Effective similarity measures for expression profiles

Authors:
Golan Yona;William Dirks;Shafquat Rahman;David M. Lin
Affiliations:
Department of Computer Science, Cornell University NY, USA;Center of Applied Mathematics, Cornell University NY, USA;Department of Computer Science, Cornell University NY, USA;Department of Biomedical Sciences, Cornell University NY, USA
Venue:
Bioinformatics
Year:
2006

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Abstract

It is commonly accepted that genes with similar expression profiles are functionally related. However, there are many ways one can measure the similarity of expression profiles, and it is not clear a priori what is the most effective one. Moreover, so far no clear distinction has been made as for the type of the functional link between genes as suggested by microarray data. Similarly expressed genes can be part of the same complex as interacting partners; they can participate in the same pathway without interacting directly; they can perform similar functions; or they can simply have similar regulatory sequences. Here we conduct a study of the notion of functional link as implied from expression data. We analyze different similarity measures of gene expression profiles and assess their usefulness and robustness in detecting biological relationships by comparing the similarity scores with results obtained from databases of interacting proteins, promoter signals and cellular pathways, as well as through sequence comparisons. We also introduce variations on similarity measures that are based on statistical analysis and better discriminate genes which are functionally nearby and faraway. Our tools can be used to assess other similarity measures for expression profiles, and are accessible at biozon.org/tools/expression/ Contact: golan@cs.technion.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.