Back to Multiple platform build/check report for BioC 3.20:   simplified   long
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This page was generated on 2024-07-24 11:39 -0400 (Wed, 24 Jul 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.4.1 (2024-06-14) -- "Race for Your Life" 4688
palomino8Windows Server 2022 Datacenterx644.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 4284
lconwaymacOS 12.7.1 Montereyx86_644.4.1 (2024-06-14) -- "Race for Your Life" 4455
kjohnson3macOS 13.6.5 Venturaarm644.4.1 (2024-06-14) -- "Race for Your Life" 4404
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 1945/2248HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.15.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-07-23 14:00 -0400 (Tue, 23 Jul 2024)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: 4d7a515
git_last_commit_date: 2024-04-30 11:06:02 -0400 (Tue, 30 Apr 2024)
nebbiolo2Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino8Windows Server 2022 Datacenter / x64  ERROR    ERROR  skippedskipped
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 nebbiolo2

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: 2.15.0
Command: /home/biocbuild/bbs-3.20-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.20-bioc/R/site-library --timings singleCellTK_2.15.0.tar.gz
StartedAt: 2024-07-24 04:06:24 -0400 (Wed, 24 Jul 2024)
EndedAt: 2024-07-24 04:21:51 -0400 (Wed, 24 Jul 2024)
EllapsedTime: 926.5 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.20-bioc/R/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/bbs-3.20-bioc/R/site-library --timings singleCellTK_2.15.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.20-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 22.04.4 LTS
* using session charset: UTF-8
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.15.0’
* 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  7.0Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    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
plotDoubletFinderResults 33.372  0.372  33.742
runDoubletFinder         32.771  0.168  32.938
runSeuratSCTransform     29.515  0.608  30.125
plotScDblFinderResults   28.067  0.468  28.533
runScDblFinder           19.868  0.580  20.448
importExampleData        16.394  1.752  18.597
plotBatchCorrCompare     12.284  0.408  12.686
plotScdsHybridResults     9.939  0.197   9.261
plotBcdsResults           8.811  0.267   8.170
plotDecontXResults        7.718  0.196   7.914
runDecontX                7.443  0.112   7.555
plotUMAP                  6.758  0.032   6.787
runUMAP                   6.587  0.188   6.773
plotCxdsResults           6.527  0.244   6.770
plotEmptyDropsResults     6.688  0.020   6.708
plotEmptyDropsScatter     6.657  0.033   6.690
runEmptyDrops             6.425  0.000   6.426
detectCellOutlier         5.399  0.263   5.663
getEnrichRResult          0.492  0.057   6.130
* 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-3.20-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.20-bioc/R/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.20-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 
  0.168   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 
277.444   4.465 283.233 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0020.0010.003
SEG0.0030.0000.003
calcEffectSizes0.1580.0000.159
combineSCE2.1260.0792.207
computeZScore0.2170.0130.229
convertSCEToSeurat3.3330.1233.457
convertSeuratToSCE1.3390.0281.367
dedupRowNames0.0530.0000.053
detectCellOutlier5.3990.2635.663
diffAbundanceFET0.0570.0000.057
discreteColorPalette0.0060.0000.006
distinctColors0.0020.0000.002
downSampleCells0.6240.0390.663
downSampleDepth0.4930.0080.500
expData-ANY-character-method0.2930.0040.297
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3050.0000.305
expData-set0.3010.0000.302
expData0.2760.0120.288
expDataNames-ANY-method0.2720.0080.280
expDataNames0.2560.0040.260
expDeleteDataTag0.0310.0040.036
expSetDataTag0.0250.0000.026
expTaggedData0.0260.0000.026
exportSCE0.0220.0000.022
exportSCEtoAnnData0.0930.0080.101
exportSCEtoFlatFile0.0920.0040.096
featureIndex0.0290.0080.038
generateSimulatedData0.0510.0000.051
getBiomarker0.0610.0000.061
getDEGTopTable0.8100.0160.826
getDiffAbundanceResults0.0490.0000.050
getEnrichRResult0.4920.0576.130
getFindMarkerTopTable3.2130.2723.486
getMSigDBTable0.0040.0000.004
getPathwayResultNames0.0240.0000.024
getSampleSummaryStatsTable0.2930.0200.313
getSoupX0.0000.0000.001
getTSCANResults1.6870.0791.767
getTopHVG1.1910.0681.260
importAnnData0.0010.0000.002
importBUStools0.2440.0080.253
importCellRanger1.0530.0641.117
importCellRangerV2Sample0.2480.0280.276
importCellRangerV3Sample0.4010.0070.408
importDropEst0.2970.0000.298
importExampleData16.394 1.75218.597
importGeneSetsFromCollection0.6860.0280.714
importGeneSetsFromGMT0.0590.0080.067
importGeneSetsFromList0.1270.0000.127
importGeneSetsFromMSigDB2.2710.2282.498
importMitoGeneSet0.0430.0070.051
importOptimus0.0010.0000.002
importSEQC0.2250.0080.233
importSTARsolo0.2320.0000.231
iterateSimulations0.3740.0080.381
listSampleSummaryStatsTables0.3490.0080.356
mergeSCEColData0.3970.0120.409
mouseBrainSubsetSCE0.0380.0000.037
msigdb_table0.0020.0000.001
plotBarcodeRankDropsResults0.7730.0120.785
plotBarcodeRankScatter0.8320.0120.845
plotBatchCorrCompare12.284 0.40812.686
plotBatchVariance0.3350.0240.360
plotBcdsResults8.8110.2678.170
plotBubble0.9550.0521.007
plotClusterAbundance0.8030.0120.815
plotCxdsResults6.5270.2446.770
plotDEGHeatmap2.8320.0442.876
plotDEGRegression3.3230.0083.325
plotDEGViolin4.0660.0924.151
plotDEGVolcano0.9070.0040.911
plotDecontXResults7.7180.1967.914
plotDimRed0.2780.0080.285
plotDoubletFinderResults33.372 0.37233.742
plotEmptyDropsResults6.6880.0206.708
plotEmptyDropsScatter6.6570.0336.690
plotFindMarkerHeatmap4.0850.0834.169
plotMASTThresholdGenes1.4260.0081.435
plotPCA0.4430.0000.443
plotPathway0.7420.0010.743
plotRunPerCellQCResults2.0120.0552.069
plotSCEBarAssayData0.1850.0000.186
plotSCEBarColData0.1690.0000.169
plotSCEBatchFeatureMean0.1960.0000.197
plotSCEDensity0.2020.0000.203
plotSCEDensityAssayData0.1640.0000.165
plotSCEDensityColData0.1990.0000.199
plotSCEDimReduceColData0.6640.0000.663
plotSCEDimReduceFeatures0.3630.0000.363
plotSCEHeatmap0.6040.0000.605
plotSCEScatter0.3270.0000.327
plotSCEViolin0.2250.0000.226
plotSCEViolinAssayData0.2760.0030.279
plotSCEViolinColData0.2240.0040.228
plotScDblFinderResults28.067 0.46828.533
plotScanpyDotPlot0.0240.0000.025
plotScanpyEmbedding0.0230.0000.024
plotScanpyHVG0.0200.0040.024
plotScanpyHeatmap0.0240.0000.023
plotScanpyMarkerGenes0.0230.0000.024
plotScanpyMarkerGenesDotPlot0.0240.0000.024
plotScanpyMarkerGenesHeatmap0.0240.0000.024
plotScanpyMarkerGenesMatrixPlot0.0240.0000.023
plotScanpyMarkerGenesViolin0.0230.0000.023
plotScanpyMatrixPlot0.0230.0000.023
plotScanpyPCA0.0190.0040.023
plotScanpyPCAGeneRanking0.0240.0000.024
plotScanpyPCAVariance0.0230.0000.023
plotScanpyViolin0.0230.0000.023
plotScdsHybridResults9.9390.1979.261
plotScrubletResults0.0240.0000.025
plotSeuratElbow0.0240.0000.024
plotSeuratHVG0.0210.0040.024
plotSeuratJackStraw0.0240.0000.024
plotSeuratReduction0.0240.0000.023
plotSoupXResults000
plotTSCANClusterDEG4.9430.0404.982
plotTSCANClusterPseudo2.1760.0002.175
plotTSCANDimReduceFeatures2.090.002.09
plotTSCANPseudotimeGenes2.0710.0042.076
plotTSCANPseudotimeHeatmap2.1930.0042.197
plotTSCANResults2.0520.0042.055
plotTSNE0.4410.0040.445
plotTopHVG0.5280.0000.528
plotUMAP6.7580.0326.787
readSingleCellMatrix0.0050.0000.005
reportCellQC0.1610.0000.161
reportDropletQC0.0250.0000.024
reportQCTool0.160.000.16
retrieveSCEIndex0.030.000.03
runBBKNN000
runBarcodeRankDrops0.3950.0080.403
runBcds2.3600.0401.484
runCellQC0.1620.0000.162
runClusterSummaryMetrics0.6290.0000.629
runComBatSeq0.4220.0080.430
runCxds0.4650.0000.465
runCxdsBcdsHybrid2.3500.0121.459
runDEAnalysis0.6720.0000.672
runDecontX7.4430.1127.555
runDimReduce0.4160.0080.423
runDoubletFinder32.771 0.16832.938
runDropletQC0.0240.0000.025
runEmptyDrops6.4250.0006.426
runEnrichR0.4700.0841.937
runFastMNN1.8190.1942.013
runFeatureSelection0.2150.0160.231
runFindMarker3.3010.2603.561
runGSVA0.8750.1271.002
runHarmony0.0370.0000.037
runKMeans0.4090.0120.421
runLimmaBC0.1430.0080.151
runMNNCorrect0.4900.0040.494
runModelGeneVar0.4360.0320.468
runNormalization2.2790.3322.611
runPerCellQC0.4760.0120.488
runSCANORAMA000
runSCMerge0.0050.0000.005
runScDblFinder19.868 0.58020.448
runScanpyFindClusters0.0250.0000.025
runScanpyFindHVG0.0230.0000.024
runScanpyFindMarkers0.0230.0000.024
runScanpyNormalizeData0.1860.0200.206
runScanpyPCA0.0240.0000.024
runScanpyScaleData0.0230.0000.023
runScanpyTSNE0.0230.0000.023
runScanpyUMAP0.0190.0040.023
runScranSNN0.6890.0560.745
runScrublet0.0210.0040.024
runSeuratFindClusters0.0240.0000.024
runSeuratFindHVG0.7240.1320.856
runSeuratHeatmap0.0240.0000.024
runSeuratICA0.0270.0000.027
runSeuratJackStraw0.0200.0040.024
runSeuratNormalizeData0.0230.0000.023
runSeuratPCA0.0190.0030.023
runSeuratSCTransform29.515 0.60830.125
runSeuratScaleData0.0240.0000.024
runSeuratUMAP0.0230.0000.024
runSingleR0.0330.0000.034
runSoupX0.0010.0000.000
runTSCAN1.3230.0321.355
runTSCANClusterDEAnalysis1.4590.0121.472
runTSCANDEG1.3440.0241.369
runTSNE0.8400.0040.845
runUMAP6.5870.1886.773
runVAM0.4760.0040.481
runZINBWaVE0.0040.0000.005
sampleSummaryStats0.2690.0000.270
scaterCPM0.1400.0040.143
scaterPCA0.5920.0120.604
scaterlogNormCounts0.2280.0120.241
sce0.0220.0000.023
sctkListGeneSetCollections0.0720.0000.073
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0780.0000.079
setSCTKDisplayRow0.3590.0120.371
singleCellTK0.0010.0000.000
subDiffEx0.5040.0080.511
subsetSCECols0.1570.0080.164
subsetSCERows0.3490.0240.373
summarizeSCE0.0650.0000.065
trimCounts0.1810.0160.197