title: Statistical challenges with gene expression studies.
abstract: Studies that include high-throughput data, such as gene expression data, raise unique issues with respect to study design and analysis. At the same time, they should be viewed through the lens (albeit a modified one) of standard scientific approach that involves such issues as specifying objectives (even if the study is mainly hypothesis generating or exploratory), a careful consideration of design, including sample size and replication, deciding whether to include technical replication in addition to biological replication, and ensuring that the methods of analysis are appropriate for the objective.
authors.affiliation: Duke University, Department of Biostatistics and Bioinformatics, 2424 Erwin Road, Hock Plaza, Suite 802, Durham, NC 27705 USA. shoem003@mc.duke.edu
authors.forename: Jennifer
authors.initials: J
authors.lastname: Shoemaker
mesh_terms: D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D011506:Proteins / Q000235:genetics; D012333:RNA, Messenger / Q000096:biosynthesis / Q000235:genetics
chemical_list: D011506:Proteins; D012333:RNA, Messenger