Discovering biclusters in gene expression data based on high-dimensional linear geometries

Background: In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits consistent pattern over a subset of conditions. Conventional clustering algorithms that deal with the entire row or column in an expression matrix would therefore fail to detect these useful patterns in the data. Recently, biclustering has been proposed to detect a subset of genes exhibiting consistent pattern over a subset of conditions. However, most existing biclustering algorithms are based on searching for sub-matrices within a data matrix by optimizing certain heuristically defined merit functions. Moreover, most of these algorithms can only detect a restricted set of bicluster patterns. Results: In this paper, we present a novel geometric perspective for the biclustering problem. The biclustering process is int...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Xiangchao Gan, Alan Wee-Chung Liew, Hong Yan
• Keywords: Algorithm, BMCBI 2008, Consistent Pattern, Linear Geometries