Back to Multiple platform build/check report for BioC 3.19:   simplified   long
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This page was generated on 2024-06-11 14:40 -0400 (Tue, 11 Jun 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 (2024-04-24) -- "Puppy Cup" 4757
palomino3Windows Server 2022 Datacenterx644.4.0 (2024-04-24 ucrt) -- "Puppy Cup" 4491
lconwaymacOS 12.7.1 Montereyx86_644.4.0 (2024-04-24) -- "Puppy Cup" 4522
kjohnson3macOS 13.6.5 Venturaarm644.4.0 (2024-04-24) -- "Puppy Cup" 4468
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: 2024-06-09 14:00 -0400 (Sun, 09 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
lconwaymacOS 12.7.1 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson3macOS 13.6.5 Ventura / arm64  OK    OK    OK    OK  UNNEEDED, same version is already published


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: 2024-06-10 03:31:00 -0400 (Mon, 10 Jun 2024)
EndedAt: 2024-06-10 03:46:08 -0400 (Mon, 10 Jun 2024)
EllapsedTime: 907.8 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 4.4.0 (2024-04-24)
* 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 22.04.4 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 33.556  0.405  33.959
runDoubletFinder         32.469  0.228  32.698
plotScDblFinderResults   29.704  0.660  30.362
runSeuratSCTransform     29.327  0.260  29.589
runScDblFinder           19.921  0.172  20.094
importExampleData        15.101  1.592  17.224
plotBatchCorrCompare     11.078  0.484  11.555
plotScdsHybridResults    10.374  0.192   9.605
plotBcdsResults           8.469  0.315   7.862
plotDecontXResults        7.474  0.232   7.707
runDecontX                6.868  0.068   6.936
plotUMAP                  6.813  0.084   6.895
plotCxdsResults           6.610  0.116   6.723
plotEmptyDropsScatter     6.597  0.032   6.629
runUMAP                   6.482  0.108   6.587
plotEmptyDropsResults     6.537  0.012   6.550
runEmptyDrops             6.329  0.000   6.329
detectCellOutlier         5.482  0.240   5.722
plotTSCANClusterDEG       5.075  0.068   5.142
* 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 4.4.0 (2024-04-24) -- "Puppy Cup"
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 
  0.169   0.031   0.189 

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: 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%
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 
261.587   9.744 275.783 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0000.0030.002
SEG0.0000.0020.003
calcEffectSizes0.1510.0040.155
combineSCE1.3950.0361.430
computeZScore0.8840.0880.972
convertSCEToSeurat4.1870.1244.311
convertSeuratToSCE0.4620.0270.490
dedupRowNames0.0520.0040.055
detectCellOutlier5.4820.2405.722
diffAbundanceFET0.0490.0080.058
discreteColorPalette0.0060.0000.007
distinctColors0.0020.0000.002
downSampleCells0.6360.0350.672
downSampleDepth0.4880.0080.496
expData-ANY-character-method0.2660.0070.274
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.2940.0050.298
expData-set0.2890.0030.293
expData0.2810.0290.309
expDataNames-ANY-method0.2560.0040.259
expDataNames0.2550.0030.258
expDeleteDataTag0.0350.0010.035
expSetDataTag0.0250.0000.025
expTaggedData0.0180.0070.026
exportSCE0.0230.0000.023
exportSCEtoAnnData0.0970.0040.101
exportSCEtoFlatFile0.0900.0070.098
featureIndex0.0370.0000.037
generateSimulatedData0.0520.0000.052
getBiomarker0.0610.0000.061
getDEGTopTable0.7840.0470.832
getDiffAbundanceResults0.0510.0000.051
getEnrichRResult0.5660.0442.740
getFindMarkerTopTable3.1870.0883.275
getMSigDBTable0.0040.0000.004
getPathwayResultNames0.0240.0000.024
getSampleSummaryStatsTable0.3080.0040.313
getSoupX0.0000.0000.001
getTSCANResults1.8170.0081.825
getTopHVG1.0850.0321.117
importAnnData0.0020.0000.002
importBUStools0.2530.0000.253
importCellRanger1.0780.0161.096
importCellRangerV2Sample0.2420.0080.250
importCellRangerV3Sample0.3440.0200.365
importDropEst0.3520.0070.361
importExampleData15.101 1.59217.224
importGeneSetsFromCollection0.7300.0760.807
importGeneSetsFromGMT0.0650.0040.070
importGeneSetsFromList0.1120.0040.115
importGeneSetsFromMSigDB2.3840.1642.549
importMitoGeneSet0.0560.0000.055
importOptimus0.0010.0020.002
importSEQC0.2120.0490.263
importSTARsolo0.2480.0280.277
iterateSimulations0.3720.0080.380
listSampleSummaryStatsTables0.3640.0160.379
mergeSCEColData0.4500.0200.471
mouseBrainSubsetSCE0.0340.0040.037
msigdb_table0.0010.0000.002
plotBarcodeRankDropsResults0.8620.0440.906
plotBarcodeRankScatter0.7870.0160.802
plotBatchCorrCompare11.078 0.48411.555
plotBatchVariance0.3270.0120.339
plotBcdsResults8.4690.3157.862
plotBubble0.8810.0110.892
plotClusterAbundance0.7960.0080.803
plotCxdsResults6.6100.1166.723
plotDEGHeatmap2.7720.1042.875
plotDEGRegression3.3340.0613.389
plotDEGViolin4.0860.0794.159
plotDEGVolcano0.9390.0080.947
plotDecontXResults7.4740.2327.707
plotDimRed0.2560.0040.260
plotDoubletFinderResults33.556 0.40533.959
plotEmptyDropsResults6.5370.0126.550
plotEmptyDropsScatter6.5970.0326.629
plotFindMarkerHeatmap4.2750.0804.355
plotMASTThresholdGenes1.4940.0191.513
plotPCA0.4480.0000.449
plotPathway0.8720.0000.872
plotRunPerCellQCResults2.1170.0202.137
plotSCEBarAssayData0.1800.0080.187
plotSCEBarColData0.1350.0000.134
plotSCEBatchFeatureMean0.2110.0000.212
plotSCEDensity0.2160.0000.216
plotSCEDensityAssayData0.2150.0000.216
plotSCEDensityColData0.2050.0000.204
plotSCEDimReduceColData0.6850.0040.690
plotSCEDimReduceFeatures0.4030.0000.402
plotSCEHeatmap0.6220.0000.623
plotSCEScatter0.3870.0120.399
plotSCEViolin0.2440.0000.244
plotSCEViolinAssayData0.2330.0040.237
plotSCEViolinColData0.2180.0040.222
plotScDblFinderResults29.704 0.66030.362
plotScanpyDotPlot0.0250.0000.025
plotScanpyEmbedding0.0260.0000.026
plotScanpyHVG0.0240.0000.025
plotScanpyHeatmap0.0240.0000.023
plotScanpyMarkerGenes0.0250.0000.025
plotScanpyMarkerGenesDotPlot0.0240.0000.025
plotScanpyMarkerGenesHeatmap0.0260.0000.025
plotScanpyMarkerGenesMatrixPlot0.0250.0000.025
plotScanpyMarkerGenesViolin0.0210.0040.025
plotScanpyMatrixPlot0.0250.0000.025
plotScanpyPCA0.0240.0000.024
plotScanpyPCAGeneRanking0.0260.0000.026
plotScanpyPCAVariance0.0210.0040.025
plotScanpyViolin0.0260.0000.025
plotScdsHybridResults10.374 0.192 9.605
plotScrubletResults0.0210.0040.025
plotSeuratElbow0.0210.0040.026
plotSeuratHVG0.0250.0000.025
plotSeuratJackStraw0.0260.0000.025
plotSeuratReduction0.0250.0000.026
plotSoupXResults0.0010.0000.000
plotTSCANClusterDEG5.0750.0685.142
plotTSCANClusterPseudo2.2470.0202.267
plotTSCANDimReduceFeatures2.1380.0082.146
plotTSCANPseudotimeGenes1.9220.0041.926
plotTSCANPseudotimeHeatmap2.1270.0082.136
plotTSCANResults1.9240.0121.937
plotTSNE0.5180.0030.521
plotTopHVG0.5130.0080.521
plotUMAP6.8130.0846.895
readSingleCellMatrix0.0060.0000.005
reportCellQC0.1660.0000.166
reportDropletQC0.0240.0000.024
reportQCTool0.1670.0000.166
retrieveSCEIndex0.0290.0000.029
runBBKNN000
runBarcodeRankDrops0.3790.0040.383
runBcds2.3290.0201.445
runCellQC0.1670.0000.167
runClusterSummaryMetrics0.6910.0120.703
runComBatSeq0.4360.0000.436
runCxds0.4310.0040.435
runCxdsBcdsHybrid2.2820.0081.420
runDEAnalysis0.6530.0000.653
runDecontX6.8680.0686.936
runDimReduce0.4100.0000.409
runDoubletFinder32.469 0.22832.698
runDropletQC0.0220.0030.025
runEmptyDrops6.3290.0006.329
runEnrichR0.5320.0074.646
runFastMNN1.6430.0641.707
runFeatureSelection0.2070.0150.222
runFindMarker3.0930.0243.117
runGSVA0.8340.0240.858
runHarmony0.0330.0000.033
runKMeans0.3930.0040.397
runLimmaBC0.0690.0000.069
runMNNCorrect0.4760.0040.480
runModelGeneVar0.4180.0000.418
runNormalization2.3050.0682.373
runPerCellQC0.4700.0000.469
runSCANORAMA000
runSCMerge0.0040.0000.004
runScDblFinder19.921 0.17220.094
runScanpyFindClusters0.0220.0040.026
runScanpyFindHVG0.0240.0000.024
runScanpyFindMarkers0.0200.0040.024
runScanpyNormalizeData0.1790.0230.201
runScanpyPCA0.0240.0000.024
runScanpyScaleData0.0230.0000.023
runScanpyTSNE0.0240.0000.023
runScanpyUMAP0.0240.0000.024
runScranSNN0.7200.0040.724
runScrublet0.0230.0000.023
runSeuratFindClusters0.0230.0000.023
runSeuratFindHVG0.7410.0200.761
runSeuratHeatmap0.0240.0000.024
runSeuratICA0.0190.0040.023
runSeuratJackStraw0.0230.0000.023
runSeuratNormalizeData0.0230.0000.023
runSeuratPCA0.0230.0000.022
runSeuratSCTransform29.327 0.26029.589
runSeuratScaleData0.0260.0000.025
runSeuratUMAP0.0240.0000.024
runSingleR0.0370.0000.037
runSoupX0.0010.0000.000
runTSCAN1.4290.0161.444
runTSCANClusterDEAnalysis1.4830.0041.488
runTSCANDEG1.3990.0111.412
runTSNE0.8460.0000.847
runUMAP6.4820.1086.587
runVAM0.5010.0000.501
runZINBWaVE0.0040.0000.004
sampleSummaryStats0.2600.0040.264
scaterCPM0.1290.0040.132
scaterPCA0.5810.0040.585
scaterlogNormCounts0.2400.0000.241
sce0.0190.0040.023
sctkListGeneSetCollections0.0720.0040.076
sctkPythonInstallConda0.0000.0000.001
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0740.0060.080
setSCTKDisplayRow0.4130.0000.412
singleCellTK000
subDiffEx0.4670.0110.478
subsetSCECols0.1560.0040.160
subsetSCERows0.3520.0360.388
summarizeSCE0.0650.0000.065
trimCounts0.1940.0120.207