Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data

Microarray experiments have been extensively used for simultaneously measuring DNA expression levels of thousands of genes in genome research. A key step in the analysis of gene expression data is the clustering of genes into groups that show similar expression values over a range of conditions. Since only a small subset of the genes participate in any cellular process of interest, by focusing on subsets of genes and conditions, we can lower the noise induced by other genes and conditions -- a co-cluster characterizes such a subset of interest. Cheng and Church [3] introduced an effective measure of co-cluster quality based on mean squared residue. In this paper, we use two similar squared residue measures and propose two fast k-means like co-clustering algorithms corresponding to the two residue measures. Our algorithms discover k row clusters and l column clusters simultaneously while monotonically decreasing the respective squared residues. Our co-clustering algorithms inherit the ...

• Type: Conference
• Year: 2004
• Where: SDM
• Authors: Hyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, Suvrit Sra
• Keywords: Co-clustering Algorithms, Data Mining, Residue Measures, SDM 2004, Squared Residues