False Discovery Rate Controlling Procedures with BLOSUM62 substitution matrix and their application to HIV Data

Identifying significant sites in sequence data and analogous data is of fundamental importance in many biological fields. Fisher's exact test is a popular technique, however this approach to sparse count data is not appropriate due to conservative decisions. Since count data in HIV data are typically very sparse, it is crucial to use additional information to statistical models to improve testing power. In order to develop new approaches to incorporate biological information in the false discovery controlling procedure, we propose two models: one based on the empirical Bayes model under independence of amino acids and the other uses pairwise associations of amino acids based on Markov random field with on the BLOSUM62 substitution matrix. We apply the proposed methods to HIV data and identify significant sites incorporating BLOSUM62 matrix while the traditional method based on Fisher's test does not discover any site. These newly developed methods have the potential to handle many biological problems in the studies of vaccine and drug trials and phenotype studies