Seminar on Computational Learning and
Adaptation
Plaid Models
Art Owen
Department of Statistics
Stanford University
www-stat.stanford.edu/~owen/
This talk describes the plaid model, a tool for exploratory
analysis of multivariate data.
The motivating application is the search for
interpretable biological structure in gene expression microarray data.
Interpretable structure can mean that a set of genes
has a similar expression pattern, in the samples under
study, or in just a subset of them (such as the cancerous samples).
A set of genes behaving similarly in a set of samples,
defines what we call a "layer". These are very much like
clusters, except that: genes can belong to more than one
layer or to none of them, the layer may be defined with respect
to only a subset of the samples, and the role of genes and samples
is symmetric in our formulation.
The plaid model is a superposition of two way anova models,
each defined over subsets of genes and samples.
We will present the plaid model, an interior point style
algorithm for fitting it, and some examples from yeast DNA
arrays and other problems.
This is joint work with Laura Lazzeroni of Stanford University.
Date: Thurs., Oct 19
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Time: 4:15-5:30PM
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Place: Cordura 100
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