title: A note on joint versus gene-specific mixed model analysis of microarray gene expression data.
abstract: Currently, linear mixed model analyses of expression microarray experiments are performed either in a gene-specific or global mode. The joint analysis provides more flexibility in terms of how parameters are fitted and estimated and tends to be more powerful than the gene-specific analysis. Here we show how to implement the gene-specific linear mixed model analysis as an exact algorithm for the joint linear mixed model analysis. The gene-specific algorithm is exact, when the mixed model equations can be partitioned into unrelated components: One for all global fixed and random effects and the others for the gene-specific fixed and random effects for each gene separately. This unrelatedness holds under three conditions: (1) any gene must have the same number of replicates or probes on all arrays, but these numbers can differ among genes; (2) the residual variance of the (transformed) expression data must be homogeneous or constant across genes (other variance components need not be homogeneous) and (3) the number of genes in the experiment is large. When these conditions are violated, the gene-specific algorithm is expected to be nearly exact.
authors.affiliation: Virginia Bioinformatics Institute and Department of Statistics, Virginia Tech, Blacksburg, VA 24061-0477, USA. inah@vt.edu
authors.forename: Ina
authors.initials: I
authors.lastname: Hoeschele
authors.forename: Hua
authors.initials: H
authors.lastname: Li
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D016014:Linear Models; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*