CRUK: Analysis of publicly available microarray data Prerequisites
Although microarrays have been superseded by high-throughput sequencing technologies for gene expression profiling, years of experience gained from analysing microarray data has led to a variety of analysis techniques and datasets that can be exploited in other contexts. In this course, we will focus on retrieving and exploring microarray data from public repositories such as Gene Expression Omnibus (GEO).
This event is part of a series of training courses organized in collaboration with Dr. Mark Dunning at CRUK Cambridge Institute.
Please note that if you are not eligible for a University of Cambridge Raven account you will need to book by linking here.
- Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
- Further details regarding eligibility criteria are available here
- Further details regarding the charging policy are available here
- A very basic knowledge of UNIX would be an advantage, but nothing will be assumed and extremely little will be required
- Attendees should be comfortable with using the R statistical language to read and manipulate data, and produce simple graphs
Number of sessions: 2
| # | Date | Time | Venue | Trainers | |
|---|---|---|---|---|---|
| 1 | Mon 15 Feb 2016 09:30 - 17:30 | 09:30 - 17:30 | Room 215, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE | map | Oscar Rueda, Mark Dunning |
| 2 | Tue 16 Feb 2016 09:30 - 17:30 | 09:30 - 17:30 | Room 215, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE | map | Oscar Rueda, Mark Dunning |
After this course you should be able to:
- Import gene expression datasets from GEO into R
- Assess the quality of a dataset in a repository
- Identify, and correct for, batch effects
- Perform a standard DE analysis to get a ranked list of genes
- Use un-supervised methods to explore a dataset
- Interrogate particular genes of interest
During this course you will learn about:
- Exploratory data analysis techniques for high-throughput data
- Workflows for the analysis of Illumina and Affymetrix gene expression data
- Normalisation of gene expression data
- Differential expression (DE) analysis using linear-modelling techniques
- Importing data from GEO into R
- Principal Components Analysis and hierarchical clustering of gene expression data
Presentations and practicals
Two full day sessions
A number of times per year
Booking / availability