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Activity Number: 242
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #305724
Title: OSRR: Efficiently Leveraging Transcriptional Databases for Improved Analysis of Differential Expression
Author(s): Jonathan Gelfond*+ and Mayetri Gupta and Joseph Ibrahim and Ming-Hui Chen
Companies: The University of Texas Health Science Center at San Antonio and Boston University and The University of North Carolina at Chapel Hill and University of Connecticut
Address: 31 Vienna, San Antonio, TX, 78258-4308, United States
Keywords: microarray ; network ; data mining
Abstract:

When there are large-scale chromosomal deletions or other non-specific perturbations of the transcriptome, it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene's expression as a function other genes accounting for the effect of gene-gene dependencies. We demonstrate that a ridge-regression model can be estimated from large gene expression databases, and then applied to smaller experiments. This method tends to maximize the stability of the parameter estimates and leads to a much greater degree of parameter shrinkage, but the biased estimation that is mitigated by a second round of regression. In our case study, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio. Both the sensitivity and reliability of differential expression measures are improved. We also show that a large proportion of gene dependencies are disrupted by copy-number variation, which would be impossible with standard differential expression methods.


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