Intro & practicals

Learning goals

Introduction to R/Bioconductor, a popular open-source environment for life science informatics, with a focus on microbiome analysis

After the course, you will know how to:

• organize your data into a coherent data science framework

• implement reproducible data science workflows

• approach common analysis tasks with available documentation and R tools

Moreno-Indias et al. (2021) Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Frontiers in Microbiology.

Target audience

• Advanced MSc, PhD & postdoctoral researchers who wish to learn new skills in scientific programming

• Earlier experience with R is expected

Welcome!

Code of Conduct

Bioconductor community values an open approach to science that promotes

• sharing of ideas, code, software and expertise

• collaboration

• diversity and inclusivity

• a kind and welcoming environment

• community contributions

By participating in this community, you agree not to engage in behavior contrary to these values at any Bioconductor-sponsored event or electronic communication channel.

For the full CoC, see: https://bioconductor.github.io/bioc_coc_multilingual

Schedule

• Day 1: data science framework

• Day 2: data analysis & visualization

• Day 3: break from hands-on

• Day 4-5: advanced topics

Session types

• Short lectures & demonstrations don’t hesitate to ask questions!

• Practicals Solve tasks by taking advantage of the online examples and resources that are pointed out in the material. There is often more than one way to solve a given task.

• Additional exercises & example data sets, supporting online material, many ways to solve a given task

• Presentations, present your solutions, highlight questions and challenges

Teaching material

• Teaching follows the open online book (beta version) created by the course teachers, Orchestrating Microbiome Analysis.

• The openly licensed teaching material, exercises and slides will be available online during and after the course.

Support

• Post-it stickers

• Online chat (Gitter) https://gitter.im/microbiome/miaverse

• Google Doc Questions at the end of the gdoc are welcome

• Feedback at the end of each day using minute cards

Other points

• Basic vs. advanced groups
• Working with your own data?

If you need a small break, take it!

Acknowledgments

Development work has received support from several sources.

Learning goals for today

Broader context and practical skills on:

• Data science workflow: setting up reproducible data science workflows with Quarto

• Data containers: understanding SummarizedExperiment

• Basic data wrangling (e.g. subsetting, aggregation)

Questions?

Moreno-Indias et al. (2021) Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Frontiers in Microbiology.