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  \textbf{\textsc{STANFORD UNIVERSITY}}\\[5pt]
  \textbf{\textsc{DEPARTMENT OF STATISTICS}}\\[5pt]
  \Large{\textbf\textsc{{DEPARTMENTAL SEMINAR}}}
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  4:15 p.m., Tuesday, October 31, 2006\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Kim-Anh Do} \\
Department of Biostatistics, U.T. M.D. Anderson Cancer Center\\
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  \textbf{ Bayesian Mixture Models for Complex High-Dimensional Count Data in Phage Display Experiments}
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\noindent Phage display is a biological process used to screen random peptide libraries for ligands that bind to a target of interest with high affinity. Based on a count data set from an innovative multi-stage phage display experiment, we propose a class of Bayesian mixture 
models to cluster peptides counts into three groups that exhibit different display patterns across stages. Among the three groups, the investigators are particularly interested in the one with an
  ascending display pattern in their counts, which implies that the peptides are likely to bind to the target with strong affinity. We apply a Bayesian false discovery rate approach to identify the peptides with the strongest affinity within the group. A list of peptides is obtained, among which important ones with meaningful functions are further validated by biologists. To examine the performance of the Bayesian model, we conduct a simulation study and desirable results are obtained.

\noindent This is joint work with Yuan Ji, Guosheng Yin, Kam-Wah Tsui, Mikhail Kolonin, Jessica Sun, Wadih Arap, and Renata Pasqualini.

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