Some Issues in Clustering Analysis with an Application to Microarray Gene Expression Data

• Activity Number: 410
• Type: Contributed
• Date/Time: Thursday, August 15, 2002 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section*
• Author(s): Samir Lababidi*+ and Fuad Gwadry and William Reinhold and Uwe Scherf and Kim Bussey and John Weinstein
• Affiliation(s): National Institute of Health and Gene Logic Inc. and National Institute of Health and Gene Logic Inc. and National Institute of Health and National Institute of Health
• Keywords: microarray ; cluster analysis ; gene expression ; omics ; genomics

Abstract

Microarray-based genomics and other high-throughput experimental approaches are becoming increasingly important in biology and chemistry. In this type of omic research (Weinstein, Science 282:627, 1998), one often generates and analyzes large databases without knowing in certainty what the most important molecular questions will turn out to be. Consequently, we need to develop and improve our ability to analyze the large amounts of quantitative measurements that these approaches produce. We will discuss the issues of normalization and transformation of the data as they affect the clustering results across different algorithms and measures of similarity, including the problem of assessing the reliability of clustering classes. As an application we will consider microarray gene expression data on the panel of 60 human cancer cell lines (NCI-60) used by the drug discovery program at the National Cancer Institute. We have assessed expression levels in those cells for thousands of genes using cDNA microarrays (Ross, et al., Nature Genetics 24:227, 2000; Scherf, et al., Nature Genetics 24:236, 2000) and Affymetrix Oligonucleotide arrays (Staunton, et al., PNAS 98:10787, 2001)