JSM 2005 - Toronto

Abstract #303851

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 74
Type: Contributed
Date/Time: Sunday, August 7, 2005 : 8:00 PM to 9:50 PM
Sponsor: Biometrics Section
Abstract - #303851
Title: The Application of Block Wavelet Shrinkage Principal Components Model on DNA Microarray Data
Author(s): Jack Lee*+ and Benny C. Zee
Companies: Chinese University of Hong Kong and Chinese University of Hong Kong
Address: 4th Floor Cancer Centre, Shatin, N/A, Hong Kong
Keywords: principal components analysis ; wavelet shrinkage ; microarray data ; clustering ; cancer
Abstract:

The advantage of using DNA microarray data when investigating human cancer gene expressions is its ability to generate enormous amounts of information from a single assay in order to speed up the scientific evaluation process. The number of variables from the gene expression data coupled with comparably much fewer samples creates new challenges to scientists and statisticians. In particular, the problems include enormous degrees of collinearity among genes expressions, likely violation of model assumptions and a high level of noise with potential outliers. To deal with these problems, we proposed a block wavelet shrinkage principal component analysis method. The National Cancer Institute database (NC160---21 human cancer cell lines with 1,375 genes for each cell line from cancers of colorectal, renal, and leukaemia) was used as an illustration. The results showed a significant improvement in dimension reduction and the proposed method was extremely useful for data denoising. We compared the accuracy and efficiency of cancer types classification to that of the BPCA method as proposed by Liu et al., and have shown that we may reduce the number of variables from 1,375 to 138.

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Revised March 2005