MAID : An effect size based model for microarray data integration across laboratories and platforms

Background: Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions. Results: Traditional effect size models can not be used to integrate array data that directly compare treatment to control samples expressed as log ratios of gene expressions. Here we extend the traditional effect size model to integrate as many array datasets as possible. The extended effect size model (MAID) can integrate any array datatype generated with either single or two channel arrays using either direct or indirect designs across different la...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Ivan Borozan, Limin Chen, Bryan Paeper, Jenny E. Heathcote, Aled M. Edwards, Michael Katze, Zhaolei Zhang, Ian D. McGilvray
• Keywords: BMCBI 2008, Effect Size Model, Gene Expression, Traditional Effect Size