Chapter 2 Introduction

2.1 Why reading this book?

The chapters are organized into several parts:

  • Introduction: introduction, background on SRT, and R/Bioconductor spatial object classes

  • Preprocessing steps: preprocessing steps to prepare raw data for loading into R

  • Analysis steps: chapters describing individual analysis steps

  • Workflows: chapters containing complete workflows for several example datasets

  • Appendix: related resources, contributors and acknowledgments, and references

For readers who are new to R and Bioconductor, additional useful resources include:

  • The Orchestrating Single-Cell Analysis with Bioconductor (OSCA) book (Amezquita et al. 2020), which contains additional introductory material on R and Bioconductor, as well as additional details on analysis steps and method originally developed for single-cell data

  • The R for Data Science online book provides an excellent introduction to R

  • Data Carpentry and Software Carpentry provide introductory and more advanced online lesson materials on R programming, the Unix shell, and git

2.2 Bioconductor

Bioconductor is an open source and open development project, providing a cohesive and flexible framework for analyzing high-throughput genomic data in R (Huber et al. 2015). The Bioconductor project consists of around 2,000 contributed R packages, as well as core infrastructure maintained by the Bioconductor Core Team, providing a rich analysis environment for users.

One of the main advantages of Bioconductor is the modularity and open development philosophy. R packages are contributed by numerous research groups, with the Bioconductor Core Team coordinating the overall project and maintaining core infrastructure, build testing, and development guidelines. A key feature is that contributed packages use consistent data structures allowing users to easily integrate packages into analysis workflows. Bioconductor packages are also required to include comprehensive documentation, including vignettes containing long-form examples or tutorials.

This modular and open-development approach allows data analysts to integrate methods into analysis workflows, and crucially, does not lock-in users to methods developed by a single group. This enables users to integrate the latest state-of-the-art methods into their analysis workflows, regardless of where these are developed. Any research group around the world can contribute new packages to Bioconductor by following the contribution guidelines provided on the Bioconductor website.