We introduce a robust method for detecting evolutionary trends of gene expression from a temporal sequence of microarray data. In this method we perform gene clustering via multi-objective optimization to reveal genes with interesting and statistically significant temporal patterns. We illustrate this gene filtering methodology in the context of exploring the time trajectories of mouse retinal genes acquired at different points over the lifetimes of a population of mice. For 6 time points sampled over 24 mouse subjects, our method can reliably reveal genes whose expression level increases or decreases monotonically, hits a peak or valley at birth, or exhibits other trends.
|Number of pages||4|
|Journal||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Publication status||Published - 2002|
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering