title: Statistical methods for analyzing microarray feature data with replications.
abstract: Expression levels in oligonucleotide microarray experiments depend on a potentially large number of factors, for example, treatment conditions, different probes, different arrays, and so on. To dissect the effects of these factors on expression levels, fixed-effects ANOVA methods have previously been proposed. Because we are not necessarily interested in estimating the specific effects of different probes and arrays, we propose to treat these as random effects. Then we only need to estimate their means and variances but not the effect of each of their levels; that is, we can work with a much reduced number of parameters and, consequently, higher precision for estimating expression levels. Thus, we developed a mixed-effects ANOVA model with some random and some fixed effects. It automatically accounts for local normalization between different arrays and for background correction. The method was applied to each of the 6,584 genes investigated in a microarray experiment on two mouse cell lines, PA6/S and PA6/8, where PA6/S enhances proliferation of Pre B cells in vitro but PA6/8 does not. To detect a set of differentially expressed genes (multiple testing problem), we applied the method of controlling the false discovery rate (FDR), which successfully identified 207 genes with significantly different expression levels.
authors.affiliation: Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA. yyang@linkage.rockefeller.edu
authors.forename: Yaning
authors.initials: Y
authors.lastname: Yang
authors.forename: Josephine
authors.initials: J
authors.lastname: Hoh
authors.forename: Clemens
authors.initials: C
authors.lastname: Broger
authors.forename: Martin
authors.initials: M
authors.lastname: Neeb
authors.forename: Joanne
authors.initials: J
authors.lastname: Edington
authors.forename: Klaus
authors.initials: K
authors.lastname: Lindpaintner
authors.forename: Jurg
authors.initials: J
authors.lastname: Ott
mesh_terms: D000465:Algorithms; D000818:Animals; D001853:Bone Marrow / Q000502:physiology*; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards; D051379:Mice; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D015203:Reproducibility of Results; D017154:Stromal Cells / Q000502:physiology*