Fall Seminar Series - September 8, 2005
University of Minnesota
School of Statistics
College of Liberal Arts
An Algorithm in Choosing Significant PCA Components on Expression
Microarrays
I-Ping Tu
Institute of Statistical Science
Academia Sinica, Taiwan
Thursday, September 8, 2005
3:30 PM, 115
Ford Hall
Minneapolis, East Bank Campus
Social at 3:00 PM, 300 Ford Hall
Abstract
PCA
(Principal Component Analysis) is one of the oldest and best known
statistical tools for multivariate analysis. Even in earlier ages, PCA
has been applied fruitfully by physicists in solving the motions of
rigid bodies in classical mechanics. In the example of the motion of a
top, the first principal component (with the largest eigenvalue) is the
direction of the axis around which the top can spin stably. Adopting
this insight, we proposed an algorithm based on robustness properties
to choose statistically significant components of PCA. We will use a
Microarray data set to demonstrate this algorithm.