A semi-nonparametric mixture model for selecting functionally consistent proteins
1 Center for Biostatistics, The Ohio State University, Columbus, OH 43221, USA
2 Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
BMC Bioinformatics 2010, 11:486 doi:10.1186/1471-2105-11-486Published: 28 September 2010
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.
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.
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.