https://github.com/Bioconductor/LearnBioconductor

Training material for introductory R / Bioconductor courses
https://github.com/Bioconductor/LearnBioconductor

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Training material for introductory R / Bioconductor courses

• Host: GitHub
• URL: https://github.com/Bioconductor/LearnBioconductor
• Owner: Bioconductor
• Created: 2014-09-09T12:03:39.000Z (over 10 years ago)
• Default Branch: devel
• Last Pushed: 2023-03-15T20:25:16.000Z (over 1 year ago)
• Last Synced: 2024-06-07T19:48:19.874Z (6 months ago)
• Language: R
• Homepage:
• Size: 3.38 MB
• Stars: 69
• Watchers: 29
• Forks: 33
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-bacteria - LearnBioconductor - Learning R / Bioconductor for Sequence Analysis (2015). (Educational resources / Bioconductor)

README

        
Learning R / Bioconductor for Sequence Analysis

===============================================

Fred Hutchinson Cancer Research Center, Seattle, WA


October 27-29

Location: October 27 / 28: Arnold Building M1-A303; October 29:

Thomas D1-080

Contact: Martin Morgan (content,

[[email protected]](mailto:[email protected])); Melissa Alvendia

(administration, [[email protected]](mailto:[email protected]))

This course is directed at beginning and intermediate users who would

like an introduction to the analysis and comprehension of

high-throughput sequence data using _R_ and

_[Bioconductor](http://bioconductor.org)_. Day 1 focuses on learning

essential background: an introduction to the _R_ programming language;

central concepts for effective use of _Bioconductor_ software; and an

overview of high-throughput sequence analysis work flows. Day 2

emphasizes use of _Bioconductor_ for specific tasks: an RNA-seq

differential expression work flow; exploratory, machine learning, and

other statistical tasks; gene set enrichment; and annotation.  Day 3

transitions to understanding effective approaches for managing larger

challenges: strategies for working with large data, writing re-usable

functions, developing reproducible reports and work flows, and

visualizing results.  The course combines lectures with extensive

hands-on practicals; students are required to bring a laptop with

wireless internet access and a modern version of the Chrome or Safari

web browser.

Schedule (tentative)

--------------------

Day 1: Learn _R_ / _Bioconductor_

- 9:00 - 10:30 Introduction to _R_: objects, functions, help!

- 11:00 - 12:30 Introduction to _Bioconductor_: working with packages and classes

- 1:30 - 5:00 (break: 3:00 - 3:30) Introduction to sequence analysis:

  typical work flow; data types and quality assessment; essential

  _Bioconductor_ packages

Day 2: Use _R_ / _Bioconductor_ 

- 9:00 - 12:30 (break (10:30 - 11:00) An RNA-seq differential

  expression work flow (detail)

- 1:30 - 2:00 Other work flows (survey): ChIP-seq, variants, copy

  number, epigenomics

- 2:00 - 3:00 Machine learning; exploratory and other statistical

  analysis

- 3:30 - 4:00 Annotating genes, genomes, and variants

- 4:00 - 5:00 Approaches to gene set enrichment

Day 3: Develop Skills and Best Practices

- 9:00 - 10:30 Working with large data

- 11:00 - 12:30 Organizing code in functions, files, and packages

- 1:30 - 3:00 Reproducible reports and work flows: markdown

- 3:30 - 4:30 Visualization

- 4:30 - 5:00 Summary