KiWi: A Scalable Subspace Clustering Algorithm for Gene Ezpression Analysis
Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a patternbased subspace cluster, is a subset of rows and columns in a data matrix for which all the rows induce the same linear ordering of columns. Existing OPSM discovery methods do not scale well to increasingly large expression datasets. In particular, twig clusters having few genes and many experiments incur explosive computational costs and are completely pruned off by existing methods. However, it is of particular interest to determine small groups of genes that are tightly coregulated across many conditions. In this paper, we present KiWi, an OPSM subspace clustering algorithm that is scalable to massive datasets, capable of discovering twig clusters and identifying negative as well as positive correlations. We extensively validate KiWi using relevant biological datasets and show that KiWi correctly assigns redundant probes to the same cluster, groups experiments with common clinical annotations, differentiates real promoter sequences from negative control sequences, and shows good association with cis-regulatory motif predictions.
KiWi subspace clustering biclustering OPSM pattern-based cluster gene ezpression analysis twig cluster
Obi L.Griffith Byron J.Gao Mikhail Bilenky Yuliya Prychyna Martin Ester Steven J.M.Jones
BC Cancer Agency,Canada Texas State University-San Marcos,USA Simon Fraser University,Canada
国际会议
北京
英文
1-9
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）