Abstract
Advances in microtechnologies are making it possible for high-throughput control and reporting of gene expression in live cells, in real-time. We explore relevant statistical challenges to modeling and inference in real-time gene expression data from single-shock experiments, with special attention on potential confounding between treatment and cell cycle variation. We propose a semi-wavelet non-linear dynamic regression model to infer modulation in gene expression due to treatment shocks in the presence of cell cycle variation. A case study is performed with public data. Results are compared ignoring cell cycle. Estimation and inference are performed by a Bayesian approach.
Original language | English (US) |
---|---|
Pages (from-to) | 611-623 |
Number of pages | 13 |
Journal | Journal of Computational Biology |
Volume | 15 |
Issue number | 6 |
DOIs | |
State | Published - Jul 1 2008 |
Externally published | Yes |
Keywords
- Cancer genomics
- DNA arrays
- Gene expression
- Statistics
ASJC Scopus subject areas
- Modeling and Simulation
- Molecular Biology
- Genetics
- Computational Mathematics
- Computational Theory and Mathematics