1 Introduction

Editors: Levi Waldron1 Sean Davis2 Marcel Ramos3 Lori Shepherd4. Martin Morgan5.
Last modified: 30 July, 2018.

Welcome to Bioc2018. This year’s conference includes a wide array of workshops for audiences ranging from beginner to advance users and developers. Workshop materials are available as a book in html, pdf, and eBook format at https://bioconductor.github.io/BiocWorkshops/. Workshops are organized by level and topic according to numbers, as described below. Every workshop starts with a syllabus that will help you to decide whether it matches your learning goals.

1.1 The Workshops

This book contains workshops for R / Bioconductor training. The workshops are divided into 3 sections:

  • Learn (100-series chapters) contains material for beginning users of R and Bioconductor. However, even experienced R and Bioconductor users may find something new here.
    • 100: R and Bioconductor for everyone: an introduction
    • 101: Introduction to Bioconductor annotation resources
    • 102: Solving common bioinformatic challenges using GenomicRanges
    • 103: Public data resources and Bioconductor
  • Use (200-series chapters) contains workshops emphasizing use of Bioconductor for common tasks, e.g., RNA-seq differential expression, single-cell analysis, gene set enrichment, multi’omics analysis, genome analysis, network analysis, and pharmacogenomics.
    • 200: RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
    • 201: RNA-seq data analysis with DESeq2
    • 202: Analysis of single-cell RNA-seq data: Dimensionality reduction, clustering, and lineage inference
    • 210: Functional enrichment analysis of high-throughput omics data
    • 220: Workflow for multi-omics analysis with MultiAssayExperiment
    • 230: Cytoscape automation in R using Rcy3
    • 240: Fluent genomic data analysis with plyranges
    • 250: Working with genomic data in R with the DECIPHER package
    • 260: Biomarker discovery from large pharmacogenomics datasets
  • Develop (500-series chapters) contains workshops to help expert users hone their skills and contribute their domain-specific knowledge to the Bioconductor community. These workshops are presented on “Developer Day”.
    • 500: Effectively using the DelayedArray framework to support the analysis of large datasets
    • 510: Maintaining your Bioconductor package

1.2 How to use these workshops

These workshops have a lot of package dependencies, and some use data that you must have on disk. There are several ways to run the code from these workshops yourself.

1.2.1 Install on your own computer

These workshops were developed for Bioconductor 3.8 (development branch) to allow teaching the most up-to-date methods. Some, but not all, workshop materials will work on Bioconductor 3.7, and (almost?) all should work after the release of Bioconductor 3.8 in October 2018. To run the workshops on your own computer, you should install Bioconductor >= 3.8 (which is the development version before October 2018, and the release version thereafter). The following commands should then install all needed dependencies, at least for Linux:

if (!require("BiocManager"))

We have noticed that on Windows and Mac, this may not install the required annotation and experimental data packages. If you get errors about missing dependencies, you can install these with additional calls to the BiocManager::install() function.

1.2.2 Use the exact same AMI as the workshop

Each participant in the Bioc2018 workshops was provided with their own machine image (called an Amazon Machine Image [AMI]) that contained up-to-date versions of R, required R packages, all necessary operating system libraries, and all workshop materials. This image has been tested by the organizers and more than 100 workshop participants, so if you use it, everything will just work. Using this image is the most hassle-free way to work through these workshops yourself without having to worry about setup.

To use this image yourself (including the AMI image #)… (Lori/Sean)

To offer this image to numerous students in a workshop… (Lori/Sean)

1.2.3 Make your own Docker image and AMI

If you want to alter the image in some way, you can rebuild it using packer, a toolkit that enables for the AMI creation process. Clone the GitHub repo:


Navigate to its packer subdirectory, then you should be able to do packer build bioc_2018.json (after packer is installed and AWS keys sorted out) to create a new AMI based on the Bioc R 3.5.1 devel AMI. You can see the “provision” section for details of the build process (Rscripts, basically). Of course, the json file can be altered for customization, including pegging to specific BiocWorkflow tags, if interested.

  1. City University of New York, New York, NY

  2. Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD

  3. City University of New York and Roswell Park Comprehensive Cancer Center, NY

  4. Roswell Park Comprehensive Cancer Center, Buffalo, NY

  5. Roswell Park Comprehensive Cancer Center, Buffalo, NY