Statistical data mining for symbol associations in genomic databases

TL;DR

This paper introduces a statistical methodology to identify significant symbol associations in genomic databases, revealing both known and novel gene or protein interactions through graph analysis of symbol co-occurrences.

Contribution

A new statistical test and graph-based approach for detecting significant symbol associations in genomic data, applicable to various databases and capable of uncovering novel biological interactions.

Findings

Detected known gene interactions in MSigDB C2 database

Identified novel symbol associations in specific gene sets

Method applicable to any genomic database

Abstract

A methodology is proposed to automatically detect significant symbol associations in genomic databases. A new statistical test is proposed to assess the significance of a group of symbols when found in several genesets of a given database. Applied to symbol pairs, the thresholded p-values of the test define a graph structure on the set of symbols. The cliques of that graph are significant symbol associations, linked to a set of genesets where they can be found. The method can be applied to any database, and is illustrated MSigDB C2 database. Many of the symbol associations detected in C2 or in non-specific selections did correspond to already known interactions. On more specific selections of C2, many previously unkown symbol associations have been detected. These associations unveal new candidates for gene or protein interactions, needing further investigation for biological evidence.