The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Online Program Home
Abstract Details
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.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2012 program
|
2012 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.