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

This paper uses two similar squared residue measures and proposes two fast co-clustering algorithms corresponding to the two residue measures, which inherit the simplicity, efficiency and wide applicability of the k-means algorithm.

Abstract

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2004 SIAM International Conference on Data Mining (SDM)Minimum Sum-Squared Residue Co-clustering of Gene Expression DataHyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, and Suvrit SraHyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, and Suvrit Srapp.114 - 125Chapter DOI:https://doi.org/10.1137/1.9781611972740.11PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract 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 simplicity, efficiency and wide applicability of the k-means algorithm. Minimizing the residues may also be formulated as trace optimization problems that allow us to obtain a spectral relaxation that we use for a principled initialization for our iterative algorithms. We further enhance our algorithms by an incremental local search strategy that helps avoid empty clusters and escape poor local minima. We illustrate co-clustering results on a yeast cell cycle dataset and a human B-cell lymphoma dataset. Our experiments show that our co-clustering algorithms are efficient and are able to discover coherent co-clusters. Previous chapter Next chapter RelatedDetails Published:2004ISBN:978-0-89871-568-2eISBN:978-1-61197-274-0 https://doi.org/10.1137/1.9781611972740Book Series Name:ProceedingsBook Code:PR117Book Pages:xiv + 537Key words:Gene-expression, co-clustering, biclustering, residue, spectral relaxation