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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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  4:15 p.m., Tuesday, May 27, 2008\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Andrew B. Nobel} \\
  Department of Statistics and Operations Research\\
  University of North Carolina, Chapel Hill
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  \subsection*{  Finding Significant Large-Average Submatrices in High Dimensional Data  }
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\noindent

Exploratory analysis of gene expression and other high dimensional data often begins with
row and column clustering, which yields a partition of the data matrix into disjoint
sample-variable blocks (submatrices). Of particular interest in practice are submatrices
whose entries are large on average.  In conjunction with clinical and functional annotation,
large average submatrices are frequently the starting point for subsequent analyses,
such as the identification of genetic pathways and new disease subtypes.

We describe a simple algorithm, belonging to the general category of biclustering
methods, for identifying large average submatrices in high dimensional data. Like other
biclustering methods, the algorithm improves on independent sample variable clustering
in several respects: the submatrices it identifies can overlap and they need not cover the
entire data matrix (features that better reflect underlying biology), and the inclusion of
samples and variables in a submatrix does not depend on their expression values outside the
submatrix. The algorithm seeks to maximize a simple measure of statistical significance, which
also provides an objective basis for comparing and selecting among submatrices of different sizes
and average intensities. We will discuss the applications of the algorithm to a
recent gene-expression based cancer studies, and will compare its performance with several
other biclustering methods. 
The talk should be accessible to statisticians, computer scientists and computational biologists.

Joint work with Andrey Shabalin, Vic Weigman, and Charles Perou.

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