Experimental design for gene expression microarrays

TLDR

Microarray experiments have multiple sources of variation, and experimental plans should ensure that effects of interest are not confounded with ancillary effects. The study examines experimental design issues arising with gene expression microarray technology. The authors link microarray designs to classical block design, using ANOVA models and A‑optimality principles to formulate general recommendations for microarray experimental design. They show that a commonly used design violates this principle and is inefficient, and illustrate their recommendations with detailed examples and a computer‑search‑derived set of good designs for a specific objective.

Abstract

We examine experimental design issues arising with gene expression microarray technology. Microarray experiments have multiple sources of variation, and experimental plans should ensure that effects of interest are not confounded with ancillary effects. A commonly used design is shown to violate this principle and to be generally inefficient. We explore the connection between microarray designs and classical block design and use a family of ANOVA models as a guide to choosing a design. We combine principles of good design and A‐optimality to give a general set of recommendations for design with microarrays. These recommendations are illustrated in detail for one kind of experimental objective, where we also give the results of a computer search for good designs.