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 - #309180
Title: Detection Call Algorithms for High-Throughput Gene Expression Microarray Data
Author(s): Sarah Reese*+ and Kellie Archer
Companies: Virginia Commonwealth University and Virginia Commonwealth University
Address: , Richmond, VA, ,
Keywords: gene expression ; microarray ; detection calls ; present

Extensive research has been conducted to improve gene expression summary methods. In addition to quantitative expression summaries, most platforms provide a qualitative detection call result for each gene. These detection call algorithms are intended to determine whether each transcript is reliably measured. In this research we review the detection call algorithms for two widely used gene expression microarray platforms. Both algorithms result in a P-value which is used to determine the qualitative detection calls. We examine the performance of these algorithms by applying the methods to two spike-in datasets and show that the default parameters yield few absent calls for high spike-in concentrations. When genes of interest are expected to be present at low concentrations, spike-in datasets can be useful for appropriately adjusting the tuning parameters for qualitative detection calls.

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