Significance evaluation in factor graphs.

ABSTRACT:

Background

Factor graphs provide a flexible and general framework for specifying probability distributions. They can capture a range of popular and recent models for analysis of both genomics data as well as data from other scientific fields. Owing to the ever larger data sets encountered in genomics and the multiple-testing issues accompanying them, accurate significance evaluation is of great importance. We here address the problem of evaluating statistical significance of observations from factor graph models.

Results

Two novel numerical approximations for evaluation of statistical significance are presented. First a method using importance sampling. Second a saddlepoint approximation based method. We develop algorithms to efficiently compute the approximations and compar

SUBMITTER: Madsen T 

PROVIDER: S-EPMC5374669 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature