARsolo0.2800.0050.290
iterateSimulations0.4090.0150.430
listSampleSummaryStatsTables0.5490.0080.563
mergeSCEColData0.5560.0240.587
mouseBrainSubsetSCE0.0550.0060.068
msigdb_table0.0010.0030.005
plotBarcodeRankDropsResults1.0160.0211.097
plotBarcodeRankScatter0.9780.0151.022
plotBatchCorrCompare14.399 0.13014.721
plotBatchVariance0.3740.0270.410
plotBcdsResults10.075 0.19410.495
plotBubble1.2380.0381.303
plotClusterAbundance0.9260.0090.949
plotCxdsResults8.3520.0768.591
plotDEGHeatmap3.3140.1023.571
plotDEGRegression4.1010.0594.233
plotDEGViolin4.4110.1064.736
plotDEGVolcano1.2900.0191.342
plotDecontXResults10.023 0.07110.291
plotDimRed0.3260.0070.337
plotDoubletFinderResults45.185 0.27146.308
plotEmptyDropsResults6.4520.0336.702
plotEmptyDropsScatter6.6270.0347.020
plotFindMarkerHeatmap5.1710.0455.452
plotMASTThresholdGenes1.8230.0371.919
plotPCA0.5580.0130.588
plotPathway0.9860.0171.029
plotRunPerCellQCResults2.3760.0262.430
plotSCEBarAssayData0.2450.0090.258
plotSCEBarColData0.1770.0100.190
plotSCEBatchFeatureMean0.2420.0030.247
plotSCEDensity0.3160.0100.329
plotSCEDensityAssayData0.2090.0100.224
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plotSCEDimReduceColData0.8310.0170.862
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plotSCEScatter0.4210.0120.453
plotSCEViolin0.2770.0100.289
plotSCEViolinAssayData0.3510.0110.366
plotSCEViolinColData0.2820.0110.296
plotScDblFinderResults37.992 0.74339.357
plotScanpyDotPlot0.0370.0040.042
plotScanpyEmbedding0.0330.0050.039
plotScanpyHVG0.0340.0050.039
plotScanpyHeatmap0.0350.0080.045
plotScanpyMarkerGenes0.0380.0020.041
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plotScanpyPCAGeneRanking0.0360.0050.042
plotScanpyPCAVariance0.0410.0040.046
plotScanpyViolin0.0390.0020.048
plotScdsHybridResults11.044 0.15511.560
plotScrubletResults0.0160.0020.019
plotSeuratElbow0.0350.0040.039
plotSeuratHVG0.0270.0070.035
plotSeuratJackStraw0.0200.0050.025
plotSeuratReduction0.0390.0050.046
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plotTSCANClusterPseudo2.5880.0362.658
plotTSCANDimReduceFeatures2.6020.0352.674
plotTSCANPseudotimeGenes2.4460.0342.513
plotTSCANPseudotimeHeatmap2.7780.0392.849
plotTSCANResults2.5580.0402.648
plotTSNE0.6380.0160.667
plotTopHVG0.6150.0170.642
plotUMAP8.7820.0748.980
readSingleCellMatrix0.0060.0010.006
reportCellQC0.2070.0080.217
reportDropletQC0.0370.0030.041
reportQCTool0.2150.0080.225
retrieveSCEIndex0.0450.0050.051
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runComBatSeq0.5380.0140.554
runCxds0.5680.0090.586
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runDecontX9.0860.0549.253
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runDoubletFinder40.379 0.22741.319
runDropletQC0.0390.0070.049
runEmptyDrops6.5420.0306.648
runEnrichR0.3390.0284.249
runFastMNN1.9450.0382.092
runFeatureSelection0.2740.0070.287
runFindMarker4.0120.0664.181
runGSVA1.0440.0391.092
runHarmony0.0450.0010.046
runKMeans0.5380.0130.555
runLimmaBC0.0890.0020.095
runMNNCorrect0.7120.0100.754
runModelGeneVar0.5550.0110.574
runNormalization3.0170.0343.120
runPerCellQC0.5790.0170.612
runSCANORAMA0.0000.0010.000
runSCMerge0.0050.0010.007
runScDblFinder25.037 0.47825.962
runScanpyFindClusters0.0370.0020.039
runScanpyFindHVG0.0380.0050.042
runScanpyFindMarkers0.0390.0020.041
runScanpyNormalizeData0.2410.0060.256
runScanpyPCA0.0340.0020.038
runScanpyScaleData0.0440.0030.046
runScanpyTSNE0.0370.0040.041
runScanpyUMAP0.0380.0050.044
runScranSNN0.8830.0190.923
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runSeuratFindHVG0.9170.0600.995
runSeuratHeatmap0.0370.0060.044
runSeuratICA0.0330.0050.039
runSeuratJackStraw0.0370.0060.042
runSeuratNormalizeData0.0340.0060.040
runSeuratPCA0.0380.0050.045
runSeuratSCTransform7.0210.0927.282
runSeuratScaleData0.0380.0050.044
runSeuratUMAP0.0350.0030.038
runSingleR0.0420.0030.046
runSoupX0.0000.0000.001
runTSCAN1.7950.0261.839
runTSCANClusterDEAnalysis1.8280.0261.876
runTSCANDEG1.8290.0291.883
runTSNE1.1150.0321.159
runUMAP8.6880.0658.884
runVAM0.5270.0110.543
runZINBWaVE0.0050.0010.006
sampleSummaryStats0.3350.0110.349
scaterCPM0.1840.0050.192
scaterPCA0.7680.0140.797
scaterlogNormCounts0.3140.0090.335
sce0.0370.0100.047
sctkListGeneSetCollections0.0940.0110.108
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv0.0000.0010.000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.1950.0070.208
setSCTKDisplayRow0.4590.0140.483
singleCellTK0.0000.0010.000
subDiffEx0.6170.0360.708
subsetSCECols0.2050.0100.216
subsetSCERows0.4930.0140.511
summarizeSCE0.0930.0080.102
trimCounts0.2680.0150.284