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This page was generated on 2024-05-04 11:40:00 -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 lconway


To the developers/maintainers of the singleCellTK package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/singleCellTK.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results


Summary

Package: singleCellTK
Version: 2.14.0
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.14.0.tar.gz
StartedAt: 2024-05-04 00:59:53 -0400 (Sat, 04 May 2024)
EndedAt: 2024-05-04 01:17:05 -0400 (Sat, 04 May 2024)
EllapsedTime: 1032.6 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.14.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.19-bioc/meat/singleCellTK.Rcheck’
* using R version 4.4.0 beta (2024-04-14 r86421)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.14.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... NOTE
  installed size is  6.8Mb
  sub-directories of 1Mb or more:
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) dedupRowNames.Rd:10: Lost braces
    10 | \item{x}{A matrix like or /linkS4class{SingleCellExperiment} object, on which
       |                                       ^
checkRd: (-1) dedupRowNames.Rd:14: Lost braces
    14 | /linkS4class{SingleCellExperiment} object. When set to \code{TRUE}, will
       |             ^
checkRd: (-1) dedupRowNames.Rd:22: Lost braces
    22 | By default, a matrix or /linkS4class{SingleCellExperiment} object
       |                                     ^
checkRd: (-1) dedupRowNames.Rd:24: Lost braces
    24 | When \code{x} is a /linkS4class{SingleCellExperiment} and \code{as.rowData}
       |                                ^
checkRd: (-1) plotBubble.Rd:42: Lost braces
    42 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runClusterSummaryMetrics.Rd:27: Lost braces
    27 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runEmptyDrops.Rd:66: Lost braces
    66 | provided \\linkS4class{SingleCellExperiment} object.
       |                       ^
checkRd: (-1) runSCMerge.Rd:44: Lost braces
    44 | construct pseudo-replicates. The length of code{kmeansK} needs to be the same
       |                                                ^
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
plotDoubletFinderResults 36.557  0.407  37.256
runDoubletFinder         32.985  0.314  33.518
plotScDblFinderResults   31.576  1.016  32.910
runScDblFinder           21.957  0.493  22.611
importExampleData        17.649  2.238  20.551
plotBatchCorrCompare     12.085  0.172  12.345
plotScdsHybridResults     9.494  0.314   9.890
plotBcdsResults           8.900  0.256   9.239
plotDecontXResults        8.533  0.109   8.722
runDecontX                7.686  0.115   7.847
runUMAP                   7.107  0.126   7.291
plotUMAP                  6.966  0.115   7.126
plotCxdsResults           6.919  0.092   7.062
plotEmptyDropsScatter     6.352  0.069   6.474
plotTSCANClusterDEG       6.257  0.148   6.463
plotEmptyDropsResults     6.310  0.069   6.434
detectCellOutlier         6.008  0.221   6.278
runSeuratSCTransform      6.087  0.137   6.341
runEmptyDrops             6.158  0.050   6.240
plotFindMarkerHeatmap     5.289  0.068   5.404
convertSCEToSeurat        4.969  0.276   5.305
plotDEGViolin             5.094  0.131   5.273
getEnrichRResult          0.360  0.050   7.351
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

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


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library’
* installing *source* package ‘singleCellTK’ ...
** using staged installation
** R
** data
** exec
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (singleCellTK)

Tests output

singleCellTK.Rcheck/tests/spelling.Rout


R version 4.4.0 beta (2024-04-14 r86421) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
NULL
> 
> proc.time()
   user  system elapsed 
  0.228   0.084   0.305 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.0 beta (2024-04-14 r86421) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(singleCellTK)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

The following objects are masked from 'package:matrixStats':

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
    union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:utils':

    findMatches

The following objects are masked from 'package:base':

    I, expand.grid, unname

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

The following objects are masked from 'package:matrixStats':

    anyMissing, rowMedians

Loading required package: SingleCellExperiment
Loading required package: DelayedArray
Loading required package: Matrix

Attaching package: 'Matrix'

The following object is masked from 'package:S4Vectors':

    expand

Loading required package: S4Arrays
Loading required package: abind

Attaching package: 'S4Arrays'

The following object is masked from 'package:abind':

    abind

The following object is masked from 'package:base':

    rowsum

Loading required package: SparseArray

Attaching package: 'DelayedArray'

The following objects are masked from 'package:base':

    apply, scale, sweep


Attaching package: 'singleCellTK'

The following object is masked from 'package:BiocGenerics':

    plotPCA

> 
> test_check("singleCellTK")
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 0 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 1 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Uploading data to Enrichr... Done.
  Querying HDSigDB_Human_2021... Done.
Parsing results... Done.
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
No annotation package name available in the input data object.
Attempting to directly match identifiers in data to gene sets.
Estimating GSVA scores for 34 gene sets.
Estimating ECDFs with Gaussian kernels

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No annotation package name available in the input data object.
Attempting to directly match identifiers in data to gene sets.
Estimating GSVA scores for 2 gene sets.
Estimating ECDFs with Gaussian kernels

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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 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 
291.459   8.389 305.751 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0030.0030.005
SEG0.0030.0030.006
calcEffectSizes0.2370.0110.251
combineSCE1.8490.0501.918
computeZScore0.9490.0170.970
convertSCEToSeurat4.9690.2765.305
convertSeuratToSCE0.5840.0240.613
dedupRowNames0.0840.0100.096
detectCellOutlier6.0080.2216.278
diffAbundanceFET0.0690.0050.075
discreteColorPalette0.0090.0010.010
distinctColors0.0040.0010.004
downSampleCells0.8140.0830.905
downSampleDepth0.6360.0380.682
expData-ANY-character-method0.3780.0120.392
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.3970.0110.413
expData-set0.4180.0140.438
expData0.4040.0290.436
expDataNames-ANY-method0.3560.0100.369
expDataNames0.3410.0070.350
expDeleteDataTag0.0400.0030.044
expSetDataTag0.0320.0020.033
expTaggedData0.0300.0030.032
exportSCE0.0230.0040.027
exportSCEtoAnnData0.0920.0040.096
exportSCEtoFlatFile0.0930.0040.098
featureIndex0.0460.0040.051
generateSimulatedData0.0570.0050.063
getBiomarker0.0780.0060.086
getDEGTopTable1.0710.0471.128
getDiffAbundanceResults0.0620.0050.067
getEnrichRResult0.3600.0507.351
getFindMarkerTopTable4.3340.0844.459
getMSigDBTable0.0040.0030.006
getPathwayResultNames0.0280.0040.033
getSampleSummaryStatsTable0.3400.0090.353
getSoupX0.0000.0010.000
getTSCANResults2.1670.0672.254
getTopHVG1.3840.0301.426
importAnnData0.0010.0010.002
importBUStools0.3340.0090.347
importCellRanger1.3240.0501.393
importCellRangerV2Sample0.3860.0060.397
importCellRangerV3Sample0.4480.0190.473
importDropEst0.3360.0060.344
importExampleData17.649 2.23820.551
importGeneSetsFromCollection0.9060.1541.077
importGeneSetsFromGMT0.0820.0080.090
importGeneSetsFromList0.1540.0080.165
importGeneSetsFromMSigDB3.8780.2064.131
importMitoGeneSet0.0670.0110.079
importOptimus0.0020.0010.003
importSEQC0.2920.0180.313
importSTARsolo0.3400.0310.377
iterateSimulations0.4780.0200.503
listSampleSummaryStatsTables0.4450.0110.459
mergeSCEColData0.5420.0280.579
mouseBrainSubsetSCE0.0470.0080.057
msigdb_table0.0010.0020.004
plotBarcodeRankDropsResults1.0260.0301.064
plotBarcodeRankScatter0.9580.0160.980
plotBatchCorrCompare12.085 0.17212.345
plotBatchVariance0.3660.0190.389
plotBcdsResults8.9000.2569.239
plotBubble1.2010.0191.231
plotClusterAbundance1.1120.0171.137
plotCxdsResults6.9190.0927.062
plotDEGHeatmap3.4670.1213.615
plotDEGRegression4.5060.0884.637
plotDEGViolin5.0940.1315.273
plotDEGVolcano1.0720.0221.104
plotDecontXResults8.5330.1098.722
plotDimRed0.3220.0100.335
plotDoubletFinderResults36.557 0.40737.256
plotEmptyDropsResults6.3100.0696.434
plotEmptyDropsScatter6.3520.0696.474
plotFindMarkerHeatmap5.2890.0685.404
plotMASTThresholdGenes1.9870.0552.067
plotPCA0.5900.0160.614
plotPathway1.0490.0201.080
plotRunPerCellQCResults2.6220.0402.685
plotSCEBarAssayData0.2270.0100.242
plotSCEBarColData0.1700.0090.181
plotSCEBatchFeatureMean0.2560.0050.264
plotSCEDensity0.2510.0070.260
plotSCEDensityAssayData0.1940.0070.204
plotSCEDensityColData0.2880.0080.298
plotSCEDimReduceColData0.8520.0180.878
plotSCEDimReduceFeatures0.5100.0150.536
plotSCEHeatmap0.8410.0160.869
plotSCEScatter0.4550.0130.474
plotSCEViolin0.3560.0100.370
plotSCEViolinAssayData0.3290.0090.342
plotSCEViolinColData0.3030.0100.319
plotScDblFinderResults31.576 1.01632.910
plotScanpyDotPlot0.0290.0040.033
plotScanpyEmbedding0.0350.0050.041
plotScanpyHVG0.0300.0050.039
plotScanpyHeatmap0.0280.0030.031
plotScanpyMarkerGenes0.0310.0040.035
plotScanpyMarkerGenesDotPlot0.0290.0050.034
plotScanpyMarkerGenesHeatmap0.0280.0030.032
plotScanpyMarkerGenesMatrixPlot0.0300.0040.034
plotScanpyMarkerGenesViolin0.0280.0030.033
plotScanpyMatrixPlot0.0340.0040.038
plotScanpyPCA0.0290.0040.033
plotScanpyPCAGeneRanking0.0290.0040.034
plotScanpyPCAVariance0.0310.0040.036
plotScanpyViolin0.0330.0040.038
plotScdsHybridResults9.4940.3149.890
plotScrubletResults0.0250.0020.027
plotSeuratElbow0.0250.0020.028
plotSeuratHVG0.0260.0030.029
plotSeuratJackStraw0.0350.0050.040
plotSeuratReduction0.0290.0040.033
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plotTSCANClusterDEG6.2570.1486.463
plotTSCANClusterPseudo2.6290.0422.690
plotTSCANDimReduceFeatures2.6190.0422.680
plotTSCANPseudotimeGenes2.6170.0432.685
plotTSCANPseudotimeHeatmap3.0820.0563.170
plotTSCANResults2.6250.0442.692
plotTSNE0.6040.0160.624
plotTopHVG0.6060.0190.632
plotUMAP6.9660.1157.126
readSingleCellMatrix0.0050.0010.006
reportCellQC0.1960.0060.203
reportDropletQC0.0300.0040.035
reportQCTool0.2010.0060.208
retrieveSCEIndex0.0350.0040.040
runBBKNN0.0000.0010.001
runBarcodeRankDrops0.4830.0110.498
runBcds2.1210.0662.202
runCellQC0.2120.0110.226
runClusterSummaryMetrics0.8740.0560.937
runComBatSeq0.5270.0200.550
runCxds0.5500.0110.565
runCxdsBcdsHybrid2.0980.0652.176
runDEAnalysis0.8490.0140.866
runDecontX7.6860.1157.847
runDimReduce0.5640.0110.577
runDoubletFinder32.985 0.31433.518
runDropletQC0.0260.0030.030
runEmptyDrops6.1580.0506.240
runEnrichR0.3290.0342.078
runFastMNN2.0110.0612.088
runFeatureSelection0.2820.0100.296
runFindMarker4.1780.0774.281
runGSVA1.1350.0501.195
runHarmony0.0440.0020.047
runKMeans0.5540.0130.570
runLimmaBC0.0950.0020.098
runMNNCorrect0.5870.0080.597
runModelGeneVar0.5670.0110.582
runNormalization2.4340.0672.520
runPerCellQC0.6110.0150.634
runSCANORAMA000
runSCMerge0.0050.0010.006
runScDblFinder21.957 0.49322.611
runScanpyFindClusters0.0300.0060.036
runScanpyFindHVG0.0270.0040.033
runScanpyFindMarkers0.0280.0040.032
runScanpyNormalizeData0.2580.0080.268
runScanpyPCA0.0270.0030.038
runScanpyScaleData0.0330.0050.038
runScanpyTSNE0.0320.0050.036
runScanpyUMAP0.0300.0060.038
runScranSNN0.9010.0230.931
runScrublet0.0280.0040.032
runSeuratFindClusters0.0290.0040.032
runSeuratFindHVG0.9770.1041.090
runSeuratHeatmap0.0310.0060.038
runSeuratICA0.0290.0030.032
runSeuratJackStraw0.0260.0050.031
runSeuratNormalizeData0.0320.0060.039
runSeuratPCA0.0290.0040.034
runSeuratSCTransform6.0870.1376.341
runSeuratScaleData0.0370.0050.042
runSeuratUMAP0.0290.0050.035
runSingleR0.0410.0030.043
runSoupX0.0010.0000.000
runTSCAN1.7950.0301.836
runTSCANClusterDEAnalysis2.0040.0452.068
runTSCANDEG1.9750.0362.030
runTSNE1.0140.0241.048
runUMAP7.1070.1267.291
runVAM0.6540.0130.672
runZINBWaVE0.0040.0010.006
sampleSummaryStats0.3430.0090.355
scaterCPM0.1420.0040.150
scaterPCA0.7180.0170.740
scaterlogNormCounts0.2680.0050.276
sce0.0250.0050.030
sctkListGeneSetCollections0.1030.0050.109
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv0.0000.0000.001
selectSCTKConda000
selectSCTKVirtualEnvironment0.0010.0000.001
setRowNames0.0910.0060.096
setSCTKDisplayRow0.5050.0270.537
singleCellTK0.0010.0010.000
subDiffEx0.5800.0260.609
subsetSCECols0.1820.0070.189
subsetSCERows0.4600.0130.475
summarizeSCE0.0790.0070.087
trimCounts0.2140.0090.227