title: Identifying differentially expressed genes in the absence of replication.
abstract: In microarray data analysis, the comparison of gene expression levels in different conditions and selection of biologically relevant genes are essential tasks. In this study, we propose a novel statistical procedure based on standardised conditional residuals from a linear mixed-effects model which allows comparison of conditions, even if only one replicate per experimental condition is available. We illustrate this method by using three publicly available datasets. We show that this method can be extended to handle more complex designs. Finally, simulations show that the tests developed have good statistical power to detect true differences among conditions at the gene level.
authors.affiliation: Interuniversity Institute for Biostatistics and statistical Bioinformatics, Katholieke Universiteit Leuven, Kapucijnenvoer 35 Blok D, bus 7001, B3000 Leuven, Belgium. pushpike@med.kuleuven.be
authors.forename: Pushpike J
authors.initials: PJ
authors.lastname: Thilakarathne
authors.forename: Geert
authors.initials: G
authors.lastname: Verbeke
authors.forename: Kristof
authors.initials: K
authors.lastname: Engelen
authors.forename: Kathleen
authors.initials: K
authors.lastname: Marchal
authors.forename: Dan
authors.initials: D
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*