MAP

The ultimate goal of the development of the MAP method is to find modules of genes that are co-regulated throughout subsets of conditions (i.e. arrays or samples). Consequently, the MAP method represents an improved method to perform the task of mining of large-scale gene expression microarray data. These research activities represent a collaboration with the Knowledge and Data Analysis Division of Unilever Research , Vlaardingen ( Netherlands)

Summary of research activities concerning MAP:

Association pattern discovery (APD) methods have been successfully applied to gene expression data. They find groups of co-regulated genes in which the genes are either up- or down-regulated throughout the identified conditions. Traditional APD algorithms, developed for binary (or Boolean) attributes, can be applied to such data with a prerequisite of transforming non-binary (continuous or categorical) attribute domains into binary ones. As a consequence of this binarization, the discovered patterns no longer reflect the associations between attributes but the relations between their binned independent values, and thus, interactions between the original attributes may be lost. Consequently, these methods fail to identify similarly expressed genes whose expressions change between up- and down-regulation from one condition to another. In order to discover these hidden patterns, we propose the concept of mining co-regulated gene profiles, also called the concept of "Mining Attribute Profiles". This method is detailed in Gyenesei A et al. (2006) . Co-regulated gene profiles contain two gene sets such that genes within the same set behave identically (up or down) while genes from different sets dis-play contrary behavior. To reduce and group the large number of similar resulting patterns, we propose a new similarity measure that can be applied together with hierarchical clustering methods. We tested our proposed method on two well-known yeast microarray datasets (Gyenesei A. , Wagner U. et al, 2007). As a results we observed that our implementation of the MAP method mined the data effectively and discovered patterns of co-regulated genes that are hidden to traditional APD methods. The high content of biologically relevant information in these patterns is demonstrated by the significant en-richment of co-regulated genes with similar functions. Our experi-mental results show that the MAP method is an efficient tool for the analysis of gene expression data and competitive with bi-clustering techniques.

Supplementary information:

Supplementary data for the publication Gyenesei A. , Wagner U. et al. (2007) and an executable demo program of the MAP implementation are freely available.

Publications

Gyenesei A, Schlapbach R, Stolte E, Wagner U. (2006) Frequent pattern discovery without binarization: Mining attribute profiles. PKDD 2006, LNAI 4213, 528 – 535

Gyenesei A.*, Wagner U.*, Barkow-Oesterreicher S., Stolte E. Schlapbach R (2007): Mining co-regulated gene profiles for the detection of functional associations in gene expression data, Bioinformatics; doi:10.1093/bioinformatics/btm276

*equal contribution

Contact

Hubert Rehrauer