Abstract
Background
High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins.
Results
We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions.
Conclusions
We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein.
Date of this Version
9-28-2010
DOI
10.1186/1471-2105-11-486
Recommended Citation
Yu, Lianbo and Doerge, Rebecca W., "A Semi-Nonparametric Mixture Model for Selecting Functionally Consistent Proteins." (2010). Department of Statistics Faculty Publications. Paper 6.
http://dx.doi.org/10.1186/1471-2105-11-486
Comments
This is the publisher pdf of Lianbo Yu, RW Doerge. A Semi-Nonparametric Mixture Model for Selecting Functionally Consistent Proteins. BMC Bioinformatics 2010, 11:486 (28 September 2010) and is available at: 10.1186/1471-2105-11-486.