Abstract #301441

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JSM 2003 Abstract #301441
Activity Number: 288
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #301441
Title: A Statistical Model for Microarray Gene Expression Data
Author(s): Jie Chen*+ and Tuhao John Chen and Arjun K. Gupta
Companies: University of Missouri, Kansas City and Bowling Green State University and Bowling Green State University
Address: Mathematics and Statistics, Kansas City, MO, 64110-2481,
Keywords: skew normal distribution ; distorted normal distribution ; hematopoietic stem cells ; gene expression ; oligonucleotide microarray data
Abstract:

This talk discusses a new distribution family, distorted normal distribution, which is originated from the investigation on the rationale for the skewness of average differences in the Affymetrix gene expression data after normalization. We show that, on the basis of normalized perfect matched data and mismatched data, the population of currently available gene expression data follows the distorted normal distribution. This clarifies the existing question on the distribution of gene expression data when the intensities are truncated according to a threshold. The identification of such a distribution of gene expression data can critically affects the filter to correctly detect differentially expressed genes. Data and histograms of 24,818 oligonucleotide gene expression data (after normalization) on hematopoietic stem-cell studies are presented to illustrate the newly proposed distribution family.

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