| | Statistics Seminar: ``Finding Significant Large-Average Submatrices in High Dimensional Data" by Andrew Nobel, UNC | |
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Speaker
| | Andrrew Nobel, UNC |
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| | Date | | Nov 5, 2009 |
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| | Time | | 4:00 pm
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| | Location | | (ROOM CHANGE) 122 Illini Hall |
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| | Sponsor | | Department of Statistics |
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| | E-Mail | | office@illinois.edu |
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| | Phone | | 3-2167 |
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| | Event type | | Seminar |
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| | Views | | 440 |
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| Abstract: Exploratory analysis of high dimensional data often begins with
independent clustering
of samples and variables, yielding a partition of the available data
matrix into disjoint
row-column 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 in the study of
gene expression data.
This talk describes 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 the underlying
structure of many problems),
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. I will discuss the applications of the algorithm to a
recent gene-expression based cancer study, and will provide a detailed
comparison
of its performance with several other biclustering method, including
its application
to semi-supervised classification. |
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