Workshop Description
This workshop will detail how to annotate inter-sample epigenetic variation with publicly available gene regulatory information, for example, associating epigenetic variation with transcription factor-binding regions. We will spend a few minutes at the beginning introducing the goals of the package and important concepts. Then, we will go through several simplified example applications from the COCOA vignette. We will cover both unsupervised and supervised analyses. Finally, we will finish with some best practices for covariation-based analyses.
Pre-requisites
- Basic knowledge of R syntax
- Familiarity with the GenomicRanges class
- Basic knowledge of epigenetic data types including DNA methylation, chromatin accessibility, and ChIP-seq
Background Reading
- Taking a look at the workshop vignette ahead of time will help familiarize you with the concepts and methods used in COCOA
Workshop and Docker links
Workshop Participation
We will work through the COCOA vignette. The vignette and results will be displayed for participants but they may also go through the vignette on their computers.
R / Bioconductor packages used
COCOA GenomicRanges
Time outline
| Introduction to COCOA |
10m |
| Annotating DNA methylation variation |
20m |
| Annotating chromatin accessibility variation |
15m |
| Best practices |
5m |
Workshop goals and objectives
Learning goals
- understand how covariation can be used effectively for epigenetic analysis
- identify and annotate genomic regions where there is inter-sample epigenetic variation
- understand how to apply COCOA in unsupervised vs supervised contexts
- visualize inter-sample epigenetic variation
Learning objectives
- use methods including PCA to quantify epigenetic variation
- aggregate information from related regions across the genome to create a single region set score
- identify region sets that display DNA methylation and chromatin accessibility variation in breast cancer
- create region set score null distributions using permutations
- create “meta-region” plots to visualize epigenetic variation in regions of interest compared to the surrounding genome
Using the workshop docker image
- Run
docker run -e PASSWORD=yourpassword -p 8787:8787 -d —rm databio/cocoa_workshop_bioc2020. Use -v $(pwd):/home/rstudio argument to map your local directory to the container.
- Log in to RStudio at http://localhost:8787 using username
rstudio and password yourpassword. Note that on Windows you need to provide your localhost IP address like http://191.163.92.108:8787/ - find it using docker-machine ip default in Docker’s terminal.
- Run
browseVignettes(package = "COCOA.workshop.BIOC2020"). Click on one of the links, “HTML”, “source”, “R code”.