Abstract
Time course gene expression experiments have proved valuable in a variety of studies. Their unique data structure and the diversity of tasks often associated with them present new challenges to statistical analysis. In this report, we give a brief review of several primary questions pertaining to such experiments and popular statistical tools to address them.
Keywords
- Hide Markov Model
- Unsupervised Cluster
- Temporal Expression Pattern
- Gaussian Process Modeling
- Lymphocytic Choriomeningitis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgements
This research was supported in part by NSF grants DMS-0706724 and DMS-0846234, NIH grant R01GM076274-01, and a grant from Georgia Cancer Coalition.
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Ruan, L., Yuan, M. (2011). Statistical Analysis of Time Course Microarray Data. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_14
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DOI: https://doi.org/10.1007/978-3-642-16345-6_14
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