Back to Multiple platform build/check report for BioC 3.19:   simplified   long
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This page was generated on 2024-10-18 20:38 -0400 (Fri, 18 Oct 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.1 (2024-06-14) -- "Race for Your Life" 4763
palomino7Windows Server 2022 Datacenterx644.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 4500
merida1macOS 12.7.5 Montereyx86_644.4.1 (2024-06-14) -- "Race for Your Life" 4530
kjohnson1macOS 13.6.6 Venturaarm644.4.1 (2024-06-14) -- "Race for Your Life" 4480
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: 2024-10-16 14:00 -0400 (Wed, 16 Oct 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 12.7.5 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 nebbiolo1

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: /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings singleCellTK_2.14.0.tar.gz
StartedAt: 2024-10-17 05:13:20 -0400 (Thu, 17 Oct 2024)
EndedAt: 2024-10-17 05:28:11 -0400 (Thu, 17 Oct 2024)
EllapsedTime: 890.7 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings singleCellTK_2.14.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.19-bioc/meat/singleCellTK.Rcheck’
* using R version 4.4.1 (2024-06-14)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
    GNU Fortran (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
* running under: Ubuntu 22.04.5 LTS
* using session charset: UTF-8
* 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  5.6Mb
  sub-directories of 1Mb or more:
    shiny   2.3Mb
* 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 loading without being on the library search path ... 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 30.993  0.475  31.466
runDoubletFinder         29.435  0.232  29.667
runSeuratSCTransform     28.894  0.648  29.543
plotScDblFinderResults   28.175  0.696  28.869
runScDblFinder           20.273  0.568  20.842
importExampleData        16.130  2.497  19.160
plotBatchCorrCompare     10.046  0.532  10.571
plotScdsHybridResults     9.027  0.144   8.333
plotBcdsResults           7.972  0.277   7.341
runUMAP                   6.991  0.228   7.216
plotDecontXResults        6.932  0.228   7.160
plotEmptyDropsScatter     6.671  0.044   6.715
plotEmptyDropsResults     6.658  0.024   6.682
plotUMAP                  6.494  0.051   6.543
runEmptyDrops             6.419  0.008   6.428
runDecontX                6.370  0.012   6.383
plotCxdsResults           6.015  0.152   6.166
detectCellOutlier         5.829  0.168   5.998
getEnrichRResult          0.584  0.067   6.343
* 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 re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 3 NOTEs
See
  ‘/home/biocbuild/bbs-3.19-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.19-bioc/R/site-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-pc-linux-gnu

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 
  0.170   0.020   0.178 

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-pc-linux-gnu

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
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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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'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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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 
260.670   9.211 272.585 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0020.0000.002
SEG0.0030.0000.003
calcEffectSizes0.1680.0280.196
combineSCE1.2420.0511.294
computeZScore0.2270.0150.243
convertSCEToSeurat3.9240.1564.080
convertSeuratToSCE0.460.000.46
dedupRowNames0.0630.0000.063
detectCellOutlier5.8290.1685.998
diffAbundanceFET0.0490.0040.054
discreteColorPalette0.0060.0000.006
distinctColors0.0020.0000.002
downSampleCells0.5700.1240.694
downSampleDepth0.4940.0070.501
expData-ANY-character-method0.2590.0080.267
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3020.0000.303
expData-set0.3080.0040.312
expData0.2810.0040.284
expDataNames-ANY-method0.2930.0250.316
expDataNames0.2600.0000.261
expDeleteDataTag0.0340.0000.035
expSetDataTag0.0210.0030.026
expTaggedData0.0220.0050.026
exportSCE0.0220.0000.022
exportSCEtoAnnData0.0850.0110.097
exportSCEtoFlatFile0.0890.0080.096
featureIndex0.0310.0030.035
generateSimulatedData0.0500.0000.051
getBiomarker0.0570.0000.057
getDEGTopTable0.7460.0350.782
getDiffAbundanceResults0.0440.0040.048
getEnrichRResult0.5840.0676.343
getFindMarkerTopTable3.1380.2963.434
getMSigDBTable0.0040.0000.004
getPathwayResultNames0.0240.0000.024
getSampleSummaryStatsTable0.2600.0360.296
getSoupX000
getTSCANResults1.6340.1441.778
getTopHVG1.0600.0361.097
importAnnData0.0020.0000.001
importBUStools0.2330.0120.246
importCellRanger0.9970.0711.069
importCellRangerV2Sample0.2180.0240.242
importCellRangerV3Sample0.3500.0240.375
importDropEst0.2790.0000.280
importExampleData16.130 2.49719.160
importGeneSetsFromCollection0.6800.0890.769
importGeneSetsFromGMT0.1060.0200.126
importGeneSetsFromList0.1080.0040.113
importGeneSetsFromMSigDB2.2140.1682.382
importMitoGeneSet0.050.000.05
importOptimus0.0010.0000.002
importSEQC0.2340.0080.243
importSTARsolo0.2550.0040.259
iterateSimulations0.3410.0150.357
listSampleSummaryStatsTables0.4150.0160.432
mergeSCEColData0.4630.0120.475
mouseBrainSubsetSCE0.0420.0000.042
msigdb_table0.0020.0000.002
plotBarcodeRankDropsResults0.7770.0480.824
plotBarcodeRankScatter0.7720.0000.772
plotBatchCorrCompare10.046 0.53210.571
plotBatchVariance0.3070.0240.331
plotBcdsResults7.9720.2777.341
plotBubble0.9350.0200.955
plotClusterAbundance0.8090.0240.834
plotCxdsResults6.0150.1526.166
plotDEGHeatmap2.6060.0562.662
plotDEGRegression3.3170.0243.335
plotDEGViolin3.9520.1564.102
plotDEGVolcano0.8920.0400.932
plotDecontXResults6.9320.2287.160
plotDimRed0.2490.0000.250
plotDoubletFinderResults30.993 0.47531.466
plotEmptyDropsResults6.6580.0246.682
plotEmptyDropsScatter6.6710.0446.715
plotFindMarkerHeatmap4.0510.0764.127
plotMASTThresholdGenes1.4110.0121.423
plotPCA0.4350.0040.439
plotPathway0.7400.0040.744
plotRunPerCellQCResults1.9150.0521.967
plotSCEBarAssayData0.1780.0000.179
plotSCEBarColData0.1340.0000.135
plotSCEBatchFeatureMean0.1970.0000.196
plotSCEDensity0.2250.0040.230
plotSCEDensityAssayData0.1520.0000.152
plotSCEDensityColData0.1910.0000.192
plotSCEDimReduceColData0.6350.0000.635
plotSCEDimReduceFeatures0.3750.0000.375
plotSCEHeatmap0.5770.0000.577
plotSCEScatter0.3190.0040.324
plotSCEViolin0.2350.0120.246
plotSCEViolinAssayData0.2740.0120.286
plotSCEViolinColData0.2200.0040.224
plotScDblFinderResults28.175 0.69628.869
plotScanpyDotPlot0.0250.0000.024
plotScanpyEmbedding0.0230.0000.023
plotScanpyHVG0.0230.0000.023
plotScanpyHeatmap0.0230.0000.023
plotScanpyMarkerGenes0.0190.0040.023
plotScanpyMarkerGenesDotPlot0.0190.0040.023
plotScanpyMarkerGenesHeatmap0.0190.0040.023
plotScanpyMarkerGenesMatrixPlot0.0230.0000.023
plotScanpyMarkerGenesViolin0.0230.0000.024
plotScanpyMatrixPlot0.0240.0000.023
plotScanpyPCA0.0200.0040.024
plotScanpyPCAGeneRanking0.0240.0000.024
plotScanpyPCAVariance0.0240.0000.023
plotScanpyViolin0.0230.0000.024
plotScdsHybridResults9.0270.1448.333
plotScrubletResults0.0190.0040.023
plotSeuratElbow0.0230.0000.023
plotSeuratHVG0.0230.0000.023
plotSeuratJackStraw0.0230.0000.023
plotSeuratReduction0.0230.0000.023
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plotTSCANClusterDEG4.7190.0484.767
plotTSCANClusterPseudo2.0180.0082.026
plotTSCANDimReduceFeatures2.0350.0122.047
plotTSCANPseudotimeGenes1.9210.0361.956
plotTSCANPseudotimeHeatmap2.0830.0042.086
plotTSCANResults1.8840.0121.897
plotTSNE0.4650.0080.473
plotTopHVG0.4940.0000.495
plotUMAP6.4940.0516.543
readSingleCellMatrix0.0060.0000.006
reportCellQC0.1580.0040.162
reportDropletQC0.0240.0000.023
reportQCTool0.1580.0000.159
retrieveSCEIndex0.0290.0000.029
runBBKNN000
runBarcodeRankDrops0.3850.0000.385
runBcds2.2920.0561.439
runCellQC0.1690.0040.173
runClusterSummaryMetrics0.6790.0080.687
runComBatSeq0.4130.0120.425
runCxds0.4260.0040.430
runCxdsBcdsHybrid2.2330.0121.396
runDEAnalysis0.6660.0240.690
runDecontX6.3700.0126.383
runDimReduce0.3970.0000.397
runDoubletFinder29.435 0.23229.667
runDropletQC0.0240.0000.024
runEmptyDrops6.4190.0086.428
runEnrichR0.5990.0602.974
runFastMNN1.7030.2281.931
runFeatureSelection0.1980.0200.218
runFindMarker3.2940.3083.602
runGSVA0.7880.1080.896
runHarmony0.0370.0000.037
runKMeans0.4050.0360.441
runLimmaBC0.0740.0040.078
runMNNCorrect0.5880.0560.644
runModelGeneVar0.4090.0150.424
runNormalization2.2180.3002.518
runPerCellQC0.4260.0080.434
runSCANORAMA0.0010.0000.001
runSCMerge0.0010.0040.004
runScDblFinder20.273 0.56820.842
runScanpyFindClusters0.0170.0080.024
runScanpyFindHVG0.0220.0000.022
runScanpyFindMarkers0.0190.0030.023
runScanpyNormalizeData0.1750.0160.190
runScanpyPCA0.0240.0000.024
runScanpyScaleData0.0190.0040.024
runScanpyTSNE0.0230.0000.024
runScanpyUMAP0.0210.0030.024
runScranSNN0.6750.0680.743
runScrublet0.0240.0000.024
runSeuratFindClusters0.0230.0000.024
runSeuratFindHVG0.7030.0640.767
runSeuratHeatmap0.0230.0000.024
runSeuratICA0.0230.0000.023
runSeuratJackStraw0.0190.0040.023
runSeuratNormalizeData0.0190.0040.023
runSeuratPCA0.0220.0000.023
runSeuratSCTransform28.894 0.64829.543
runSeuratScaleData0.0240.0000.024
runSeuratUMAP0.0190.0040.023
runSingleR0.0330.0000.033
runSoupX000
runTSCAN1.3390.0201.359
runTSCANClusterDEAnalysis1.3740.0041.377
runTSCANDEG1.3280.0241.352
runTSNE0.8520.0040.856
runUMAP6.9910.2287.216
runVAM0.4740.0000.474
runZINBWaVE0.0000.0040.004
sampleSummaryStats0.2530.0000.253
scaterCPM0.1320.0040.136
scaterPCA0.5790.0240.604
scaterlogNormCounts0.2350.0120.247
sce0.0230.0000.024
sctkListGeneSetCollections0.0680.0080.076
sctkPythonInstallConda0.0000.0000.001
sctkPythonInstallVirtualEnv000
selectSCTKConda0.0000.0010.000
selectSCTKVirtualEnvironment000
setRowNames0.1100.0040.115
setSCTKDisplayRow0.3620.0040.366
singleCellTK000
subDiffEx0.4370.0120.449
subsetSCECols0.1510.0040.155
subsetSCERows0.3960.0000.396
summarizeSCE0.0690.0000.068
trimCounts0.1950.0120.207