Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data
1 Equipe Signalisations et identités cellulaires, Centre de Génétique Moléculaire et Cellulaire CNRS UMR 5534, Université Claude Bernard Lyon 1, 16 rue Dubois, F-69622 Villeurbanne cedex, France
2 Laboratoire d'Ingénierie des Systèmes d'Information, Institut National des Sciences Appliquées de Lyon, Bâtiment Blaise Pascal, F-69621 Villeurbanne cedex, France
Genome Biology 2002, 3:research0067-research0067.16 doi:10.1186/gb-2002-3-12-research0067Published: 21 November 2002
The association-rules discovery (ARD) technique has yet to be applied to gene-expression data analysis. Even in the absence of previous biological knowledge, it should identify sets of genes whose expression is correlated. The first association-rule miners appeared six years ago and proved efficient at dealing with sparse and weakly correlated data. A huge international research effort has led to new algorithms for tackling difficult contexts and these are particularly suited to analysis of large gene-expression matrices. To validate the ARD technique we have applied it to freely available human serial analysis of gene expression (SAGE) data.
The approach described here enables us to designate sets of strong association rules. We normalized the SAGE data before applying our association rule miner. Depending on the discretization algorithm used, different properties of the data were highlighted. Both common and specific interpretations could be made from the extracted rules. In each and every case the extracted collections of rules indicated that a very strong co-regulation of mRNA encoding ribosomal proteins occurs in the dataset. Several rules associating proteins involved in signal transduction were obtained and analyzed, some pointing to yet-unexplored directions. Furthermore, by examining a subset of these rules, we were able both to reassign a wrongly labeled tag, and to propose a function for an expressed sequence tag encoding a protein of unknown function.
We show that ARD is a promising technique that turns out to be complementary to existing gene-expression clustering techniques.