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This page was generated on 2024-05-04 11:41:08 -0400 (Sat, 04 May 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 beta (2024-04-15 r86425) -- "Puppy Cup" 4753
palomino3Windows Server 2022 Datacenterx644.4.0 beta (2024-04-15 r86425 ucrt) -- "Puppy Cup" 4486
lconwaymacOS 12.7.1 Montereyx86_644.4.0 beta (2024-04-14 r86421) -- "Puppy Cup" 4519
kunpeng2Linux (openEuler 22.03 LTS-SP1)aarch644.4.0 beta (2024-04-15 r86425) -- "Puppy Cup" 4479
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: 2024-05-03 14:00:19 -0400 (Fri, 03 May 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
lconwaymacOS 12.7.1 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kunpeng2Linux (openEuler 22.03 LTS-SP1) / aarch64  OK    OK    OK  
kjohnson3macOS 13.6.5 Ventura / arm64see weekly results here

CHECK results for singleCellTK on kunpeng2


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.
- See Martin Grigorov's blog post for how to debug Linux ARM64 related issues on a x86_64 host.

raw results


Summary

Package: singleCellTK
Version: 2.14.0
Command: /home/biocbuild/R/R-beta-2024-04-15_r86425/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/R/R-beta-2024-04-15_r86425/site-library --no-vignettes --timings singleCellTK_2.14.0.tar.gz
StartedAt: 2024-05-04 12:04:58 -0000 (Sat, 04 May 2024)
EndedAt: 2024-05-04 12:25:48 -0000 (Sat, 04 May 2024)
EllapsedTime: 1250.1 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R-beta-2024-04-15_r86425/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/R/R-beta-2024-04-15_r86425/site-library --no-vignettes --timings singleCellTK_2.14.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.19-bioc/meat/singleCellTK.Rcheck’
* using R version 4.4.0 beta (2024-04-15 r86425)
* using platform: aarch64-unknown-linux-gnu
* R was compiled by
    gcc (GCC) 10.3.1
    GNU Fortran (GCC) 10.3.1
* running under: openEuler 22.03 (LTS-SP1)
* 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  6.9Mb
  sub-directories of 1Mb or more:
    extdata   1.6Mb
    shiny     3.0Mb
* 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
runSeuratSCTransform     48.801  0.570  49.473
plotScDblFinderResults   43.289  0.463  43.834
plotDoubletFinderResults 42.824  0.448  43.341
runDoubletFinder         37.798  0.167  38.038
runScDblFinder           30.237  0.331  30.627
importExampleData        23.017  2.025  30.958
plotBatchCorrCompare     14.060  0.451  14.521
plotScdsHybridResults    12.415  0.096  11.380
plotBcdsResults          11.104  0.303  10.260
plotDecontXResults       10.006  0.127  10.154
runUMAP                   8.447  0.056   8.512
runDecontX                8.304  0.020   8.340
plotCxdsResults           8.152  0.156   8.316
plotUMAP                  8.023  0.148   8.177
plotTSCANClusterDEG       7.663  0.028   7.708
detectCellOutlier         7.586  0.068   7.673
plotFindMarkerHeatmap     6.894  0.072   6.981
plotDEGViolin             6.580  0.100   6.694
plotEmptyDropsScatter     5.915  0.031   5.956
plotEmptyDropsResults     5.934  0.008   5.949
runFindMarker             5.166  0.427   5.606
runEmptyDrops             5.566  0.016   5.588
convertSCEToSeurat        5.462  0.036   5.512
plotDEGRegression         5.460  0.032   5.503
getFindMarkerTopTable     4.769  0.263   5.046
runEnrichR                0.435  0.056   9.954
getEnrichRResult          0.380  0.055   9.978
* 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
  ‘/home/biocbuild/bbs-3.19-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R-beta-2024-04-15_r86425/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/R/R-beta-2024-04-15_r86425/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 4.4.0 beta (2024-04-15 r86425) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-unknown-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 
  0.202   0.031   0.217 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.0 beta (2024-04-15 r86425) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-unknown-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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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'
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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 
352.673   3.950 369.362 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0000.003
SEG0.0020.0000.003
calcEffectSizes0.2800.0120.294
combineSCE1.8730.0751.953
computeZScore0.2990.0040.304
convertSCEToSeurat5.4620.0365.512
convertSeuratToSCE0.6510.0000.653
dedupRowNames0.070.000.07
detectCellOutlier7.5860.0687.673
diffAbundanceFET0.0600.0040.064
discreteColorPalette0.0070.0000.007
distinctColors0.0020.0000.002
downSampleCells0.8840.0680.954
downSampleDepth0.8020.0040.807
expData-ANY-character-method0.3930.0000.394
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.4350.0000.437
expData-set0.4230.0040.429
expData0.3790.0040.384
expDataNames-ANY-method0.4030.0280.432
expDataNames0.3770.0040.383
expDeleteDataTag0.0380.0000.039
expSetDataTag0.0260.0000.027
expTaggedData0.0280.0000.028
exportSCE0.0250.0000.024
exportSCEtoAnnData0.0840.0040.088
exportSCEtoFlatFile0.0820.0040.086
featureIndex0.0360.0040.040
generateSimulatedData0.0590.0000.059
getBiomarker0.0630.0000.064
getDEGTopTable1.0850.0241.111
getDiffAbundanceResults0.0500.0040.054
getEnrichRResult0.3800.0559.978
getFindMarkerTopTable4.7690.2635.046
getMSigDBTable0.0050.0000.004
getPathwayResultNames0.0310.0000.031
getSampleSummaryStatsTable0.4220.0240.447
getSoupX000
getTSCANResults2.4860.1482.640
getTopHVG1.5250.0881.618
importAnnData0.0000.0010.002
importBUStools0.3480.0220.373
importCellRanger1.5680.0521.628
importCellRangerV2Sample0.3540.0070.363
importCellRangerV3Sample0.5650.0030.570
importDropEst0.4020.0080.413
importExampleData23.017 2.02530.958
importGeneSetsFromCollection1.0300.0631.097
importGeneSetsFromGMT0.0810.0160.098
importGeneSetsFromList0.1860.0080.195
importGeneSetsFromMSigDB3.1540.1083.270
importMitoGeneSet0.0810.0040.085
importOptimus0.0030.0000.002
importSEQC0.4270.0120.441
importSTARsolo0.3830.0240.410
iterateSimulations0.5330.0160.550
listSampleSummaryStatsTables0.6370.0280.667
mergeSCEColData0.7120.0280.741
mouseBrainSubsetSCE0.0480.0000.049
msigdb_table0.0020.0000.002
plotBarcodeRankDropsResults1.2600.0201.284
plotBarcodeRankScatter1.2780.0041.285
plotBatchCorrCompare14.060 0.45114.521
plotBatchVariance0.4550.0080.463
plotBcdsResults11.104 0.30310.260
plotBubble1.4120.0201.436
plotClusterAbundance1.2270.0041.233
plotCxdsResults8.1520.1568.316
plotDEGHeatmap4.1880.0724.276
plotDEGRegression5.4600.0325.503
plotDEGViolin6.5800.1006.694
plotDEGVolcano1.3810.0201.405
plotDecontXResults10.006 0.12710.154
plotDimRed0.3920.0000.394
plotDoubletFinderResults42.824 0.44843.341
plotEmptyDropsResults5.9340.0085.949
plotEmptyDropsScatter5.9150.0315.956
plotFindMarkerHeatmap6.8940.0726.981
plotMASTThresholdGenes2.3370.0362.378
plotPCA0.7080.0040.714
plotPathway1.2610.0241.289
plotRunPerCellQCResults3.1850.0123.204
plotSCEBarAssayData0.2540.0040.259
plotSCEBarColData0.2050.0040.209
plotSCEBatchFeatureMean0.3220.0110.334
plotSCEDensity0.3720.0000.373
plotSCEDensityAssayData0.2430.0000.243
plotSCEDensityColData0.3050.0000.307
plotSCEDimReduceColData1.0570.0001.059
plotSCEDimReduceFeatures0.5860.0040.590
plotSCEHeatmap0.9290.0000.932
plotSCEScatter0.5160.0000.518
plotSCEViolin0.3520.0000.353
plotSCEViolinAssayData0.4240.0040.428
plotSCEViolinColData0.3470.0000.347
plotScDblFinderResults43.289 0.46343.834
plotScanpyDotPlot0.0310.0000.030
plotScanpyEmbedding0.0310.0000.031
plotScanpyHVG0.030.000.03
plotScanpyHeatmap0.0310.0000.031
plotScanpyMarkerGenes0.030.000.03
plotScanpyMarkerGenesDotPlot0.0310.0000.031
plotScanpyMarkerGenesHeatmap0.0260.0040.031
plotScanpyMarkerGenesMatrixPlot0.0270.0040.030
plotScanpyMarkerGenesViolin0.0310.0000.031
plotScanpyMatrixPlot0.0310.0000.032
plotScanpyPCA0.0320.0000.032
plotScanpyPCAGeneRanking0.030.000.03
plotScanpyPCAVariance0.0290.0000.029
plotScanpyViolin0.0310.0000.031
plotScdsHybridResults12.415 0.09611.380
plotScrubletResults0.0350.0000.035
plotSeuratElbow0.0310.0040.035
plotSeuratHVG0.0320.0000.032
plotSeuratJackStraw0.0310.0000.031
plotSeuratReduction0.0270.0040.031
plotSoupXResults000
plotTSCANClusterDEG7.6630.0287.708
plotTSCANClusterPseudo3.3920.0163.416
plotTSCANDimReduceFeatures3.3730.0003.379
plotTSCANPseudotimeGenes3.2330.0283.268
plotTSCANPseudotimeHeatmap3.4610.0283.497
plotTSCANResults3.1600.0203.187
plotTSNE0.7810.0040.786
plotTopHVG0.7600.0120.774
plotUMAP8.0230.1488.177
readSingleCellMatrix0.0060.0000.006
reportCellQC0.2550.0000.256
reportDropletQC0.0330.0000.033
reportQCTool0.2570.0000.257
retrieveSCEIndex0.0410.0000.042
runBBKNN0.0000.0000.001
runBarcodeRankDrops0.5950.0000.597
runBcds3.4460.0242.331
runCellQC0.2280.0000.229
runClusterSummaryMetrics0.9820.0030.988
runComBatSeq0.6910.0040.697
runCxds0.6960.0010.697
runCxdsBcdsHybrid3.3550.0242.305
runDEAnalysis0.9510.0040.956
runDecontX8.3040.0208.340
runDimReduce0.6190.0000.620
runDoubletFinder37.798 0.16738.038
runDropletQC0.0330.0000.032
runEmptyDrops5.5660.0165.588
runEnrichR0.4350.0569.954
runFastMNN2.5820.2922.880
runFeatureSelection0.3200.0440.365
runFindMarker5.1660.4275.606
runGSVA1.3150.0791.398
runHarmony0.0560.0040.061
runKMeans0.6550.0320.689
runLimmaBC0.1210.0040.125
runMNNCorrect0.8400.0880.930
runModelGeneVar0.6730.0360.711
runNormalization2.9420.2363.184
runPerCellQC0.7280.0160.745
runSCANORAMA0.0010.0000.000
runSCMerge0.0050.0000.005
runScDblFinder30.237 0.33130.627
runScanpyFindClusters0.0310.0000.031
runScanpyFindHVG0.030.000.03
runScanpyFindMarkers0.030.000.03
runScanpyNormalizeData0.2950.0200.316
runScanpyPCA0.030.000.03
runScanpyScaleData0.0290.0000.029
runScanpyTSNE0.030.000.03
runScanpyUMAP0.030.000.03
runScranSNN1.0770.0601.139
runScrublet0.0290.0000.030
runSeuratFindClusters0.0290.0000.030
runSeuratFindHVG1.2130.0441.259
runSeuratHeatmap0.0350.0000.035
runSeuratICA0.0330.0000.034
runSeuratJackStraw0.0340.0000.034
runSeuratNormalizeData0.0280.0040.033
runSeuratPCA0.0340.0000.034
runSeuratSCTransform48.801 0.57049.473
runSeuratScaleData0.0330.0030.037
runSeuratUMAP0.0350.0000.035
runSingleR0.0550.0000.055
runSoupX000
runTSCAN2.2040.0122.221
runTSCANClusterDEAnalysis2.3420.0362.383
runTSCANDEG2.2700.0082.283
runTSNE1.4950.0001.498
runUMAP8.4470.0568.512
runVAM0.8250.0000.827
runZINBWaVE0.0050.0000.005
sampleSummaryStats0.4230.0000.423
scaterCPM0.1470.0000.147
scaterPCA0.9010.0040.907
scaterlogNormCounts0.3220.0000.323
sce0.0250.0040.029
sctkListGeneSetCollections0.1090.0000.109
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.1800.0000.181
setSCTKDisplayRow0.6060.0100.619
singleCellTK0.0000.0000.001
subDiffEx0.7020.0080.712
subsetSCECols0.2470.0070.254
subsetSCERows0.5810.0160.599
summarizeSCE0.0950.0000.095
trimCounts0.2710.0000.271