**IMA Complex Systems Seminar**

**3:30 Wednesday, October 8,
2003**

Randomization
test for array-based comparative genomic hybridization

Departments of Statistics and Biostatistics and Medical Informatics

University of Wisconsin – Madison

Madison, WI 53706

After low-level preprocessing, data from an array-based
comparative genomic hybridization (aCGH) experiment may be viewed as a set of
independent and identically distributed (iid) copies of the random vector X =
(X_1, X_2, ..., X_n) where X_i ~
Bernoulli( p_i ), but in which there is dependence among the components owing
to aspects of the tumor growth process that is being measured by X. Here i denotes a position in the genome and
X_i indicates whether or not genomic damage occurs at position i in the sampled
tumor. One statistical problem is to
test the null hypotheses H_0: p_i = p
for all i; that is, there are no aberration hot-spots in the genome. I will describe several attempts to
implement such a test using conditional inference and three permutation
methods. I conjecture that an
elementary shuffling procedure provides a conservative hypothesis test. The
approach is tested on a set of 60 aCGH profiles from cancer cell lines. Issues with
hidden-Markov model-based inferences will also be discussed.