Abstract
Time course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such data are thought to reflect underlying biological processes developing over time. In this article, we propose a model that allows us to examine differential expression and gene network relationships using time course microarray data. We model each gene-expression profile as a random functional transformation of the scale, amplitude, and phase of a common curve. Inferences about the gene-specific amplitude parameters allow us to examine differential gene expression. Inferences about measures of functional similarity based on estimated time-transformation functions allow us to examine gene networks while accounting for features of the gene-expression profiles. We discuss applications to simulated data as well as to microarray data on prostate cancer progression.
Original language | English (US) |
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Pages (from-to) | 793-804 |
Number of pages | 12 |
Journal | Biometrics |
Volume | 65 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2009 |
Externally published | Yes |
Keywords
- Bayesian hierarchical model
- Differential expression
- Functional data
- Functional similarity
- Gene networks
- Time course microarray data
- Time transformation
ASJC Scopus subject areas
- Statistics and Probability
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- General Agricultural and Biological Sciences
- Applied Mathematics