Using False Discovery Rates in DNA Microarrays

• Activity Number: 373
• Type: Topic Contributed
• Date/Time: Thursday, August 15, 2002 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing*
• Author(s): John Storey*+
• Affiliation(s): Stanford University
• Keywords: multiple comparisons ; p-values ; q-values ; simultaneous inference ; expression arrays

Abstract

DNA microarrays allow the simultaneous measurement of the expression levels of thousands of genes from a single sample of cells. A common experiment performed with this technology is to obtain a number of microarrays from two or more types of cells. A basic yet important question one can try to answer from these data is which genes show a statistically significant change in gene expression between the cell types. This task falls under the statistical heading of multiple hypothesis testing. One must: (1) form a statistic for each gene; (2) calculate the null distribution; (3) form a set of significance regions; and (4) assess the false positives in some fashion. We briefly review these four issues, but concentrate on the last one. We argue that false discovery rates are particularly appropriate for assessing false positives in these experiments. We present some current developments in false discovery rate methodology that are well-suited for these data.