Discovering local structure in gene expression data: the order-preserving submatrix problem

This paper concerns the discovery of patterns in gene expression matrices, in which each element gives the expression level of a given gene in a given experiment. Most existing methods for pattern discovery in such matrices are based on clustering genes by comparing their expression levels in all experiments, or clustering experiments by comparing their expression levels for all genes. Our work goes beyond such global approaches by looking for local patterns that manifest themselves when we focus simultaneously on a subset G of the genes and a subset T of the experiments. Speci cally, we look for order-preserving submatrices (OPSMs), in which the expression levels of all genes induce the same linear ordering of the experiments (we show that the OPSM search problem is NP-hard in the worst case). Such a pattern might arise, for example, if the experiments in T represent distinct stages in the progress of a disease or in a cellular process and the expression levels of all genes in G vary...

• Type: Conference
• Year: 2002
• Where: RECOMB
• Authors: Amir Ben-Dor, Benny Chor, Richard M. Karp, Zohar Yakhini
• Keywords: Computational Biology, Ef Cient Algorithm, Expression Levels, Random Matrix, RECOMB 2002