The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
Activity Number:
|
612
|
Type:
|
Topic Contributed
|
Date/Time:
|
Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
|
Sponsor:
|
ENAR
|
Abstract - #302062 |
Title:
|
Bayesian Models in Biomarker Discovery Using Spectral Count Data in the Label-Free Shotgun Proteomics
|
Author(s):
|
Xia Wang*+ and Nell Sedransk
|
Companies:
|
National Institute of Statistical Sciences and National Institute of Statistical Sciences
|
Address:
|
, , ,
|
Keywords:
|
Biomarker discovery ;
Bayesian models ;
Mixture priors ;
Spectral count
|
Abstract:
|
Spectral counting is one of the measures used in label-free quantitative proteomics. Extensive research in biomarker discovery studies focuses on detecting differentially expressed proteins in cancer and in chronic diseases. In label-free proteomics, peptides are measured rather than whole proteins using mass spectrometry. "Shotgun" studies are designed to identify peptides present in a biological sample, typically using spectral counts from a liquid chromatography-mass spectrometry (LC-MS) system. Spectral counting is used in other applications as well, but there are several challenges inherent to LC-MS spectral counting: few replicates, sparse counts, large number of proteins, and unreliable variance estimation. A Bayesian hierarchical model using mixture priors is proposed to model and analyze these data. Compared to simple Poisson regressions and the QSpec model, the proposed approac
|
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 2011 program
|
2011 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.