Abstract
Hierarchical clustering is a popular method for grouping together similar elements based on a distance measure between them. In many cases, annotations for some elements are known beforehand, which can aid the clustering process. We present a novel approach for decomposing a hierarchical clustering into the clusters that optimally match a set of known annotations, as measured by the variation of information metric. Our approach is general and does not require the user to enter the number of clusters desired. We apply it to two biological domains: finding protein complexes within protein interaction networks and identifying species within metagenomic DNA samples. For these two applications, we test the quality of our clusters by using them to predict complex and species membership, respectively. We find that our approach generally outperforms the commonly used heuristic methods.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arnau, V., Mars, S., Marín, I.: Iterative cluster analysis of protein interaction data. Bioinformatics 21(3), 364–378 (2005)
Bader, G.D., Hogue, C.W.V.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 2 (2003)
Bernard, A., Vaughn, D.S., Hartemink, A.J.: Reconstructing the topology of protein complexes. In: Speed, T., Huang, H. (eds.) RECOMB 2007. LNCS (LNBI), vol. 4453, pp. 32–46. Springer, Heidelberg (2007)
Böhm, C., Plant, C.: HISSCLU: a hierarchical density-based method for semi-supervised clustering. In: Proceedings of the 2008 International Conference on Extending Database Technology, pp. 440–451. ACM Press, New York (2008)
Brohee, S., van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7, 488+ (2006)
Brun, C., Chevenet, F., Martin, D., Wojcik, J., Guenoche, A., Jacq, B.: Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biol. 5(1), R6 (2003)
Buehler, E.C., Sachs, J.R., Shao, K., Bagchi, A., Ungar, L.H.: The CRASSS plug-in for integrating annotation data with hierarchical clustering results. Bioinformatics 20(17), 3266–3269 (2004)
Cole, J.R., Chai, B., Farris, R.J., Wang, Q., Kulam, S.A., McGarrell, D.M., Garrity, G.M., Tiedje, J.M.: The ribosomal database project (RDP-II): sequences and tools for high-throughput rRNA analysis. Nucleic Acids Res. 33, 294–296 (2005)
Corby-Harris, V., et al.: Geographical distribution and diversity of bacteria associated with natural populations of Drosophila melanogaster. Appl. Environ. Microbiol. 73, 3470–3479 (2007)
DeSantis, T.Z., Hugenholtz, P., Keller, K., Brodie, E.L., Larsen, N., Piceno, Y.M., Phan, R., Andersen, G.L.: NAST: a multiple sequence alignment server for comparative analysis of 16s rRNA genes. Nucleic Acids Res. 34(Web Server issue), W394–W399 (2006)
Dhillon, I.S., Guan, Y., Kulis, B.: Weighted graph cuts without eigenvectors a multilevel approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(11), 1944–1957 (2007)
Dotan-Cohen, D., Melkman, A.A., Kasif, S.: Hierarchical tree snipping: Clustering guided by prior knowledge. Bioinformatics 23(24), 3335–3342 (2007)
Eckburg, P.B., Bik, E.M., Bernstein, C.N., Purdom, E., Dethlefsen, L., Sargent, M., Gill, S.R., Nelson, K.E., Relman, D.A.: Diversity of the human intestinal microbial flora. Science 308(5728), 1635–1638 (2005)
Edgar, R.C.: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5), 1792–1797 (2004)
Felsenstein, J.: PHYLIP: Phylogeny inference package (version 3.2). Cladistics 5, 164–166 (1989)
Fulthorpe, R.R., Roesch, L.F.W., Riva, A., Triplett, E.W.: Distantly sampled soils carry few species in common. ISME J. 2, 901–910 (2008)
Garey, M.R., Johnson, D.S.: Comptuers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, New York (1979)
Gascuel, O.: BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Mol. Biol. Evol. 14(7), 685–695 (1997)
Guldener, U., Munsterkotter, M., Kastenmuller, G., Strack, N., van Helden, J., Lemer, C., Richelles, J., Wodak, S.J., Garcia-Martinez, J., Perez-Ortin, J.E., Michael, H., Kaps, A., Talla, E., Dujon, B., Andre, B., Souciet, J.L., De Mon tigny, J., Bon, E., Gaillardin, C., Mewes, H.W.: CYGD: the comprehensive yeast genome database. Nucleic Acids Res. 33(suppl. 1), D364+ (2005)
Hart, T.G., Ramani, A.K., Marcotte, E.M.: How complete are current yeast and human protein-interaction networks? Genome Biol. 7, 120+ (2006)
Jaccard, P.: Nouvelles recherches sur la distribution florale. Bulletin de la Socit Vaudoise des Sciences Naturelles, 223–270 (1908)
Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N.J., Chung, S., Emili, A., Snyder, M., Greenblatt, J.F., Gerstein, M.: A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302(5644), 449–453 (2003)
Jukes, T.H., Cantor, C.R.: Evolution of Protein Molecules. Academic Press, London (1969)
Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)
Kennedy, J., et al.: Diversity of microbes associated with the marine sponge, Haliclona simulans, isolated from Irish waters and identification of polyketide synthase genes from the sponge metagenome. Environ. Microbiol. 10, 1888–1902 (2008)
Kerrien, S., Alam-Faruque, Y., Aranda, B., Bancarz, I., Bridge, A., Derow, C., Dimmer, E., Feuermann, M., Friedrichsen, A., Huntley, R., Kohler, C., Khadake, J., Leroy, C., Liban, A., Lieftink, C., Montecchi-Palazzi, L., Orchard, S., Risse, J., Robbe, K., Roechert, B., Thorneycroft, D., Zhang, Y., Apweiler, R., Hermjakob, H.: IntAct—open source resource for molecular interaction data. Nucleic Acids Res. 35(Database issue), D561–D565 (2007)
Kimura, M.: A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980)
King, A.D., Przulj, N., Jurisica, I.: Protein complex prediction via cost-based clustering. Bioinformatics 20(17), 3013–3020 (2004)
Li, X.L., Foo, C.S., Ng, S.K.: Discovering protein complexes in dense reliable neighborhoods of protein interaction networks. In: Comp. Syst. Bioinformatics Conference, vol. 6, pp. 157–168 (2007)
Mavromatis, K., Ivanova, N., Barry, K., Shapiro, H., Goltsman, E., McHardy, A.C.C., Rigoutsos, I., Salamov, A., Korzeniewski, F., Land, M., Lapidus, A., Grigoriev, I., Richardson, P., Hugenholtz, P., Kyrpides, N.C.C.: Use of simulated data sets to evaluate the fidelity of metagenomic processing methods. Nat. Methods, 495–500 (2007)
Meila, M.: Comparing clusterings—an information based distance. J. Multivariate Anal. 98(5), 873–895 (2007)
Mirkin, B.: Mathematical classification and clustering. J. Global Optim. 12(1), 105–108 (1998)
Navlakha, S., Rastogi, R., Shrivastava, N.: Graph summarization with bounded error. In: Proceedings of the 2008 ACM SIGMOD Conference, pp. 419–432 (2008)
Navlakha, S., Schatz, M.C., Kingsford, C.: Revealing biological modules via graph summarization. J. Comp. Biol. 16(2), 253–264 (2009)
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103(23), 8577–8582 (2006)
Pei, P., Zhang, A.: A “seed-refine” algorithm for detecting protein complexes from protein interaction data. IEEE T. Nanobiosci. 6(1), 43–50 (2007)
Qiu, J., Noble, W.S.: Predicting co-complexed protein pairs from heterogeneous data. PLoS Comp. Biol. 4(4) (2008)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)
Rives, A.W., Galitski, T.: Modular organization of cellular networks. Proc. Natl. Acad. Sci. USA 100(3), 1128–1133 (2003)
Samanta, M.P., Liang, S.: Predicting protein functions from redundancies in large-scale protein interaction networks. Proc. Natl. Acad. Sci. USA 100(22), 12579–12583 (2003)
Schloss, P.D., Handelsman, J.: Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl. Environ. Microbiol. 71(3), 1501–1506 (2005)
Schloss, P.D., Handelsman, J.: Toward a census of bacteria in soil. PLoS Comp. Biol. 2(7), e92 (2006)
Sharan, R., Ulitsky, I., Shamir, R.: Network-based prediction of protein function. Nat. Mol. Syst. Biol. 3, 88 (2007)
Sogin, M.L.L., Morrison, H.G.G., Huber, J.A.A., Welch, D.M.M., Huse, S.M.M., Neal, P.R.R., Arrieta, J.M.M., Herndl, G.J.J.: Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc. Natl. Acad. Sci. USA 103(32), 12115–12120 (2006)
Tan, M., Smith, E., Broach, J., Floudas, C.: Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures. BMC Bioinformatics 9(1), 268 (2008)
Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22(22), 4673–4680 (1994)
Toronen, P.: Selection of informative clusters from hierarchical cluster tree with gene classes. BMC Bioinformatics 5, 32 (2004)
van Dongen, S.: A cluster algorithm for graphs. Technical Report INS-R0010, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam (2000)
Wang, Q., Garrity, G.M., Tiedje, J.M., Cole, J.R.: Naive bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73(16), 5261–5267 (2007)
Warnecke, F., Luginbühl, P., Ivanova, N., Ghassemian, M., Richardson, T.H., Stege, J.T., Cayouette, M., Mchardy, A.C., Djordjevic, G., Aboushadi, N., Sorek, R., Tringe, S.G., Podar, M., Martin, H.G., Kunin, V., Dalevi, D., Madejska, J., Kirton, E., Platt, D., Szeto, E., Salamov, A., Barry, K., Mikhailova, N., Kyrpides, N.C., Matson, E.G., Ottesen, E.A., Zhang, X., Hernández, M., Murillo, C., Acosta, L.G., Rigoutsos, I., Tamayo, G., Green, B.D., Chang, C., Rubin, E.M., Mathur, E.J., Robertson, D.E., Hugenholtz, P., Leadbetter, J.R.: Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 450(7169), 560–565 (2007)
Yu, H., Paccanaro, A., Trifonov, V., Gerstein, M.: Predicting interactions in protein networks by completing defective cliques. Bioinformatics 22(7), 823–829 (2006)
Zhu, X., Gerstein, M., Snyder, M.: Getting connected: analysis and principles of biological networks. Genes Dev. 21(9), 1010–1024 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Navlakha, S., White, J., Nagarajan, N., Pop, M., Kingsford, C. (2009). Finding Biologically Accurate Clusterings in Hierarchical Tree Decompositions Using the Variation of Information. In: Batzoglou, S. (eds) Research in Computational Molecular Biology. RECOMB 2009. Lecture Notes in Computer Science(), vol 5541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02008-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-02008-7_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02007-0
Online ISBN: 978-3-642-02008-7
eBook Packages: Computer ScienceComputer Science (R0)