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Activity Number: 523
Type: Invited
Date/Time: Thursday, August 2, 2007 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #307991
Title: Iterative Robust Sliced Inverse Regression for Gene Clustering in Microarray Gene Expression Data Analysis
Author(s): Han-Ming Wu*+
Companies: Academia Sinica
Address: Institute of Statistical Science, Taipei, 11529 , Taiwan
Keywords: Dimension reduction ; K-means clustering ; unsupervised learning
Abstract:

Clustering techniques such as hierarchical clustering, K-means are useful in revealing biologically relevant groups of genes in microarray data analysis. However, due to the noisy nature of microarray data, these methods are sensitive to outliers. In this study, an iterative robust version of sliced inverse regression (SIR) (Li, 1991) is proposed. Firstly, the partitioning around medoids (PAM) is used to generate the initial cluster labels. Then the robust version of SIR is combined with the nearest medoids classifier (NMC) to iteratively update the cluster labels for genes. Simulation results and real microarray data analyses show that the proposed method is capable of identifying more coherent expression patterns and providing more stable clusters than the K-means method.

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Revised September, 2007