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Activity Number:
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305
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Risk Analysis
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| Abstract - #303846 |
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Title:
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Statistical Expression Deconvolution from Mixed Tissue Samples and Relevance to Biomarker Discovery
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Author(s):
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Jennifer L. Clarke*+ and Marc E. Lippman and J. Seo
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Companies:
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University of Miami and University of Miami and University of Miami
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Address:
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Department of Epidemiology and Public Health, Miami, FL, 33136,
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Keywords:
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breast cancer ; stem cells ; gene expression ; classification ; clustering ; patient treatment
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Abstract:
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Global expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. We propose a statistical approach to expression deconvolution from mixed tissue samples in which the proportion of each component cell type is unknown. Our method estimates the proportion of each component in a mixed tissue sample; this estimate can be used to provide estimates of gene expression from each component. We demonstrate our technique on xenograft samples from breast cancer research and publicly available experimental data sets found in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO).
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