Real-time gene expression: statistical challenges in design and inference.
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.
Gold
David
D
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, USA. dlgold@bu.edu
Mallick
Bani
B
Coombes
Kevin
K
D001185:Artificial Intelligence; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D049452:Green Fluorescent Proteins; D015233:Models, Statistical
D049452:Green Fluorescent Proteins