Abstract #301789

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JSM 2003 Abstract #301789
Activity Number: 95
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301789
Title: Using the t-distribution to Simulate Microarray Data
Author(s): Johanna S. Hardin*+
Companies: Pomona College
Address: Dept. of Math, Claremont, CA, 91711-4411,
Keywords: t-distribution ; microarray ; simulation ; robust
Abstract:

One of the difficulties in analyzing microarray data is in knowing what the underlying distribution is. Many newly created methods of analysis have been "checked" using simulated data from a normal distribution (Yeung et al., Bitner et al.) . We know how to transform the data to deal with symmetry and nonconstant variance across different expression levels, but the resulting transformed data have much heavier tails than a normal distribution (Durbin et. Al., Kerr and Churchill). Typically, a t-distribution has been used as a model to provide robustness for heavy tailed distributions, but this model addresses only the heavy tails and not additional outlying points. In microarray data we often also have outlying points that can influence ordinary estimates of shape and location. We provide a method of fitting a t-distribution to a dataset using a robust estimate of scale. We propose that this type of robust fit will be much more useful in microarray data analysis.

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