--- 
title: "Orchestrating Single-Cell Analysis with Bioconductor"
documentclass: book
bibliography: [ref.bib, packages.bib]
biblio-style: apalike
link-citations: yes
favicon: "favicon.ico"
description: "Or: how I learned to stop worrying and love the t-SNEs."
cover-image: "https://github.com/Bioconductor/BiocStickers/raw/master/Bioconductor/Bioconductor-serial.gif"
github-repo: Bioconductor/OrchestratingSingleCellAnalysis
---



---
date: "**Authors:** Robert Amezquita [aut], Aaron Lun [aut, cre], Stephanie Hicks [aut], Raphael Gottardo [aut]<br/>
  **Version:** 1.0.6<br/>
  **Modified:** 2020-11-13<br/>
  **Compiled:** 2021-03-24<br/>
  **Environment:** R version 4.0.4 (2021-02-15), Bioconductor 3.12<br/>
  **License:** CC BY-NC-ND 3.0 US<br/>
  **Copyright:** Bioconductor, 2020<br/>
  **Source:** https://github.com/Bioconductor/OrchestratingSingleCellAnalysis"
url: "https://github.com/Bioconductor/OrchestratingSingleCellAnalysis"
---

# Welcome {-}

<a href="https://bioconductor.org"><img src="https://github.com/Bioconductor/BiocStickers/raw/master/Bioconductor/Bioconductor-serial.gif" width="200" alt="Bioconductor Sticker" align="right" style="margin: 0 1em 0 1em" /></a> 

This is the website for __"Orchestrating Single-Cell Analysis with Bioconductor"__, a book that teaches users some common workflows for the analysis of single-cell RNA-seq data (scRNA-seq). This book will teach you how to make use of cutting-edge Bioconductor tools to process, analyze, visualize, and explore scRNA-seq data. Additionally, it serves as an online companion for the manuscript __"Orchestrating Single-Cell Analysis with Bioconductor"__. 

While we focus here on scRNA-seq data, a newer technology that profiles transcriptomes at the single-cell level, many of the tools, conventions, and analysis strategies utilized throughout this book are broadly applicable to other types of assays. By learning the grammar of Bioconductor workflows, we hope to provide you a starting point for the exploration of your own data, whether it be scRNA-seq or otherwise. 

This book is organized into three parts. In the _Preamble_, we introduce the book and dive into resources for learning R and Bioconductor (both at a beginner and developer level). Part I ends with a tutorial for a key data infrastructure, the _SingleCellExperiment_ class, that is used throughout Bioconductor for single-cell analysis and in the subsequent section. 

The second part, _Focus Topics_, begins with an overview of the framework for analysis of scRNA-seq data, with deeper dives into specific topics are presented in each subsequent chapter. 

The third part, _Workflows_, provides primarily code detailing the analysis of various datasets throughout the book.

Finally, the _Appendix_ highlights our contributors.

If you would like to cite this work, please use the reference [__"Orchestrating Single-Cell Analysis with Bioconductor"__](https://www.nature.com/articles/s41592-019-0654-x). 

---

The book is written in [RMarkdown](https://rmarkdown.rstudio.com) with [bookdown](https://bookdown.org). OSCA is a collaborative effort, supported by various folks from the Bioconductor team who have contributed workflows, fixes, and improvements.

This website is __free to use__, and is licensed under the [Creative Commons Attribution-NonCommercial-NoDerivs 3.0](http://creativecommons.org/licenses/by-nc-nd/3.0/us/) License.