A new measure for gene expression biclustering based on non-parametric correlation.
BACKGROUND
One of the emerging techniques for performing the analysis of the DNA microarray data known as biclustering is the search of subsets of genes and conditions which are coherently expressed. These subgroups provide clues about the main biological processes. Until now, different approaches to this problem have been proposed. Most of them use the mean squared residue as quality measure but relevant and interesting patterns can not be detected such as shifting, or scaling patterns. Furthermore, recent papers show that there exist new coherence patterns involved in different kinds of cancer and tumors such as inverse relationships between genes which can not be captured.


RESULTS
The proposed measure is called Spearman's biclustering measure (SBM) which performs an estimation of the quality of a bicluster based on the non-linear correlation among genes and conditions simultaneously. The search of biclusters is performed by using a evolutionary technique called estimation of distribution algorithms which uses the SBM measure as fitness function. This approach has been examined from different points of view by using artificial and real microarrays. The assessment process has involved the use of quality indexes, a set of bicluster patterns of reference including new patterns and a set of statistical tests. It has been also examined the performance using real microarrays and comparing to different algorithmic approaches such as Bimax, CC, OPSM, Plaid and xMotifs.


CONCLUSIONS
SBM shows several advantages such as the ability to recognize more complex coherence patterns such as shifting, scaling and inversion and the capability to selectively marginalize genes and conditions depending on the statistical significance.
Intelligent Systems Group, Department of Computer Sciences and Artificial Intelligence, University of the Basque Country, P.O. Box 649, 20080 Donostia - San Sebastian, Spain. Electronic address: joseluis.flores@ehu.es.
Jose L
JL
Flores
Iñaki
I
Inza
Pedro
P
Larrañaga
Borja
B
Calvo
D000465:Algorithms; D016000:Cluster Analysis; D015870:Gene Expression
Artificial intelligence; Biclustering; Biomedicine; Machine learning