EURASIP Journal on Applied Signal Processing 
Volume 2006 (2006), Article ID 59809, 12 pages
doi:10.1155/ASP/2006/59809

DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

Alain B. Tchagang1 and Ahmed H. Tewfik2

 1Department of Biomedical Engineering, Institute of Technology, University of Minnesota, 312 Church Street SE, Minneapolis 55455, MN, USA
2Department of Electrical and Computer Engineering, Institute of Technology, University of Minnesota, 200 Union Street SE, Minneapolis 55455, MN, USA
Received 15 May 2005; Revised 5 October 2005; Accepted 1 December 2005

Abstract

Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNA microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this study, we develop novel biclustering algorithms using basic linear algebra and arithmetic tools. The proposed biclustering algorithms can be used to search for all biclusters with constant values, biclusters with constant values on rows, biclusters with constant values on columns, and biclusters with coherent values from a set of data in a timely manner and without solving any optimization problem. We also show how one of the proposed biclustering algorithms can be adapted to identify biclusters with coherent evolution. The algorithms developed in this study discover all valid biclusters of each type, while almost all previous biclustering approaches will miss some.