Summary
Objectives:
A variety of linear models have recently been proposed for the design and analysis of micro-array experiments. This article gives an introduction to the most common models and describes their respective characteristics.
Methods:
We focus on the application of linear models to logarithmized and normalized microarray data from two-color arrays. Linear models can be applied at different stages of evaluating microarray experiments, such as experimental design, background correction, normalization and hypothesis testing. Both one-stage and two-stage linear models including technical and possibly biological replicates are described. Issues related to selecting robust and efficient microarray designs are also discussed.
Results:
Linear models provide flexible and powerful tools, which are easily implemented and interpreted. The methods are illustrated with an experiment performed in our laboratory, which demonstrates the value of using linear models for the evaluation of current microarray experiments.
Conclusions:
Linear models provide a flexible approach to properly account for variability, both across and within genes. This allows the experimenter to adequately model the sources of variability, which are assumed to be of major influence on the final measurements. In addition, design considerations essential for any well-planned microarray experiments are best incorporated using linear models. Results from such experimental design investigations show that the widely used common reference design is often substantially less efficient than alternative designs and its use is therefore not recommended.
Keywords
ANOVA techniques - hypothesis testing - normalization - experimental design
