Home banner
A-Z Index

Quick way to the find the information that you need...

More button
Register with FRAME

Although you do not need to register, any information you provide will be confidential and used only by FRAME to improve the website

Register button
Account Login
Forgot password?

The Journal


Alternatives to Laboratory Animals - ATLA

Download latest issue button Download back issues button Subscribe to ATLA
Contact Us

Tel icon

Tel: +44 (0)115 9584740

Tel icon

Fax: +44 (0)115 9503570

Make an Enquiry

The design and analysis of microarray experiments: applications in parasitology.

Morrison, D.A. and Ellis, J.T.

DNA and Cell Biology, 22(6), 357-394 (2003).

Microarray experiments can generate enormous amounts of data, but large datasets are usually inherently complex, and the relevant information they contain can be difficult to extract. For the practicing biologist, we provide an overview of what we believe to be the most important issues that need to be addressed when dealing with microarray data. In a microarray experiment we are simply trying to identify which genes are the most "interesting" in terms of our experimental question, and these will usually be those that are either overexpressed or underexpressed (upregulated or downregulated) under the experimental conditions. Analysis of the data to rind these genes involves first preprocessing of the raw data for quality control, including filtering of the data (e.g., detection of outlying values) followed by standardization of the data (i.e., making the data uniformly comparable throughout the dataset). This is followed by the formal quantitative analysis of the data, which will involve either statistical hypothesis testing or multivariate pattern recognition. Statistical hypothesis testing is the usual approach to "class comparison," where several experimental groups are being directly compared. The best approach to this problem is to use analysis of variance, although issues related to multiple hypothesis testing and probability estimation still need to be evaluated. Pattern recognition can involve "class prediction," for which a range of supervised multivariate techniques are available, or "class discovery," for which an even broader range of unsupervised multivariate techniques have been developed. Each technique has its own limitations, which need to be kept in mind when making a choice from among them. To put these ideas in context, we provide a detailed examination of two specific examples of the analysis of microarray data, both from parasitology, covering many of the most important points raised.