title: Using ANOVA to analyze microarray data.
abstract: ANOVA provides a general approach to the analysis of single and multiple factor experiments on both one- and two-color microarray platforms. Mixed model ANOVA is important because in many microarray experiments there are multiple sources of variation that must be taken into consideration when constructing tests for differential expression of a gene. The genome is large, and the signals of expression change can be small, so we must rely on rigorous statistical methods to distinguish signal from noise. We apply statistical tests to ensure that we are not just making up stories based on seeing patterns where there may be none.
authors.affiliation: The Jackson Laboratory, Bar Harbor, ME, USA.
authors.forename: Gary A
authors.initials: GA
authors.lastname: Churchill
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D014644:Genetic Variation; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size; D012984:Software