Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Genome-Scale Computational Approaches to Memory-Intensive Applications in Systems Biology
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Scalable Parallel Algorithms for FPT Problems
Algorithmica
Techniques for clustering gene expression data
Computers in Biology and Medicine
Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
The cluster editing problem: implementations and experiments
IWPEC'06 Proceedings of the Second international conference on Parameterized and Exact Computation
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A wealth of clustering algorithms has been applied to gene coexpression experiments. These algorithms cover a broad array of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray data that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae.Clusters are scored using Jaccard similarity coefficients for the analysis of the positive match of clusters to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further development and application of combinatorial strategies is warranted.