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Term enrichment analysis facilitates biological interpretation by assigningto experimentally/computationally obtained data annotation associated withterms from controlled vocabularies. This process usually involves obtainingstatistical significance for each vocabulary term and using the mostsignificant terms to describe a given set of biological entities, oftenassociated with weights. Many existing enrichment methods require selections of(arbitrary number of) the most significant entities and/or do not account forweights of entities. Others either mandate extensive simulations to obtainstatistics or assume normal weight distribution. In addition, most methods havedifficulty assigning correct statistical significance to terms with fewentities. Implementing the well-known Lugananni-Rice formula, we have developeda novel approach, called SaddleSum, that is free from all the aforementionedconstraints and evaluated it against several existing methods. With entityweights properly taken into account, SaddleSum is internally consistent andstable with respect to the choice of number of most significant entitiesselected. Making few assumptions on the input data, the proposed method isuniversal and can thus be applied to areas beyond analysis of microarrays.Employing asymptotic approximation, SaddleSum provides a term-size dependentscore distribution function that gives rise to accurate statisticalsignificance even for terms with few entities. As a consequence, SaddleSumenables researchers to place confidence in its significance assignments tosmall terms that are often biologically most specific.

Robust and accurate data enrichment statistics via distribution function of sum of weights