Microarray Data Analysis
Date: June 4-5, 2012
Location: Downtown Toronto, ON
Lead Faculty (2012): Paul Boutros
Registration Fee for Applications received before May 4, 2012: $500 + HST
Registration Fee for Applications received after May 4, 2012: $700 + HST
Awards available for 2012.
Target Audience
Microarrays remain the most popular, most affordable, and best-established technique for functional genomics. Tens of thousands of datasets have been generated to address diverse aspects of biology. This workshop provides an overview of the analysis of microarray data ranging in size from one to two samples to hundreds of samples.
This workshop is aimed at researchers who are working with mRNA expression microarray data and focuses on the popular Affymetrix platforms. However, many of the algorithms and techniques discussed will be applicable to non-mRNA and non-Affymetrix datasets. All practical analysis will employ the R statistical environment, with a combination of custom scripts and BioConductor libraries.
Prerequisite: Your own laptop computer with R and BioConductor installed. Minimum requirements: 1.6 GHz CPU, 2GB RAM, 10 GB free disk space, recent versions of Windows, Mac OS X or Linux. Most computers purchased in the past 3-4 years likely meet these requirements. If you do not have access to your own computer, you may loan one from the CBW. Please contact course_info@bioinformatics.ca for more information.
Course Objectives
The goal of this workshop is to provide procedures and analysis workflows for the analysis of microarray datasets. Discussions will include data-organization practices, data-annotation, normalization, pre-processing, and (briefly) downstream analysis. This workshop provides an ideal entry to the "Pathway and Network Analysis of –omics Data " workshop.
Course Outline
Day 1
Module 1: Introduction to microarrays and R (Chair: Paul Boutros)
- Ice-breaking session for participants (to promote networking)
- Overview of microarray technologies
- Outline of microarray analysis pipelines
- Quality-Control & Data Pre-Processing
Laboratory
- Basics of the R statistical environment
- Loading microarray data into R
- Data pre-processing approaches
Module 2: Quality Control of Microarrays
- Approaches for ProbeSet and gene annotation
- Two-sample statistical analyses
Laboratory
- Generating Quality-Control plots
- Using alternative CDFs
- Parametric analyses
- Permutation-based approaches
Day 2
Module 3: Statistical Analysis
- Data-organization and storage issues
- Two-factor experimental designs
- Continuous variables
- Survival analysis
Laboratory
- One-way ANOVA
- General linear models
- Correlation analyses
- Cox proportional hazards modeling
Module 4: Beyond the Microarray Experiment
- Reporting of microarray data
- Critical assessment of literature
- What comes next?
Laboratory
- Comparison of two microarray papers
- Open lab time to work on your own data or on a provided sample dataset
Pre-Readings
You are expected to have completed the following tutorials in R beforehand. The tutorial should be very accessible even if you have never used R before. Please look through sections 1-3 of the following tutorial: http://www.cyclismo.org/tutorial/R/
