This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #309283
Title: Performance of Different Filtering Methods When Testing for Differential Expression in Microarray Data
Author(s): Soledad Fernandez*+ and Parul Gulati and David Jarjoura and Lianbo Yu and Michael Pennell
Companies: The Ohio State University and The Ohio State University and The Ohio State University and The Ohio State University and The Ohio State University
Address: 2012 Kenny Road, columbus, OH, 43210, USA
Keywords: microarray ; multiple testing ; differential expression ; filtering

Microarray data analysis involves a large number of hypothesis tests and hence requires multiple testing adjustments. This leads to low power when sample sizes are not large. Filtering reduces the number of tests and can lead to higher power. A large proportion of genes are unexpressed in tissue samples for microarray experiments, resulting in observed values that are the result of the random variation associated with processing chips rather than gene expression. Researchers commonly filter out genes with low mean signal or low variance. These approaches could be problematic with small sample sizes where gene-specific variance estimates are unreliable and mean signal is more sensitive to outliers. We propose a simple filtering procedure which is less sensitive to small sample sizes. The superior performance of the proposed procedure is demonstrated in a simulation study.

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