Wednesday October 11th, 2006
CSE Room 305
12:00 - 1:00 PM
|
Markers
improve the clustering of CGH data |
|
Jun Liu |
|
Motivation:
We consider the problem of clustering a population of Comparative Genomic
Hybridization (CGH) data samples using similarity base clustering methods. A
key requirement for clustering is to avoid using the noisy aberrations in the
CGH samples. Results:
We develop a dynamic programming algorithm to identify a small set of
important genomic intervals called markers..
The advantage of using these markers is that the potentially noisy
genomic intervals are excluded during the clustering process. We also develop
two clustering strategies using these markers. The first one, model-based
approach, maximizes the support for the markers. The second one,
distance-based approach, develops a new similarity measure called RSim and iteratively refines clusters with the aim of maximizing
the RSim measure between the samples in the same cluster.
Our results demonstrate that the markers we found represent the aberration
patterns of cancer types well and they improve the quality of clustering
significantly. |
For
upcoming talks, visit http://www.cise.ufl.edu/dbcenter/seminar.shtml