title: Differential expression analysis for pathways.
abstract: Life science technologies generate a deluge of data that hold the keys to unlocking the secrets of important biological functions and disease mechanisms. We present DEAP, Differential Expression Analysis for Pathways, which capitalizes on information about biological pathways to identify important regulatory patterns from differential expression data. DEAP makes significant improvements over existing approaches by including information about pathway structure and discovering the most differentially expressed portion of the pathway. On simulated data, DEAP significantly outperformed traditional methods: with high differential expression, DEAP increased power by two orders of magnitude; with very low differential expression, DEAP doubled the power. DEAP performance was illustrated on two different gene and protein expression studies. DEAP discovered fourteen important pathways related to chronic obstructive pulmonary disease and interferon treatment that existing approaches omitted. On the interferon study, DEAP guided focus towards a four protein path within the 26 protein Notch signalling pathway.
authors.affiliation: Bioinformatics & High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington, USA.
authors.forename: Winston A
authors.initials: WA
authors.lastname: Haynes
authors.forename: Roger
authors.initials: R
authors.lastname: Higdon
authors.forename: Larissa
authors.initials: L
authors.lastname: Stanberry
authors.forename: Dwayne
authors.initials: D
authors.lastname: Collins
authors.forename: Eugene
authors.initials: E
authors.lastname: Kolker
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D004194:Disease / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D015203:Reproducibility of Results; D015398:Signal Transduction