Background
Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. Bioconductor has two releases each year which corresponds to the released version of R, and an active user community.
The following tutorials can optionally be used within the RStudio platform and using makrdown.
Tutorials
TUTORIAL 1 – R / Bioconductor: from raw data to annotated gene list
(last updated: May 9, 2017)
In class short hand out (rendered ipython jupyter notebook)
- Bioconductor: installation instructions: HTML, PDF
- From CEL Files to Annotated Gene Lists: HTML, PDF (based on chapter 25 of book cited below)
Note: Tutorial is based on the last chapter of the book Bioinformatics and Computational Biology Solutions Using R and Bioconductor
TUTORIAL 2 – R / Bioconductor: analysis of a vitaminD public dataset
(last updated: May 9, 2017)
In class short hand out (rendered ipython jupyter notebook)
- Bioconductor installation instructions: (reminder, see link above.)
- GEO Database: GSE46268 vitamin D: HTML, PDF, DOCX
REFERENCES:
- Robert Gentleman, Vince Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit EDS (2005) – “Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)” Springer, ISBN-13: 978-0387251462 (book preview)
- Ramos, M, et al. (2017) “Software for the Integration of Multiomics Experiments in Bioconductor Cancer Research” 77:e39-e42. doi:10.1158⁄0008-5472.CAN-17-0344