Back to Multiple platform build/check report for BioC 3.19:   simplified   long
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This page was generated on 2024-06-25 17:44 -0400 (Tue, 25 Jun 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 (2024-04-24) -- "Puppy Cup" 4760
palomino3Windows Server 2022 Datacenterx644.4.0 (2024-04-24 ucrt) -- "Puppy Cup" 4494
merida1macOS 12.7.4 Montereyx86_644.4.0 (2024-04-24) -- "Puppy Cup" 4508
kjohnson1macOS 13.6.6 Venturaarm644.4.0 (2024-04-24) -- "Puppy Cup" 4466
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: 2024-06-23 14:00 -0400 (Sun, 23 Jun 2024)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: RELEASE_3_19
git_last_commit: cd29b84
git_last_commit_date: 2024-04-30 11:06:02 -0400 (Tue, 30 Apr 2024)
nebbiolo1Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino3Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
merida1macOS 12.7.4 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson1macOS 13.6.6 Ventura / arm64  OK    OK    OK    OK  UNNEEDED, same version is already published


CHECK results for singleCellTK on 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: 2024-06-25 09:37:51 -0400 (Tue, 25 Jun 2024)
EndedAt: 2024-06-25 09:55:27 -0400 (Tue, 25 Jun 2024)
EllapsedTime: 1055.2 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: 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 43.474  0.209  44.011
plotScDblFinderResults   37.695  0.697  38.548
runDoubletFinder         38.092  0.195  38.467
runScDblFinder           24.698  0.421  25.239
importExampleData        22.947  1.563  26.887
plotBatchCorrCompare     14.005  0.109  14.187
plotScdsHybridResults    11.079  0.167  11.299
plotBcdsResults           9.902  0.171  10.135
plotDecontXResults        9.711  0.056   9.816
runDecontX                8.741  0.059   8.821
plotUMAP                  8.321  0.047   8.416
runUMAP                   8.249  0.086   8.360
plotCxdsResults           7.872  0.047   7.965
detectCellOutlier         7.747  0.112   7.887
runSeuratSCTransform      6.721  0.074   6.811
plotEmptyDropsResults     6.686  0.025   6.739
runEmptyDrops             6.347  0.021   6.382
plotEmptyDropsScatter     6.036  0.024   6.127
plotTSCANClusterDEG       5.825  0.081   5.935
convertSCEToSeurat        4.833  0.185   5.037
getEnrichRResult          0.362  0.035   7.961
* 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 4.4.0 (2024-04-24) -- "Puppy Cup"
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 
  0.231   0.065   0.279 

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: 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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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 
305.529   5.792 321.091 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0040.007
SEG0.0040.0030.007
calcEffectSizes0.2090.0190.228
combineSCE1.4710.0461.521
computeZScore0.3170.0090.334
convertSCEToSeurat4.8330.1855.037
convertSeuratToSCE0.5330.0090.542
dedupRowNames0.0740.0030.078
detectCellOutlier7.7470.1127.887
diffAbundanceFET0.0810.0030.085
discreteColorPalette0.0080.0010.009
distinctColors0.0030.0010.003
downSampleCells0.7980.0740.874
downSampleDepth0.6480.0360.685
expData-ANY-character-method0.3530.0080.361
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.4000.0090.408
expData-set0.3610.0070.367
expData0.3390.0220.362
expDataNames-ANY-method0.3570.0270.385
expDataNames0.3180.0070.325
expDeleteDataTag0.0510.0030.054
expSetDataTag0.0370.0020.040
expTaggedData0.0430.0030.046
exportSCE0.0370.0060.044
exportSCEtoAnnData0.1410.0050.151
exportSCEtoFlatFile0.1390.0040.143
featureIndex0.0500.0050.056
generateSimulatedData0.0710.0070.079
getBiomarker0.0790.0070.088
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getDiffAbundanceResults0.0720.0040.076
getEnrichRResult0.3620.0357.961
getFindMarkerTopTable3.5030.0543.633
getMSigDBTable0.0050.0030.009
getPathwayResultNames0.0350.0040.040
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getTopHVG1.3360.0201.366
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importCellRangerV3Sample0.4270.0170.447
importDropEst0.3310.0050.336
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importGeneSetsFromCollection0.8490.0780.930
importGeneSetsFromGMT0.0840.0070.091
importGeneSetsFromList0.1500.0060.156
importGeneSetsFromMSigDB3.1810.1183.313
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importOptimus0.0020.0010.003
importSEQC0.3380.0140.353
importSTARsolo0.2790.0050.285
iterateSimulations0.4060.0120.420
listSampleSummaryStatsTables0.5050.0090.517
mergeSCEColData0.5180.0220.541
mouseBrainSubsetSCE0.0600.0060.067
msigdb_table0.0020.0030.005
plotBarcodeRankDropsResults0.9870.0191.014
plotBarcodeRankScatter0.9290.0130.953
plotBatchCorrCompare14.005 0.10914.187
plotBatchVariance0.3520.0220.376
plotBcdsResults 9.902 0.17110.135
plotBubble1.1350.0311.171
plotClusterAbundance0.8710.0090.888
plotCxdsResults7.8720.0477.965
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plotDEGRegression3.8560.0553.920
plotDEGViolin4.6290.0894.737
plotDEGVolcano1.2100.0151.230
plotDecontXResults9.7110.0569.816
plotDimRed0.3270.0080.337
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plotEmptyDropsScatter6.0360.0246.127
plotFindMarkerHeatmap4.1700.0334.232
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plotPCA0.5350.0130.550
plotPathway0.9200.0140.938
plotRunPerCellQCResults2.2590.0232.302
plotSCEBarAssayData0.2350.0070.243
plotSCEBarColData0.1640.0060.172
plotSCEBatchFeatureMean0.2300.0030.233
plotSCEDensity0.2830.0080.293
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plotSCEDensityColData0.2390.0090.248
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plotSCEScatter0.4040.0110.415
plotSCEViolin0.2730.0090.284
plotSCEViolinAssayData0.3310.0090.341
plotSCEViolinColData0.2710.0100.282
plotScDblFinderResults37.695 0.69738.548
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plotScanpyEmbedding0.0350.0040.040
plotScanpyHVG0.0360.0050.040
plotScanpyHeatmap0.0350.0030.038
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plotScdsHybridResults11.079 0.16711.299
plotScrubletResults0.0390.0030.042
plotSeuratElbow0.0410.0050.046
plotSeuratHVG0.0500.0050.056
plotSeuratJackStraw0.0360.0030.040
plotSeuratReduction0.0380.0100.048
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plotTSCANClusterDEG5.8250.0815.935
plotTSCANClusterPseudo2.4910.0352.547
plotTSCANDimReduceFeatures2.4390.0292.474
plotTSCANPseudotimeGenes2.2780.0272.314
plotTSCANPseudotimeHeatmap2.5350.0362.588
plotTSCANResults2.2920.0312.340
plotTSNE0.5850.0210.607
plotTopHVG0.5850.0130.600
plotUMAP8.3210.0478.416
readSingleCellMatrix0.0060.0010.007
reportCellQC0.1850.0070.193
reportDropletQC0.0380.0050.043
reportQCTool0.2020.0050.207
retrieveSCEIndex0.0430.0040.048
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runEmptyDrops6.3470.0216.382
runEnrichR0.3450.0323.702
runFastMNN1.6080.0331.656
runFeatureSelection0.2530.0050.258
runFindMarker3.1770.0573.256
runGSVA0.7460.0310.785
runHarmony0.0380.0010.040
runKMeans0.5200.0140.537
runLimmaBC0.0860.0030.087
runMNNCorrect0.6570.0120.670
runModelGeneVar0.5130.0080.523
runNormalization2.8050.0402.883
runPerCellQC0.5700.0140.586
runSCANORAMA0.0000.0000.001
runSCMerge0.0050.0010.007
runScDblFinder24.698 0.42125.239
runScanpyFindClusters0.0310.0030.034
runScanpyFindHVG0.0360.0040.039
runScanpyFindMarkers0.0370.0010.038
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runScanpyPCA0.0340.0080.042
runScanpyScaleData0.0350.0070.042
runScanpyTSNE0.0370.0020.039
runScanpyUMAP0.0330.0030.037
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runSeuratFindHVG0.8550.0500.908
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runSeuratICA0.0380.0020.041
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runSeuratPCA0.0340.0020.036
runSeuratSCTransform6.7210.0746.811
runSeuratScaleData0.0360.0050.042
runSeuratUMAP0.0350.0020.037
runSingleR0.0360.0040.040
runSoupX0.0000.0010.000
runTSCAN1.3470.0371.392
runTSCANClusterDEAnalysis1.5610.0211.582
runTSCANDEG1.3540.0261.384
runTSNE1.0800.0411.122
runUMAP8.2490.0868.360
runVAM0.3210.0150.342
runZINBWaVE0.0020.0010.003
sampleSummaryStats0.1480.0080.158
scaterCPM0.0790.0040.084
scaterPCA0.3360.0280.368
scaterlogNormCounts0.2340.0160.251
sce0.0350.0060.041
sctkListGeneSetCollections0.0910.0100.101
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment0.0010.0010.000
setRowNames0.1750.0310.206
setSCTKDisplayRow0.4290.0130.443
singleCellTK0.0000.0000.001
subDiffEx0.4900.0330.525
subsetSCECols0.1030.0070.112
subsetSCERows0.2680.0130.285
summarizeSCE0.0460.0070.052
trimCounts0.1330.0210.157