MOTIVATION: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. RESULTS: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.
MOTIVATION: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. RESULTS: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.
Authors: Yuliang Wang; Diana G Eng; Natalya V Kaverina; Carol J Loretz; Abbal Koirala; Shreeram Akilesh; Jeffrey W Pippin; Stuart J Shankland Journal: Kidney Int Date: 2020-06-25 Impact factor: 10.612
Authors: Sebastian Brähler; Bernd H Zinselmeyer; Saravanan Raju; Maximilian Nitschke; Hani Suleiman; Brian T Saunders; Michael W Johnson; Alexander M C Böhner; Susanne F Viehmann; Derek J Theisen; Nicole M Kretzer; Carlos G Briseño; Konstantin Zaitsev; Olga Ornatsky; Qing Chang; Javier A Carrero; Jeffrey B Kopp; Maxim N Artyomov; Christian Kurts; Kenneth M Murphy; Jeffrey H Miner; Andrey S Shaw Journal: J Am Soc Nephrol Date: 2017-12-07 Impact factor: 10.121
Authors: Vladimir Zhurov; Marie Navarro; Kristie A Bruinsma; Vicent Arbona; M Estrella Santamaria; Marc Cazaux; Nicky Wybouw; Edward J Osborne; Cherise Ens; Cristina Rioja; Vanessa Vermeirssen; Ignacio Rubio-Somoza; Priti Krishna; Isabel Diaz; Markus Schmid; Aurelio Gómez-Cadenas; Yves Van de Peer; Miodrag Grbic; Richard M Clark; Thomas Van Leeuwen; Vojislava Grbic Journal: Plant Physiol Date: 2013-11-27 Impact factor: 8.340
Authors: Gregory S Downs; Yong-Mei Bi; Joseph Colasanti; Wenqing Wu; Xi Chen; Tong Zhu; Steven J Rothstein; Lewis N Lukens Journal: Plant Physiol Date: 2013-02-06 Impact factor: 8.340