Abstract #301340

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JSM 2003 Abstract #301340
Activity Number: 250
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #301340
Title: Statistical Inference for Cluster Analysis With Application to Microarray Gene Expression Data
Author(s): Samir Lababidi*+ and Uma Shankavaram and William C. Reinhold and Uwe Scherf and John N. Weinstein
Companies: National Institute of Health and National Institute of Health and National Institute of Health and Gene Logic Inc. and National Cancer Institute
Address: Bldg. 37 Rm 5041, Bethesda, MD, 20892,
Keywords: microarray ; cluster analysis ; resampling ; gene expression ; omics ; genomics
Abstract:

Microarray-based genomics and other high-throughput experimental approaches are increasingly becoming important in cancer research. In this type of omic research (Weinstein 1998) ,scientists measure the expression levels in cells of thousands of genes simultaneously. The NCI-60 is a panel of 60 human cancer cell lines used by the drug discovery program at the National Cancer Institute to screen >100,000 compounds for anticancer activity over the last 12 years. We have assessed expression levels in those cells for thousands of genes using cDNA microarrays (Ross et al. 2000, Scherf et al. 2000) and Affymetrix Oligonucleotide arrays (Staunton et al. 2001). Here, through statistical inference, we compare different types of transformations and similarity distances as they affect clustering results for the NCI-60 using different clustering algorithms for data from the two different types of arrays.

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