Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels an

Background: The detection of small yet statistically significant differences in gene expression in spotted DNA microarray studies is an ongoing challenge. Meeting this challenge requires careful examination of the performance of a range of statistical models, as well as empirical examination of the effect of replication on the power to resolve these differences. Results: New models are derived and software is developed for the analysis of microarray ratio data. These models incorporate multiplicative small error terms, and error standard deviations that are proportional to expression level. The fastest and most powerful method incorporates additive small error terms and error standard deviations proportional to expression level. Data from four studies are profiled for the degree to which they reveal statistically significant differences in gene expression. The gene expression level at which there is an empirical 50% probability of a significant call is presented as a summary statistic...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Jeffrey P. Townsend
• Keywords: BMCBI 2004, Gene Expression, Gene Expression Levels, Small Error Terms