Analyzing factorial designed microarray experiments

AbstractHigh-throughput quantification of gene expression using microarray technology has dramatically changed biological investigation into the roles of genes in normal cell functioning, as well as the mechanisms of disease. We discuss an analytic approach for framing biological questions in terms of statistical parameters to efficiently and confidently answer questions of interest using microarray data from factorial designed experiments. Investigators can extract pertinent and interpretable information from the data about the effects of the factors, their interactions with each other, and the statistical significance of these effects, rather than rely solely on clustering techniques or fold change point estimates in hopes of finding co-expressed genes. By first examining how biological mechanisms are reflected in mRNA transcript abundance, investigators can better design microarray experiments to answer the most interesting questions