Back to Build/check report for BioC 3.18:   simplified   long
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This page was generated on 2023-05-31 05:44:44 -0000 (Wed, 31 May 2023).

HostnameOSArch (*)R versionInstalled pkgs
kunpeng1Linux (Ubuntu 22.04.1 LTS)aarch644.3.0 (2023-04-21) -- "Already Tomorrow" 4219
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

CHECK results for singleCellTK on kunpeng1


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.

Note: If "R CMD check" recently failed on the Linux builder over a missing dependency, add the missing dependency to "Suggests" in your DESCRIPTION file. See the Renviron.bioc for details.

raw results

Package 1904/2197HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.11.0  (landing page)
Yichen Wang
Snapshot Date: 2023-05-29 10:19:22 -0000 (Mon, 29 May 2023)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: bce567d
git_last_commit_date: 2023-04-25 15:01:21 -0000 (Tue, 25 Apr 2023)
kunpeng1Linux (Ubuntu 22.04.1 LTS) / aarch64  OK    OK    OK  

Summary

Package: singleCellTK
Version: 2.11.0
Command: /home/biocbuild/R/R-4.3.0/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/R/R-4.3.0/site-library --timings singleCellTK_2.11.0.tar.gz
StartedAt: 2023-05-30 19:25:11 -0000 (Tue, 30 May 2023)
EndedAt: 2023-05-30 19:43:30 -0000 (Tue, 30 May 2023)
EllapsedTime: 1099.4 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R-4.3.0/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/home/biocbuild/R/R-4.3.0/site-library --timings singleCellTK_2.11.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.18-bioc/meat/singleCellTK.Rcheck’
* using R version 4.3.0 (2023-04-21)
* using platform: aarch64-unknown-linux-gnu (64-bit)
* R was compiled by
    gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
    GNU Fortran (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
* running under: Ubuntu 22.04.2 LTS
* using session charset: UTF-8
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.11.0’
* 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   1.6Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R 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 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 ... OK
* 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
plotScDblFinderResults   37.570  0.432  38.001
plotDoubletFinderResults 29.308  0.136  29.441
runScDblFinder           28.232  0.468  28.701
runDoubletFinder         22.751  1.164  23.916
importExampleData        19.382  0.868  26.846
plotBatchCorrCompare     13.220  0.116  13.328
plotScdsHybridResults    11.507  0.148  10.592
plotBcdsResults          10.390  0.136   9.490
plotDecontXResults        9.542  0.112   9.654
runDecontX                8.109  0.840   8.949
plotCxdsResults           7.836  0.052   7.884
plotUMAP                  7.664  0.076   7.738
runUMAP                   7.519  0.180   7.695
plotTSCANClusterDEG       7.100  0.088   7.188
detectCellOutlier         6.488  0.324   6.812
plotFindMarkerHeatmap     6.331  0.028   6.359
plotDEGViolin             6.036  0.088   6.124
plotEmptyDropsResults     5.817  0.016   5.833
plotEmptyDropsScatter     5.656  0.048   5.704
runEmptyDrops             5.313  0.032   5.345
plotDEGRegression         5.135  0.004   5.139
getEnrichRResult          0.533  0.012  10.109
runEnrichR                0.492  0.052   7.655
* 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 in ‘inst/doc’ ... OK
* checking running R code from vignettes ...
  ‘singleCellTK.Rmd’ using ‘UTF-8’... OK
 NONE
* checking re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  ‘/home/biocbuild/bbs-3.18-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.



Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R-4.3.0/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/R/R-4.3.0/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 4.3.0 (2023-04-21) -- "Already Tomorrow"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu (64-bit)

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 
  0.178   0.058   0.218 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.3.0 (2023-04-21) -- "Already Tomorrow"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu (64-bit)

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, sort, 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

Attaching package: 'S4Arrays'

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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Estimating GSVA scores for 34 gene sets.
Estimating ECDFs with Gaussian kernels

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Error in fitdistr(mahalanobis.sq.null[nonzero.values], "gamma", lower = 0.01) : 
  optimization failed
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: 9590

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8042
Number of communities: 6
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 20 | SKIP 0 | PASS 220 ]

[ FAIL 0 | WARN 20 | SKIP 0 | PASS 220 ]
> 
> proc.time()
   user  system elapsed 
304.743   4.307 324.454 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0040.0000.004
SEG0.0030.0000.003
calcEffectSizes1.0920.2481.341
combineSCE2.0680.0282.097
computeZScore0.3170.0080.325
convertSCEToSeurat4.3780.3564.734
convertSeuratToSCE0.5490.0000.549
dedupRowNames0.0760.0000.075
detectCellOutlier6.4880.3246.812
diffAbundanceFET0.0500.0040.055
discreteColorPalette0.0080.0000.008
distinctColors0.0030.0000.002
downSampleCells0.9380.0360.974
downSampleDepth0.7750.0040.779
expData-ANY-character-method0.4040.0040.408
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.4560.0000.457
expData-set0.450.000.45
expData0.4140.0280.442
expDataNames-ANY-method0.4130.0040.417
expDataNames0.3970.0000.397
expDeleteDataTag0.0390.0040.043
expSetDataTag0.0290.0080.037
expTaggedData0.0330.0000.033
exportSCE0.0240.0040.028
exportSCEtoAnnData0.0930.0040.097
exportSCEtoFlatFile0.0830.0120.095
featureIndex0.0470.0000.047
generateSimulatedData0.0500.0040.054
getBiomarker0.0600.0040.063
getDEGTopTable1.1440.0201.165
getDiffAbundanceResults0.0460.0000.046
getEnrichRResult 0.533 0.01210.109
getFindMarkerTopTable4.2730.0564.329
getMSigDBTable0.0050.0000.004
getPathwayResultNames0.0250.0040.028
getSampleSummaryStatsTable0.4230.0000.424
getSoupX000
getTSCANResults2.4380.0242.461
getTopHVG1.1010.0121.113
importAnnData0.0010.0000.001
importBUStools0.3730.0160.390
importCellRanger1.4740.0601.540
importCellRangerV2Sample0.3600.0030.364
importCellRangerV3Sample0.5700.0000.571
importDropEst0.4410.0000.442
importExampleData19.382 0.86826.846
importGeneSetsFromCollection0.9480.0400.988
importGeneSetsFromGMT0.0690.0160.086
importGeneSetsFromList0.1620.0080.170
importGeneSetsFromMSigDB4.3270.1324.460
importMitoGeneSet0.0620.0000.062
importOptimus0.0020.0000.002
importSEQC0.3040.0040.310
importSTARsolo0.3330.0520.387
iterateSimulations0.4790.0000.479
listSampleSummaryStatsTables0.5010.0000.502
mergeSCEColData0.6030.0120.614
mouseBrainSubsetSCE0.0310.0000.031
msigdb_table0.0010.0000.002
plotBarcodeRankDropsResults1.1730.0401.213
plotBarcodeRankScatter0.9420.0280.969
plotBatchCorrCompare13.220 0.11613.328
plotBatchVariance0.4830.0120.495
plotBcdsResults10.390 0.136 9.490
plotClusterAbundance1.4350.0041.439
plotCxdsResults7.8360.0527.884
plotDEGHeatmap3.9920.0474.039
plotDEGRegression5.1350.0045.139
plotDEGViolin6.0360.0886.124
plotDEGVolcano1.4330.0201.453
plotDecontXResults9.5420.1129.654
plotDimRed0.3580.0000.359
plotDoubletFinderResults29.308 0.13629.441
plotEmptyDropsResults5.8170.0165.833
plotEmptyDropsScatter5.6560.0485.704
plotFindMarkerHeatmap6.3310.0286.359
plotMASTThresholdGenes2.0250.0562.081
plotPCA0.6510.0000.651
plotPathway1.2530.0121.266
plotRunPerCellQCResults3.0620.0043.067
plotSCEBarAssayData0.2330.0080.240
plotSCEBarColData0.2500.0000.251
plotSCEBatchFeatureMean0.3080.0000.308
plotSCEDensity0.290.000.29
plotSCEDensityAssayData0.2220.0000.222
plotSCEDensityColData0.2850.0000.285
plotSCEDimReduceColData1.1160.0041.120
plotSCEDimReduceFeatures0.4920.0080.501
plotSCEHeatmap1.0450.0161.061
plotSCEScatter0.4760.0000.476
plotSCEViolin0.3240.0080.332
plotSCEViolinAssayData0.4400.0160.456
plotSCEViolinColData0.3240.0040.328
plotScDblFinderResults37.570 0.43238.001
plotScanpyDotPlot0.0280.0000.028
plotScanpyEmbedding0.0280.0000.027
plotScanpyHVG0.0270.0000.027
plotScanpyHeatmap0.0270.0000.027
plotScanpyMarkerGenes0.0270.0000.027
plotScanpyMarkerGenesDotPlot0.0270.0000.027
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plotScanpyMarkerGenesViolin0.0270.0000.027
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plotScanpyPCAGeneRanking0.0280.0000.027
plotScanpyPCAVariance0.0240.0040.027
plotScanpyViolin0.0290.0040.033
plotScdsHybridResults11.507 0.14810.592
plotScrubletResults0.0270.0000.028
plotSeuratElbow0.0270.0000.027
plotSeuratHVG0.0270.0000.027
plotSeuratJackStraw0.0280.0000.027
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plotTSCANClusterPseudo2.9440.0202.965
plotTSCANDimReduceFeatures3.1290.0683.196
plotTSCANPseudotimeGenes2.9870.0082.996
plotTSCANPseudotimeHeatmap3.1330.0203.153
plotTSCANResults3.0330.0483.082
plotTSNE0.7440.0120.757
plotTopHVG0.5360.0080.544
plotUMAP7.6640.0767.738
readSingleCellMatrix0.0060.0000.006
reportCellQC0.2560.0000.256
reportDropletQC0.0310.0040.034
reportQCTool0.2730.0000.274
retrieveSCEIndex0.0370.0000.036
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runComBatSeq0.6650.0160.681
runCxds0.7610.0080.769
runCxdsBcdsHybrid3.3690.0312.322
runDEAnalysis0.8970.0070.904
runDecontX8.1090.8408.949
runDimReduce0.6200.0160.636
runDoubletFinder22.751 1.16423.916
runDropletQC0.0280.0000.027
runEmptyDrops5.3130.0325.345
runEnrichR0.4920.0527.655
runFastMNN2.2710.1002.371
runFeatureSelection0.2770.0120.289
runFindMarker4.2420.1044.347
runGSVA0.9040.0280.932
runHarmony0.0500.0000.049
runKMeans0.5820.0360.618
runLimmaBC0.1010.0040.104
runMNNCorrect0.6570.0280.686
runModelGeneVar0.5790.0120.591
runNormalization0.6840.0480.732
runPerCellQC0.6990.0360.735
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runScDblFinder28.232 0.46828.701
runScanpyFindClusters0.0200.0150.035
runScanpyFindHVG0.0330.0000.033
runScanpyFindMarkers0.0330.0000.033
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runScanpyPCA0.0290.0000.029
runScanpyScaleData0.0300.0000.029
runScanpyTSNE0.0290.0000.030
runScanpyUMAP0.0300.0000.029
runScranSNN1.0110.0721.084
runScrublet0.0270.0000.028
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runSeuratFindHVG0.8450.0200.865
runSeuratHeatmap0.0280.0000.028
runSeuratICA0.0280.0000.028
runSeuratJackStraw0.0270.0000.028
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runSeuratPCA0.0280.0000.027
runSeuratSCTransform3.8670.2444.112
runSeuratScaleData0.0280.0000.027
runSeuratUMAP0.0270.0000.027
runSingleR0.0460.0000.046
runSoupX0.0000.0000.001
runTSCAN1.8560.0361.892
runTSCANClusterDEAnalysis2.0860.0162.102
runTSCANDEG2.0000.0042.005
runTSNE1.3320.0121.344
runUMAP7.5190.1807.695
runVAM0.7370.0000.738
runZINBWaVE0.0050.0000.005
sampleSummaryStats0.4010.0120.412
scaterCPM0.1540.0080.163
scaterPCA0.5850.0080.592
scaterlogNormCounts0.3270.0080.336
sce0.0290.0000.028
sctkListGeneSetCollections0.0980.0000.098
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.1060.0000.106
setSCTKDisplayRow0.5290.0080.536
singleCellTK000
subDiffEx0.6480.0120.661
subsetSCECols0.2420.0000.241
subsetSCERows0.5770.0080.585
summarizeSCE0.0740.0040.078
trimCounts0.3030.0070.310