A semi-nonparametric mixture model for selecting functionally consistent proteins

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 d...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Lianbo Yu, R. W. Doerge
• Keywords: BMCBI 2010, Consistent Proteins, Protein, Semi-nonparametric Mixture Model