| Back to Multiple platform build/check report for BioC 3.19: simplified long |
|
This page was generated on 2024-07-24 09:08 -0400 (Wed, 24 Jul 2024).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo1 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4747 |
| palomino7 | Windows Server 2022 Datacenter | x64 | 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" | 4489 |
| merida1 | macOS 12.7.5 Monterey | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4518 |
| kjohnson1 | macOS 13.6.6 Ventura | arm64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4467 |
| 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 1992/2300 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| singleCellTK 2.14.0 (landing page) Joshua David Campbell
| nebbiolo1 | Linux (Ubuntu 22.04.3 LTS) / x86_64 | OK | OK | OK |  | ||||||||
| palomino7 | Windows Server 2022 Datacenter / x64 | OK | OK | ERROR | OK | |||||||||
| merida1 | macOS 12.7.5 Monterey / x86_64 | OK | OK | OK | OK | | ||||||||
| kjohnson1 | macOS 13.6.6 Ventura / arm64 | OK | OK | OK | OK | | ||||||||
|
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. |
| 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: 2024-07-23 11:18:55 -0400 (Tue, 23 Jul 2024) |
| EndedAt: 2024-07-23 11:37:35 -0400 (Tue, 23 Jul 2024) |
| EllapsedTime: 1120.4 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: singleCellTK.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### 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 4.4.1 (2024-06-14)
* using platform: aarch64-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 Ventura 13.6.6
* 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 44.404 0.277 45.624
plotScDblFinderResults 39.677 0.781 41.399
runDoubletFinder 39.794 0.233 40.748
importExampleData 25.128 1.633 29.750
runScDblFinder 22.670 0.501 23.379
plotBatchCorrCompare 14.593 0.134 14.922
plotScdsHybridResults 11.311 0.169 11.774
plotBcdsResults 10.201 0.184 10.589
plotDecontXResults 10.094 0.074 10.339
runDecontX 8.940 0.061 9.216
plotUMAP 8.521 0.075 8.799
runUMAP 8.477 0.068 8.838
detectCellOutlier 8.130 0.127 8.531
plotCxdsResults 7.973 0.061 8.411
plotEmptyDropsResults 6.852 0.034 6.958
plotEmptyDropsScatter 6.798 0.033 6.928
runSeuratSCTransform 6.576 0.098 6.806
plotTSCANClusterDEG 6.097 0.093 6.260
runEmptyDrops 5.957 0.027 6.087
plotFindMarkerHeatmap 5.044 0.046 5.155
convertSCEToSeurat 4.894 0.183 5.272
plotDEGViolin 4.949 0.107 5.245
getEnrichRResult 0.364 0.038 6.865
* 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.
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-arm64/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)
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: aarch64-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 0.072 0.294
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: aarch64-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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 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
|
| | 0%
|
|== | 3%
|
|==== | 6%
|
|====== | 9%
|
|======== | 12%
|
|========== | 15%
|
|============ | 18%
|
|============== | 21%
|
|================ | 24%
|
|=================== | 26%
|
|===================== | 29%
|
|======================= | 32%
|
|========================= | 35%
|
|=========================== | 38%
|
|============================= | 41%
|
|=============================== | 44%
|
|================================= | 47%
|
|=================================== | 50%
|
|===================================== | 53%
|
|======================================= | 56%
|
|========================================= | 59%
|
|=========================================== | 62%
|
|============================================= | 65%
|
|=============================================== | 68%
|
|================================================= | 71%
|
|=================================================== | 74%
|
|====================================================== | 76%
|
|======================================================== | 79%
|
|========================================================== | 82%
|
|============================================================ | 85%
|
|============================================================== | 88%
|
|================================================================ | 91%
|
|================================================================== | 94%
|
|==================================================================== | 97%
|
|======================================================================| 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 2 gene sets.
Estimating ECDFs with Gaussian kernels
|
| | 0%
|
|=================================== | 50%
|
|======================================================================| 100%
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
|
| | 0%
|
|======================================================================| 100%
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
317.184 6.487 343.194
singleCellTK.Rcheck/singleCellTK-Ex.timings
| name | user | system | elapsed | |
| MitoGenes | 0.003 | 0.004 | 0.007 | |
| SEG | 0.004 | 0.003 | 0.006 | |
| calcEffectSizes | 0.219 | 0.019 | 0.241 | |
| combineSCE | 1.593 | 0.047 | 1.664 | |
| computeZScore | 0.323 | 0.009 | 0.336 | |
| convertSCEToSeurat | 4.894 | 0.183 | 5.272 | |
| convertSeuratToSCE | 0.564 | 0.010 | 0.590 | |
| dedupRowNames | 0.082 | 0.003 | 0.089 | |
| detectCellOutlier | 8.130 | 0.127 | 8.531 | |
| diffAbundanceFET | 0.081 | 0.005 | 0.087 | |
| discreteColorPalette | 0.010 | 0.001 | 0.012 | |
| distinctColors | 0.002 | 0.000 | 0.003 | |
| downSampleCells | 0.828 | 0.073 | 0.929 | |
| downSampleDepth | 0.687 | 0.032 | 0.737 | |
| expData-ANY-character-method | 0.359 | 0.009 | 0.373 | |
| expData-set-ANY-character-CharacterOrNullOrMissing-logical-method | 0.411 | 0.009 | 0.423 | |
| expData-set | 0.386 | 0.009 | 0.399 | |
| expData | 0.372 | 0.023 | 0.398 | |
| expDataNames-ANY-method | 0.381 | 0.028 | 0.418 | |
| expDataNames | 0.342 | 0.008 | 0.354 | |
| expDeleteDataTag | 0.054 | 0.003 | 0.061 | |
| expSetDataTag | 0.039 | 0.004 | 0.044 | |
| expTaggedData | 0.045 | 0.004 | 0.050 | |
| exportSCE | 0.035 | 0.005 | 0.041 | |
| exportSCEtoAnnData | 0.135 | 0.005 | 0.141 | |
| exportSCEtoFlatFile | 0.131 | 0.006 | 0.140 | |
| featureIndex | 0.057 | 0.006 | 0.064 | |
| generateSimulatedData | 0.076 | 0.008 | 0.085 | |
| getBiomarker | 0.091 | 0.007 | 0.098 | |
| getDEGTopTable | 1.034 | 0.038 | 1.084 | |
| getDiffAbundanceResults | 0.069 | 0.004 | 0.073 | |
| getEnrichRResult | 0.364 | 0.038 | 6.865 | |
| getFindMarkerTopTable | 3.758 | 0.059 | 3.923 | |
| getMSigDBTable | 0.006 | 0.004 | 0.010 | |
| getPathwayResultNames | 0.035 | 0.006 | 0.041 | |
| getSampleSummaryStatsTable | 0.367 | 0.008 | 0.379 | |
| getSoupX | 0 | 0 | 0 | |
| getTSCANResults | 2.143 | 0.049 | 2.216 | |
| getTopHVG | 1.419 | 0.021 | 1.466 | |
| importAnnData | 0.002 | 0.001 | 0.002 | |
| importBUStools | 0.287 | 0.007 | 0.300 | |
| importCellRanger | 1.350 | 0.044 | 1.422 | |
| importCellRangerV2Sample | 0.290 | 0.004 | 0.297 | |
| importCellRangerV3Sample | 0.467 | 0.021 | 0.496 | |
| importDropEst | 0.342 | 0.004 | 0.350 | |
| importExampleData | 25.128 | 1.633 | 29.750 | |
| importGeneSetsFromCollection | 0.873 | 0.082 | 0.971 | |
| importGeneSetsFromGMT | 0.088 | 0.007 | 0.097 | |
| importGeneSetsFromList | 0.147 | 0.007 | 0.155 | |
| importGeneSetsFromMSigDB | 3.303 | 0.130 | 3.638 | |
| importMitoGeneSet | 0.074 | 0.009 | 0.087 | |
| importOptimus | 0.002 | 0.001 | 0.003 | |
| importSEQC | 0.341 | 0.013 | 0.362 | |
| importSTARsolo | 0.282 | 0.005 | 0.294 | |
| iterateSimulations | 0.430 | 0.014 | 0.458 | |
| listSampleSummaryStatsTables | 0.523 | 0.011 | 0.555 | |
| mergeSCEColData | 0.544 | 0.025 | 0.581 | |
| mouseBrainSubsetSCE | 0.051 | 0.006 | 0.057 | |
| msigdb_table | 0.001 | 0.003 | 0.005 | |
| plotBarcodeRankDropsResults | 1.029 | 0.020 | 1.060 | |
| plotBarcodeRankScatter | 0.985 | 0.013 | 1.027 | |
| plotBatchCorrCompare | 14.593 | 0.134 | 14.922 | |
| plotBatchVariance | 0.380 | 0.025 | 0.409 | |
| plotBcdsResults | 10.201 | 0.184 | 10.589 | |
| plotBubble | 1.224 | 0.041 | 1.318 | |
| plotClusterAbundance | 0.933 | 0.009 | 0.955 | |
| plotCxdsResults | 7.973 | 0.061 | 8.411 | |
| plotDEGHeatmap | 3.296 | 0.098 | 3.500 | |
| plotDEGRegression | 4.028 | 0.058 | 4.250 | |
| plotDEGViolin | 4.949 | 0.107 | 5.245 | |
| plotDEGVolcano | 1.279 | 0.019 | 1.340 | |
| plotDecontXResults | 10.094 | 0.074 | 10.339 | |
| plotDimRed | 0.343 | 0.009 | 0.360 | |
| plotDoubletFinderResults | 44.404 | 0.277 | 45.624 | |
| plotEmptyDropsResults | 6.852 | 0.034 | 6.958 | |
| plotEmptyDropsScatter | 6.798 | 0.033 | 6.928 | |
| plotFindMarkerHeatmap | 5.044 | 0.046 | 5.155 | |
| plotMASTThresholdGenes | 1.775 | 0.042 | 1.835 | |
| plotPCA | 0.575 | 0.015 | 0.599 | |
| plotPathway | 1.007 | 0.019 | 1.044 | |
| plotRunPerCellQCResults | 2.407 | 0.030 | 2.479 | |
| plotSCEBarAssayData | 0.242 | 0.009 | 0.259 | |
| plotSCEBarColData | 0.182 | 0.011 | 0.195 | |
| plotSCEBatchFeatureMean | 0.242 | 0.005 | 0.251 | |
| plotSCEDensity | 0.319 | 0.013 | 0.337 | |
| plotSCEDensityAssayData | 0.201 | 0.009 | 0.213 | |
| plotSCEDensityColData | 0.242 | 0.010 | 0.255 | |
| plotSCEDimReduceColData | 0.830 | 0.018 | 0.863 | |
| plotSCEDimReduceFeatures | 0.473 | 0.011 | 0.494 | |
| plotSCEHeatmap | 0.740 | 0.011 | 0.766 | |
| plotSCEScatter | 0.413 | 0.011 | 0.429 | |
| plotSCEViolin | 0.295 | 0.008 | 0.306 | |
| plotSCEViolinAssayData | 0.347 | 0.010 | 0.362 | |
| plotSCEViolinColData | 0.282 | 0.011 | 0.303 | |
| plotScDblFinderResults | 39.677 | 0.781 | 41.399 | |
| plotScanpyDotPlot | 0.033 | 0.005 | 0.040 | |
| plotScanpyEmbedding | 0.036 | 0.011 | 0.068 | |
| plotScanpyHVG | 0.045 | 0.003 | 0.050 | |
| plotScanpyHeatmap | 0.037 | 0.002 | 0.041 | |
| plotScanpyMarkerGenes | 0.037 | 0.004 | 0.043 | |
| plotScanpyMarkerGenesDotPlot | 0.039 | 0.003 | 0.043 | |
| plotScanpyMarkerGenesHeatmap | 0.038 | 0.003 | 0.041 | |
| plotScanpyMarkerGenesMatrixPlot | 0.037 | 0.004 | 0.045 | |
| plotScanpyMarkerGenesViolin | 0.068 | 0.003 | 0.071 | |
| plotScanpyMatrixPlot | 0.038 | 0.002 | 0.043 | |
| plotScanpyPCA | 0.037 | 0.002 | 0.042 | |
| plotScanpyPCAGeneRanking | 0.041 | 0.002 | 0.045 | |
| plotScanpyPCAVariance | 0.037 | 0.002 | 0.041 | |
| plotScanpyViolin | 0.036 | 0.002 | 0.038 | |
| plotScdsHybridResults | 11.311 | 0.169 | 11.774 | |
| plotScrubletResults | 0.036 | 0.006 | 0.043 | |
| plotSeuratElbow | 0.038 | 0.006 | 0.044 | |
| plotSeuratHVG | 0.042 | 0.006 | 0.048 | |
| plotSeuratJackStraw | 0.043 | 0.006 | 0.051 | |
| plotSeuratReduction | 0.037 | 0.004 | 0.041 | |
| plotSoupXResults | 0 | 0 | 0 | |
| plotTSCANClusterDEG | 6.097 | 0.093 | 6.260 | |
| plotTSCANClusterPseudo | 2.630 | 0.040 | 2.708 | |
| plotTSCANDimReduceFeatures | 2.573 | 0.034 | 2.645 | |
| plotTSCANPseudotimeGenes | 2.477 | 0.034 | 2.541 | |
| plotTSCANPseudotimeHeatmap | 2.758 | 0.041 | 2.849 | |
| plotTSCANResults | 2.499 | 0.043 | 2.592 | |
| plotTSNE | 0.498 | 0.016 | 0.530 | |
| plotTopHVG | 0.540 | 0.012 | 0.562 | |
| plotUMAP | 8.521 | 0.075 | 8.799 | |
| readSingleCellMatrix | 0.006 | 0.000 | 0.007 | |
| reportCellQC | 0.203 | 0.008 | 0.215 | |
| reportDropletQC | 0.037 | 0.007 | 0.044 | |
| reportQCTool | 0.203 | 0.008 | 0.213 | |
| retrieveSCEIndex | 0.039 | 0.007 | 0.047 | |
| runBBKNN | 0.000 | 0.000 | 0.001 | |
| runBarcodeRankDrops | 0.482 | 0.011 | 0.506 | |
| runBcds | 2.039 | 0.092 | 2.179 | |
| runCellQC | 0.212 | 0.010 | 0.227 | |
| runClusterSummaryMetrics | 0.859 | 0.030 | 0.910 | |
| runComBatSeq | 0.519 | 0.014 | 0.542 | |
| runCxds | 0.573 | 0.016 | 0.601 | |
| runCxdsBcdsHybrid | 2.178 | 0.101 | 2.316 | |
| runDEAnalysis | 0.902 | 0.027 | 0.950 | |
| runDecontX | 8.940 | 0.061 | 9.216 | |
| runDimReduce | 0.519 | 0.011 | 0.544 | |
| runDoubletFinder | 39.794 | 0.233 | 40.748 | |
| runDropletQC | 0.035 | 0.005 | 0.040 | |
| runEmptyDrops | 5.957 | 0.027 | 6.087 | |
| runEnrichR | 0.340 | 0.029 | 3.975 | |
| runFastMNN | 1.794 | 0.034 | 1.849 | |
| runFeatureSelection | 0.266 | 0.006 | 0.274 | |
| runFindMarker | 4.044 | 0.066 | 4.179 | |
| runGSVA | 0.974 | 0.036 | 1.037 | |
| runHarmony | 0.045 | 0.002 | 0.047 | |
| runKMeans | 0.542 | 0.017 | 0.566 | |
| runLimmaBC | 0.091 | 0.002 | 0.094 | |
| runMNNCorrect | 0.701 | 0.010 | 0.720 | |
| runModelGeneVar | 0.535 | 0.012 | 0.551 | |
| runNormalization | 2.990 | 0.037 | 3.057 | |
| runPerCellQC | 0.608 | 0.017 | 0.640 | |
| runSCANORAMA | 0 | 0 | 0 | |
| runSCMerge | 0.005 | 0.002 | 0.006 | |
| runScDblFinder | 22.670 | 0.501 | 23.379 | |
| runScanpyFindClusters | 0.033 | 0.005 | 0.039 | |
| runScanpyFindHVG | 0.017 | 0.001 | 0.019 | |
| runScanpyFindMarkers | 0.033 | 0.003 | 0.036 | |
| runScanpyNormalizeData | 0.180 | 0.004 | 0.185 | |
| runScanpyPCA | 0.035 | 0.002 | 0.036 | |
| runScanpyScaleData | 0.038 | 0.003 | 0.041 | |
| runScanpyTSNE | 0.038 | 0.006 | 0.044 | |
| runScanpyUMAP | 0.035 | 0.007 | 0.043 | |
| runScranSNN | 0.858 | 0.021 | 0.882 | |
| runScrublet | 0.037 | 0.002 | 0.039 | |
| runSeuratFindClusters | 0.035 | 0.002 | 0.037 | |
| runSeuratFindHVG | 0.738 | 0.057 | 0.805 | |
| runSeuratHeatmap | 0.026 | 0.004 | 0.030 | |
| runSeuratICA | 0.017 | 0.005 | 0.022 | |
| runSeuratJackStraw | 0.036 | 0.005 | 0.042 | |
| runSeuratNormalizeData | 0.016 | 0.003 | 0.019 | |
| runSeuratPCA | 0.033 | 0.003 | 0.036 | |
| runSeuratSCTransform | 6.576 | 0.098 | 6.806 | |
| runSeuratScaleData | 0.037 | 0.005 | 0.043 | |
| runSeuratUMAP | 0.042 | 0.006 | 0.049 | |
| runSingleR | 0.043 | 0.004 | 0.048 | |
| runSoupX | 0 | 0 | 0 | |
| runTSCAN | 1.796 | 0.031 | 1.863 | |
| runTSCANClusterDEAnalysis | 1.813 | 0.028 | 1.884 | |
| runTSCANDEG | 1.734 | 0.021 | 1.802 | |
| runTSNE | 0.983 | 0.021 | 1.122 | |
| runUMAP | 8.477 | 0.068 | 8.838 | |
| runVAM | 0.524 | 0.012 | 0.583 | |
| runZINBWaVE | 0.006 | 0.001 | 0.006 | |
| sampleSummaryStats | 0.327 | 0.008 | 0.345 | |
| scaterCPM | 0.184 | 0.008 | 0.197 | |
| scaterPCA | 0.781 | 0.013 | 0.830 | |
| scaterlogNormCounts | 0.313 | 0.007 | 0.328 | |
| sce | 0.035 | 0.011 | 0.046 | |
| sctkListGeneSetCollections | 0.101 | 0.013 | 0.119 | |
| sctkPythonInstallConda | 0 | 0 | 0 | |
| sctkPythonInstallVirtualEnv | 0 | 0 | 0 | |
| selectSCTKConda | 0 | 0 | 0 | |
| selectSCTKVirtualEnvironment | 0 | 0 | 0 | |
| setRowNames | 0.201 | 0.013 | 0.220 | |
| setSCTKDisplayRow | 0.475 | 0.012 | 0.502 | |
| singleCellTK | 0.000 | 0.001 | 0.000 | |
| subDiffEx | 0.616 | 0.035 | 0.673 | |
| subsetSCECols | 0.210 | 0.015 | 0.228 | |
| subsetSCERows | 0.506 | 0.016 | 0.541 | |
| summarizeSCE | 0.092 | 0.009 | 0.105 | |
| trimCounts | 0.275 | 0.019 | 0.315 | |