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Activity Number:
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378
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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 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304046 |
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Title:
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Dimension Reduction in the Study of Genetics of Gene Expression
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Author(s):
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Qiang Guo*+ and Stephanie A. Santorico
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Companies:
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Oklahoma State University and University of Colorado
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Address:
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Department of Statistics, Stillwater, OK, 74078,
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Keywords:
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Dimension reduction ; Principal component analysis ; Partial least squares ; Non-negative matrix factorization ; Gene expression ; Linkage
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Abstract:
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Combining genome-scale gene expression profiling and marker information has become a powerful strategy for better understanding the genetic basis of complex traits. One of the challenges of such an approach is how to study the genetics of a vast set of highly interrelated measures which likely represent a smaller set of truly meaningful variables. In this paper three dimension reduction methods, principal component analysis, partial least squares, and non-negative matrix factorization, were reviewed and applied to a large-scale gene expression data set. Transcripts representing the top weights for each basis vector from the three methods were tested for functional enrichment based on Gene Ontology annotation. Linkage tests were performed on the components from each method and identified Quantitative Trait Loci were compared with the results from the analysis without dimension reduction.
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