An introductory course to transcriptomic data analysis with R.
This course is aimed at basic and clinical researchers, the course will balance theory and hands-on. We will introduce R programming language and Bioconductor packages. We will provide an overview of methods to process, quality control and analyse microarray and RNA sequencing data to identify differentially expressed genes. We will explain how to retrieve published data from major open-access repositories (NCBI, TCGA). We will also introduce participants to Linux environment, statistical methods and R packages for basic machine learning. Finally, we will introduce concepts of pathway analysis and demonstrate means of estimating enrichment of pathways in different experimental conditions using open-access databases. All codes and material from the course will be available to participants online after the course.
This course is part of a series organized by the Bioinformatics Hub Training Platform; Associate Professor Francesca Buffa (Head), Dr Dimitris Voukantsis (Instructor), Dr Naveen Prasaad (Instructor) and Dr Sanjay Rathee (Instructor).
Monday 09 March, 9am-5pm R/RStudio, Visualization, Statistics
Tuesday 10 March, 9am-5pm Processing and analysis of microarray data
Wednesday 11 March, 9am-5pm Linux, Open Data Repositories
Thursday 12 March, 9am-5pm Processing and analysis of RNAseq data
Friday 13 March, 9am-5pm Pathway analysis, ‘Omic Hackathon
Room 71a, Old Road Campus Research Building, Oxford, OX3 7DQ
No previous computational experience required. 10 hours remote compulsory pre-course work will be assigned.
We expect that participants will be able to:
- Use the R environment to explore and visualize data
- Use R core statistics functions and basic Linux commands
- Have a good understanding of the processing microarray and RNA sequencing data, and the resources required
- Identify specific pathways that are enriched in a specific condition or dataset to aid biological interpretation
- Use their own, or publicly available microarray and RNA sequencing expression data to identify and visualize differentially expressed genes
First come first served basis. Cost is £780 per academic participants from the Department of Oncology, Oxford, and £868 for other academic participants. A limited number of bursaries available. Applications from non-academics will be considered upon request. Registrations are processed through the Oxford University online shop
Registration open until Friday 21 February 2020.
All enquiries to email@example.com