An
Information Criterion for Order-restricted Inference and Its
Application to Clustering Short Time-course Microarray Data
Nan Lin
Department of Mathematics
Washington University in St. Louis
Friday, November 6, 2009, 12:30–1:30 pm
GEMS classroom, 3rd Floor in
Shriner's Building
Coffee, tea, and cookies will be provided
Abstract
Techniques of statistical inference under order restrictions have
been successfully used in many areas in statistics. We consider the problem
of selecting an order restriction profile out of a set of candidate
profiles. Existing information criteria can only be used with simple
ordering, such as monotone increasing or decreasing profiles. We developed a
new information criterion for order-resticted inference applicable to
general order restrictions, and showed its consistency in selecting the
correct order restriction profile. This new information criterion is also
successfully applied to clustering short time-course microarray data. We
first define candiate gene clusters using the order restriction profile over
the mean expression across time. A gene is then assigned to the best matched
profile/cluster determined by our new information criterion. Compared to
other existing clustering algorithms for short time-course microarray data,
the information criterion-based clustering provides competitive clustering
accuracy and is computationally more efficient.