Statistical Analysis for Protoemics data
by
Mourad Atlas
University Of Louisville
Coauthors: Pihur,V., Datta, S.

Mass spectrometry has emerged as a core technology for high throughput proteomics profiling. It has enormous potential in biomedical research; however the complexity of the data poses new statistical challenges for the analysis. In this paper, we develop novel and improved statistical methods for analyzing high dimensional mass-spectrometry proteomics data. In particular, we focus on detecting monoisotopic peaks using statistical modeling, estimating the parameters and hypotheses testing. First, we propose a new approach to find isotopic distributions and a nonparametric approach to test them. Once an isotopic distribution has been taken into consideration, a sequential nonparametric test is applied to test whether monoisotopic peaks exist or not. We illustrate two examples using both real and simulated data.

Date received: February 28, 2008

Copyright © 2008 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cawu-56.