Robust and accurate data enrichment statistics via distribution
function of sum of weights
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.
Web application:
SaddleSum
Standalone program:
FTP
Cytoscape plugin:
FTP
Documentation:
HTML,
PDF
Publications: