Linear Models for Microarray Data Analysis: Hidden Similarities and Differences

M. Kathleen Kerr, University of Washington


In the past several years many linear models have been proposed for analyzing spotted microarray data. As presented in the literature, many of these models appear dramatically different. However, many of these models are reformulations of the same basic approach to analyzing microarray data. This paper demonstrates the equivalence of some of these models. Attention is directed at choices in microarray data analysis that have a larger impact on the results than the choice of linear model.