Department of Biostatistics Seminar/Workshop Series

Statistical models for structured high-throughput data

Saunak Sen PhD, Professor and Chief of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN

Biomedical sciences have been revolutionized by high-throughput data collection. Examples include gene expression measured using microarrays, proteomic data, and high throughput genetic screens. We will outline statistical models for taking advantage of known relationships such data, focusing on high throughput genetic screens.

In such screens, one measures the fitness of a library of genetic mutants in a variety of growth conditions. Mutants might be grouped by gene or gene family; growth conditions might be grouped by antibiotic class or temperature. Our proposed models provide a simple framework for encoding such known relationships (e.g. growth condition class and gene family of mutant) to enhance detection of associations that might otherwise be masked. We show that fast estimation algorithms can be developed by taking advantage of the structure of those models. Our methodís performance in simulations and on an E. coli chemical genetic screen will be presented.
Topic revision: r1 - 20 Feb 2017, AshleeBartley
 

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