  Term enrichment analysis facilitates biological interpretation by assigning
to experimentally/computationally obtained data annotation associated with
terms from controlled vocabularies. This process usually involves obtaining
statistical significance for each vocabulary term and using the most
significant terms to describe a given set of biological entities, often
associated with weights. Many existing enrichment methods require selections of
(arbitrary number of) the most significant entities and/or do not account for
weights of entities. Others either mandate extensive simulations to obtain
statistics or assume normal weight distribution. In addition, most methods have
difficulty assigning correct statistical significance to terms with few
entities. Implementing the well-known Lugananni-Rice formula, we have developed
a novel approach, called SaddleSum, that is free from all the aforementioned
constraints and evaluated it against several existing methods. With entity
weights properly taken into account, SaddleSum is internally consistent and
stable with respect to the choice of number of most significant entities
selected. Making few assumptions on the input data, the proposed method is
universal and can thus be applied to areas beyond analysis of microarrays.
Employing asymptotic approximation, SaddleSum provides a term-size dependent
score distribution function that gives rise to accurate statistical
significance even for terms with few entities. As a consequence, SaddleSum
enables researchers to place confidence in its significance assignments to
small terms that are often biologically most specific.
