Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor (https://bioconductor.org) is a flexible, widely used, and respected collection of R packages for the statistical analysis and comprehension of many common types of high-throughput genomic data. Learning to use Bioconductor's core infrastructure, domain-specific analysis packages, and annotation resources can pose significant challenges, both to those embarking on their first significant bioinformatic analysis and to those encountering the Bioconductor ecosystem after developing considerable skill in other programming paradigms. This workshop introduces strategies for effectively training users new to Bioconductor. Topics covered include the following:
- Assessing abilities: where do we begin?
- Connecting with new users and refocusing experienced analysts
- The central importance of reproducible research
- How Bioconductor departs from common idioms in R and other languages
- Practical activities with meaningful feedback
- Representing informatic data: BED files and GenomicRanges
- Managing cognitive load
- Single cell RNA-seq differential expression workflows: how 'looking at the data' motivates subtle statistical analysis
- Matching student expectations with learning objectives
- Focused topics with broad appeal
- Annotation resources for effective communication
- Effective instruction
- Markdown documents and jupyter notebooks for guided analysis
- Live coding for engaging exploration
- What have we learned?