simpleSingleCell

This package is for version 3.12 of Bioconductor; for the stable, up-to-date release version, see simpleSingleCell.

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

Bioconductor version: 3.12

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.

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

Maintainer: Aaron Lun <infinite.monkeys.with.keyboards at gmail.com>

Citation (from within R, enter citation("simpleSingleCell")):

Installation

To install this package, start R (version "4.0") and enter:


if (!require("BiocManager", quietly = TRUE))
 install.packages("BiocManager")
BiocManager::install("simpleSingleCell")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("simpleSingleCell")

01. Introduction HTML

02. Read count data HTML

03. UMI count data HTML

04. Droplet-based data HTML

05. Correcting batch effects HTML

06. Quality control details HTML

07. Spike-in normalization HTML

08. Detecting doublets HTML

09. Advanced variance modelling HTML

10. Detecting differential expression HTML

11. Advanced batch correction HTML

12. Scalability for big data HTML

13. Further analysis strategies HTML R Script

Need some help? Ask on the Bioconductor Support site!

Details

biocViews ImmunoOncologyWorkflow, SingleCellWorkflow, Workflow

Version 1.14.0

License Artistic-2.0

Depends

Imports utils, methods, knitr, callr, rmarkdown, CodeDepends, BiocStyle

System Requirements

URL https://www.bioconductor.org/help/workflows/simpleSingleCell/

Suggests readxl, R.utils, SingleCellExperiment, scater, scran, limma, BiocFileCache, org.Mm.eg.db

Linking To

Enhances

Depends On Me

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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package simpleSingleCell_1.14.0.tar.gz

Windows Binary

macOS 10.13 (High Sierra)

Source Repository git clone https://git.bioconductor.org/packages/simpleSingleCell

Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/simpleSingleCell

Package Short Url https://bioconductor.org/packages/simpleSingleCell/

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