Package: simpleSingleCell 1.37.0

Aaron Lun

simpleSingleCell: A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

Once a proud workflow package, this is now a shell of its former self. Almost all of its content has been cannibalized for use in the "Orchestrating Single-Cell Analyses with Bioconductor" book at https://osca.bioconductor.org. Most vignettes here are retained as reminders of the glory that once was, also providing redirection for existing external links to the relevant OSCA book chapters.

Authors:Aaron Lun [aut, cre], Davis McCarthy [aut], John Marioni [aut]

simpleSingleCell_1.37.0.tar.gz
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simpleSingleCell_1.37.0.tgz(r-4.6-any)simpleSingleCell_1.37.0.tgz(r-4.5-any)
simpleSingleCell_1.37.0.tar.gz(r-4.7-any)simpleSingleCell_1.37.0.tar.gz(r-4.6-any)
simpleSingleCell_1.37.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
simpleSingleCell/json (API)

# Install 'simpleSingleCell' in R:
install.packages('simpleSingleCell', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

On BioConductor:simpleSingleCell-1.37.0(workflows 3.24)simpleSingleCell-1.36.0(workflows 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

immunooncologyworkflowworkflowsinglecellworkflow

4.78 score 23 scripts 6 exports 38 dependencies

Last updated from:9ca00eba24. Checks:1 WARNING, 7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING180
linux-develERROR306
source / vignettesOK278
linux-releaseERROR261
macos-releaseERROR129
macos-oldrelERROR97
windows-develERROR139
windows-releaseERROR185
windows-oldrelERROR189
wasm-releaseOK177

Exports:chapterPreamblecompileChapterextractCachedprettySessionInfoscrapeDependenciessetupHTML

Dependencies:base64encBiocGenericsBiocManagerBiocStylebookdownbslibcachemcallrcliCodeDependscodetoolsdigestevaluatefastmapfontawesomefsgenericsgraphhighrhtmltoolsjquerylibjsonliteknitrlifecyclememoisemimeotelprocessxpsR6rappdirsrlangrmarkdownsasstinytexxfunXMLyaml

Detecting differental expression from single-cell RNA-seq data
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Last update: 2020-08-11
Started: 2019-01-02

Further strategies for analyzing single-cell RNA-seq data
Overview | Identifying correlated gene pairs with Spearman's rho | Comments on filtering by abundance | Concluding remarks | References
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Analyzing single-cell RNA-seq data with R/Bioconductor
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Last update: 2020-08-09
Started: 2017-12-12

Analyzing single-cell RNA-seq data containing read counts
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Last update: 2020-08-09
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Analyzing single-cell RNA-seq data containing UMI counts
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Last update: 2020-08-09
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Analyzing single-cell RNA sequencing data from droplet-based protocols
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Correcting batch effects in single-cell RNA-seq data
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Elaborating on cell-based quality control in single-cell RNA-seq data
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Normalizing single-cell RNA-seq data using spike-in information
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Detecting doublets in single-cell RNA-seq data
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Characterizing gene expression variance in single-cell RNA-seq data
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Multi-step correction of batch effects in single-cell RNA-seq data
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Last update: 2020-08-09
Started: 2019-05-12

Scalable analyses for big scRNA-seq data with Bioconductor
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Last update: 2020-08-09
Started: 2019-01-02