Bioinformatics: Microarray Analysis with Bioconductor

This course introduces researchers to a multidisciplinary approach to microarray data analysis. Attention is devoted to the design of microarray experiments, data normalization and quality control as well as to statistical analysis. Further information is available here.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book by linking here.

• Our courses are open to all who might benefit
• Booking priority is given to people from Cambridge University and Collaborating Institutes

• Knowledge of what microarrays are and the basic principles of their application to gene expression studies
• Introductory training in R. Minimally completion of the R tutorial from computational biology group, Department of Oncology, Bioinformatics: Introduction to R or equivalent
• Only a basic understanding of statistics

Number of sessions: 3

• Lecture: Introduction to R and Bioconductor
• Lecture: Data pre-processing
• Lecture: Experimental Design
• Practical: First steps in R
• Practical: Introduction to limma
• Lecture: Linear Models

• Lecture: Statistics of differential expression
• Practical: Limma (differential expression)
• Lecture: Processing Illumina BeadChips
• Practical: Beadarray

• Lecture: Affymetrix arrays
• Practical: Affymetrix arrays
• Lecture: SNP and Copy Number Analysis
• Practical: SNP and Copy Number Analysis
• Lecture: Downstream Analysis
• Practical: Using GOstats to interpret Illumina data

• To provide an understanding of how to approach designing microarray experiments planned in the lab
• To provide a knowledge and understanding of microarray analysis and quality issues
• To encourage confidence in performing preprocessing, quality assessment, and differential expression and downstream analysis using the limma program and other R libraries in Bioconductor

Presentations and practicals

3

A number of times per year