Department of Biostatistics Seminar/Workshop Series

Statistical Methods for the Analysis of Biological Pathways

Lily Wang, PhD

Assistant Professor, Department of Biostatistics

Wednesday, October 1, 1:45-2:55pm, MRBIII Conference Room 1220

Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users

Gene class, ontology, or pathway testing analysis has become increasingly popular in microarray data analysis. Such approaches allow the integration of gene annotation databases such as Gene Ontology and KEGG Pathway to formally test for subtle but coordinated changes at a system level. Higher power in gene class testing is gained by combining weak signals from a number of individual genes in each pathway.

In this talk, I will provide an overview of statistical issues involved in pathway analysis. In addition to discussing the popular analysis approaches, I will also introduce a new method developed recently by our group using mixed models (Wang et al. PLoS Genetics 2008 4(7): e1000115), a class of statistical models that (a) provides the ability to model and borrow strength across genes that are both up and down in a pathway; (b) operates within a well-established statistical framework amenable to direct control of false positive or false discovery rates, (c) exhibits improved power over widely used methods under normal location-based alternative hypotheses, and (d) handles complex experimental designs for which permutation resampling is difficult.
Topic revision: r2 - 26 Apr 2013, JohnBock

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