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This page was generated on 2024-06-25 17:42 -0400 (Tue, 25 Jun 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_644.4.0 (2024-04-24) -- "Puppy Cup" 4760
palomino3Windows Server 2022 Datacenterx644.4.0 (2024-04-24 ucrt) -- "Puppy Cup" 4494
merida1macOS 12.7.4 Montereyx86_644.4.0 (2024-04-24) -- "Puppy Cup" 4508
kjohnson1macOS 13.6.6 Venturaarm644.4.0 (2024-04-24) -- "Puppy Cup" 4466
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: 2024-06-23 14:00 -0400 (Sun, 23 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
merida1macOS 12.7.4 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson1macOS 13.6.6 Ventura / arm64  OK    OK    OK    OK  UNNEEDED, same version is already published

CHECK results for singleCellTK on merida1

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: 2024-06-24 11:56:54 -0400 (Mon, 24 Jun 2024)
EndedAt: 2024-06-24 12:29:59 -0400 (Mon, 24 Jun 2024)
EllapsedTime: 1984.8 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 (2024-04-24)
* 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 12.7.4
* 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:
    R         1.0Mb
    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
plotScDblFinderResults     50.094  1.493  60.304
plotDoubletFinderResults   47.369  0.345  51.439
runDoubletFinder           41.750  0.359  48.815
runScDblFinder             34.431  0.578  40.350
importExampleData          27.327  2.779  41.763
plotBatchCorrCompare       15.130  0.242  18.227
plotScdsHybridResults      13.901  0.218  16.622
plotTSCANClusterDEG        13.000  0.287  14.678
plotBcdsResults            12.632  0.386  14.450
plotDecontXResults         12.497  0.146  14.068
plotFindMarkerHeatmap      12.269  0.093  14.299
plotDEGViolin              11.059  0.201  12.228
plotEmptyDropsScatter      10.601  0.070  12.125
plotEmptyDropsResults      10.532  0.066  11.865
runEmptyDrops               9.936  0.079  11.666
plotCxdsResults             9.823  0.102  10.887
detectCellOutlier           9.697  0.228  11.675
convertSCEToSeurat          9.453  0.382  11.655
runSeuratSCTransform        9.606  0.138  11.749
runDecontX                  9.443  0.086  11.081
plotDEGRegression           9.346  0.110  10.466
getFindMarkerTopTable       8.698  0.103  10.149
runUMAP                     8.668  0.087  10.088
plotUMAP                    8.561  0.100  10.014
runFindMarker               8.466  0.108  10.046
plotDEGHeatmap              7.456  0.165   8.640
plotTSCANPseudotimeHeatmap  5.873  0.080   6.887
plotTSCANDimReduceFeatures  5.738  0.092   7.133
plotTSCANPseudotimeGenes    5.585  0.090   6.898
plotTSCANClusterPseudo      5.410  0.103   7.396
plotTSCANResults            5.433  0.063   6.337
plotRunPerCellQCResults     5.402  0.049   6.220
importGeneSetsFromMSigDB    4.907  0.204   6.028
getTSCANResults             4.416  0.074   5.258
runFastMNN                  4.211  0.080   5.333
plotMASTThresholdGenes      4.158  0.072   5.075
getEnrichRResult            0.740  0.059   5.242
* 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 (2024-04-24) -- "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 
  0.368   0.125   0.486

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-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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  |======================================================================| 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
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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 
483.732  10.656 564.636

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0040.0050.011
SEG0.0050.0050.011
calcEffectSizes0.5240.0600.654
combineSCE3.5420.1454.434
computeZScore0.4710.0210.577
convertSCEToSeurat 9.453 0.38211.655
convertSeuratToSCE1.2030.0161.405
dedupRowNames0.1230.0090.148
detectCellOutlier 9.697 0.22811.675
diffAbundanceFET0.1030.0110.163
discreteColorPalette0.0120.0020.024
distinctColors0.0040.0020.007
downSampleCells1.4450.1711.898
downSampleDepth1.3020.0621.642
expData-ANY-character-method0.7870.0120.965
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.8140.0130.949
expData-set0.8240.0100.960
expData0.7590.0650.960
expDataNames-ANY-method0.7860.0781.003
expDataNames0.7410.0140.863
expDeleteDataTag0.0820.0040.100
expSetDataTag0.0530.0030.064
expTaggedData0.0560.0040.070
exportSCE0.0490.0070.070
exportSCEtoAnnData0.1430.0070.176
exportSCEtoFlatFile0.1430.0060.174
featureIndex0.0750.0060.098
generateSimulatedData0.1000.0110.122
getBiomarker0.1290.0080.160
getDEGTopTable2.0800.0622.489
getDiffAbundanceResults0.0890.0040.105
getEnrichRResult0.7400.0595.242
getFindMarkerTopTable 8.698 0.10310.149
getMSigDBTable0.0080.0070.016
getPathwayResultNames0.0460.0070.063
getSampleSummaryStatsTable0.7510.0090.885
getSoupX0.0000.0000.001
getTSCANResults4.4160.0745.258
getTopHVG2.7660.0333.253
importAnnData0.0020.0010.003
importBUStools0.6760.0100.796
importCellRanger2.7730.0733.419
importCellRangerV2Sample0.6680.0060.788
importCellRangerV3Sample0.9850.0271.150
importDropEst0.7670.0080.892
importExampleData27.327 2.77941.763
importGeneSetsFromCollection1.6920.1522.139
importGeneSetsFromGMT0.1360.0100.173
importGeneSetsFromList0.2850.0100.342
importGeneSetsFromMSigDB4.9070.2046.028
importMitoGeneSet0.1210.0120.161
importOptimus0.0040.0010.004
importSEQC0.6560.0300.790
importSTARsolo0.6720.0080.764
iterateSimulations0.8100.0180.928
listSampleSummaryStatsTables0.9930.0121.145
mergeSCEColData1.1180.0341.296
mouseBrainSubsetSCE0.0670.0070.086
msigdb_table0.0030.0050.011
plotBarcodeRankDropsResults2.0000.0282.286
plotBarcodeRankScatter2.1460.0232.509
plotBatchCorrCompare15.130 0.24218.227
plotBatchVariance0.8040.0620.979
plotBcdsResults12.632 0.38614.450
plotBubble2.4520.0922.771
plotClusterAbundance2.0900.0172.292
plotCxdsResults 9.823 0.10210.887
plotDEGHeatmap7.4560.1658.640
plotDEGRegression 9.346 0.11010.466
plotDEGViolin11.059 0.20112.228
plotDEGVolcano2.3700.0322.652
plotDecontXResults12.497 0.14614.068
plotDimRed0.6680.0110.767
plotDoubletFinderResults47.369 0.34551.439
plotEmptyDropsResults10.532 0.06611.865
plotEmptyDropsScatter10.601 0.07012.125
plotFindMarkerHeatmap12.269 0.09314.299
plotMASTThresholdGenes4.1580.0725.075
plotPCA1.1860.0201.425
plotPathway2.0780.0272.452
plotRunPerCellQCResults5.4020.0496.220
plotSCEBarAssayData0.4240.0110.487
plotSCEBarColData0.3390.0080.392
plotSCEBatchFeatureMean0.5520.0070.653
plotSCEDensity0.5750.0140.673
plotSCEDensityAssayData0.3860.0100.457
plotSCEDensityColData0.5240.0110.623
plotSCEDimReduceColData1.7800.0232.099
plotSCEDimReduceFeatures0.9840.0191.410
plotSCEHeatmap1.6320.0231.976
plotSCEScatter0.8550.0161.120
plotSCEViolin0.5820.0150.858
plotSCEViolinAssayData0.6850.0170.946
plotSCEViolinColData0.5640.0160.919
plotScDblFinderResults50.094 1.49360.304
plotScanpyDotPlot0.0470.0050.063
plotScanpyEmbedding0.0400.0040.049
plotScanpyHVG0.0450.0030.056
plotScanpyHeatmap0.0400.0040.051
plotScanpyMarkerGenes0.0480.0050.058
plotScanpyMarkerGenesDotPlot0.0430.0050.058
plotScanpyMarkerGenesHeatmap0.0420.0050.056
plotScanpyMarkerGenesMatrixPlot0.0420.0070.062
plotScanpyMarkerGenesViolin0.0420.0080.059
plotScanpyMatrixPlot0.0420.0050.053
plotScanpyPCA0.0410.0060.054
plotScanpyPCAGeneRanking0.0410.0030.052
plotScanpyPCAVariance0.0420.0050.056
plotScanpyViolin0.0440.0040.057
plotScdsHybridResults13.901 0.21816.622
plotScrubletResults0.0460.0060.196
plotSeuratElbow0.0420.0070.052
plotSeuratHVG0.0410.0050.053
plotSeuratJackStraw0.0500.0050.056
plotSeuratReduction0.0440.0050.053
plotSoupXResults0.0010.0010.002
plotTSCANClusterDEG13.000 0.28714.678
plotTSCANClusterPseudo5.4100.1037.396
plotTSCANDimReduceFeatures5.7380.0927.133
plotTSCANPseudotimeGenes5.5850.0906.898
plotTSCANPseudotimeHeatmap5.8730.0806.887
plotTSCANResults5.4330.0636.337
plotTSNE1.2550.0221.508
plotTopHVG1.2180.0251.428
plotUMAP 8.561 0.10010.014
readSingleCellMatrix0.0100.0020.013
reportCellQC0.4060.0100.498
reportDropletQC0.0410.0040.051
reportQCTool0.4260.0100.507
retrieveSCEIndex0.0590.0060.073
runBBKNN0.0000.0010.001
runBarcodeRankDrops0.9650.0161.124
runBcds3.8220.0714.513
runCellQC0.4030.0090.464
runClusterSummaryMetrics1.7630.0502.088
runComBatSeq1.0280.0281.244
runCxds1.0860.0171.282
runCxdsBcdsHybrid3.9080.0804.651
runDEAnalysis1.7470.0412.076
runDecontX 9.443 0.08611.081
runDimReduce1.0950.0191.270
runDoubletFinder41.750 0.35948.815
runDropletQC0.0520.0040.066
runEmptyDrops 9.936 0.07911.666
runEnrichR0.6910.0471.977
runFastMNN4.2110.0805.333
runFeatureSelection0.4670.0100.537
runFindMarker 8.466 0.10810.046
runGSVA2.0550.0692.434
runHarmony0.0900.0020.109
runKMeans1.0600.0201.269
runLimmaBC0.1960.0030.234
runMNNCorrect1.3460.0231.594
runModelGeneVar1.0840.0141.264
runNormalization3.2740.0503.857
runPerCellQC1.2150.0231.425
runSCANORAMA0.0000.0010.001
runSCMerge0.0070.0020.011
runScDblFinder34.431 0.57840.350
runScanpyFindClusters0.0420.0050.050
runScanpyFindHVG0.0430.0070.052
runScanpyFindMarkers0.0400.0040.046
runScanpyNormalizeData0.4710.0100.531
runScanpyPCA0.0440.0040.054
runScanpyScaleData0.0400.0030.048
runScanpyTSNE0.0440.0070.056
runScanpyUMAP0.0450.0040.055
runScranSNN1.8080.0342.091
runScrublet0.0460.0070.067
runSeuratFindClusters0.0410.0060.051
runSeuratFindHVG1.9750.1192.472
runSeuratHeatmap0.0450.0060.059
runSeuratICA0.0500.0070.067
runSeuratJackStraw0.0460.0040.061
runSeuratNormalizeData0.0400.0050.055
runSeuratPCA0.0410.0060.058
runSeuratSCTransform 9.606 0.13811.749
runSeuratScaleData0.0460.0050.061
runSeuratUMAP0.0420.0030.054
runSingleR0.0920.0050.111
runSoupX0.0010.0010.001
runTSCAN3.6580.0464.323
runTSCANClusterDEAnalysis3.9400.0444.845
runTSCANDEG3.8380.0534.670
runTSNE1.7770.0312.114
runUMAP 8.668 0.08710.088
runVAM1.3270.0181.559
runZINBWaVE0.0070.0020.009
sampleSummaryStats0.6950.0140.827
scaterCPM0.2390.0040.267
scaterPCA1.5790.0171.847
scaterlogNormCounts0.5100.0110.567
sce0.0430.0070.054
sctkListGeneSetCollections0.1830.0090.220
sctkPythonInstallConda0.0010.0000.001
sctkPythonInstallVirtualEnv0.0000.0010.000
selectSCTKConda0.0000.0010.001
selectSCTKVirtualEnvironment000
setRowNames0.2950.0210.360
setSCTKDisplayRow0.9260.0121.049
singleCellTK0.0010.0010.001
subDiffEx1.1260.0401.319
subsetSCECols0.4180.0120.483
subsetSCERows0.9890.0201.129
summarizeSCE0.1360.0120.167
trimCounts0.3560.0110.411