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Activity Number:
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135
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2005 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #304774 |
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Title:
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Data Integration Method for High-throughput Data
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Author(s):
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Joseph Beyene*+ and Pingzhao Hu and Celia C. M. Greenwood
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Companies:
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University of Toronto and University of Toronto, Hospital for Sick Children and University of Toronto, Hospital for Sick Children
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Address:
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Hospital for Sick Children/Population Health Sciences, Toronto, ON, M5G 1X8, Canada
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Keywords:
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Data integration ; meta-analysis ; quality score ; microarrays
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Abstract:
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The utility of data integration and synthesis in scientific inference has been recognized in many scientific disciplines, including the medical, biological, and social sciences. For example, results from metaanalysis and systematic reviews are being used increasingly in important clinical and health care policy decisions. A more recent phenomenon is the unprecedented explosion in the amount of data being generated through a variety of high-throughput technologies such as microarray experiments. These technologies undergo constant change, leaving behind a trail of data with different forms, shapes, and sizes. Statistical and computational methodologies are therefore critical for extracting the most out of these related but not identical sources of data. We have extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation. We illustrate our approach with publicly available microarray datasets.
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