Biclustering algorithms for biological data analysis: a survey

A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.

[1]  J. Hartigan.  Direct Clustering of a Data Matrix , 1972 .

[2]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[3]  D. Duffy,et al.  A permutation-based algorithm for block clustering , 1991 .

[4]  M. Clanet.  Interferon-Beta in the Treatment of Multiple Sclerosis , 2001, Multiple sclerosis.

[5]  Martin Schader,et al.  A New Algorithm for Two-Mode Clustering , 1996 .

[6]  Nir Friedman,et al.  Learning Bayesian Networks with Local Structure , 1996, UAI.

[7]  Dan Gusfield,et al.  Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology , 1997 .

[8]  Dan Gusfield,et al.  Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology , 1997 .

[9]  G. S. Johnson,et al.  An Information-Intensive Approach to the Molecular Pharmacology of Cancer , 1997, Science.

[10]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[11]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[12]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[13]  Dean P. Foster,et al.  A Formal Statistical Approach to Collaborative Filtering , 1998 .

[14]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[15]  R. Tibshirani,et al.  Clustering methods for the analysis of DNA microarray data , 1999 .

[16]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[17]  D. Botstein,et al.  The transcriptional program in the response of human fibroblasts to serum. , 1999, Science.

[18]  Thomas Hofmann,et al.  Latent Class Models for Collaborative Filtering , 1999, IJCAI.

[19]  Andrea Califano,et al.  Analysis of Gene Expression Microarrays for Phenotype Classification , 2000, ISMB.

[20]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[21]  Yudong D. He,et al.  Functional Discovery via a Compendium of Expression Profiles , 2000, Cell.

[22]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[23]  D. Botstein,et al.  Genomic expression programs in the response of yeast cells to environmental changes. , 2000, Molecular biology of the cell.

[24]  George M. Church,et al.  Biclustering of Expression Data , 2000, ISMB.

[25]  G. Getz,et al.  Coupled two-way clustering analysis of gene microarray data. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[26]  L. Lazzeroni.  Plaid models for gene expression data , 2000 .

[27]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[28]  D. Botstein,et al.  Genomic expression responses to DNA-damaging agents and the regulatory role of the yeast ATR homolog Mec1p. , 2001, Molecular biology of the cell.

[29]  Ben Taskar,et al.  Rich probabilistic models for gene expression , 2001, ISMB.

[30]  Y. Tu,et al.  Gene Expression Profiling of B Cell Chronic Lymphocytic Leukemia Reveals a Homogeneous Phenotype Related to Memory B Cells , 2001, The Journal of experimental medicine.

[31]  Aidong Zhang,et al.  Interrelated two-way clustering: an unsupervised approach for gene expression data analysis , 2001, Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001).

[32]  Inderjit S. Dhillon,et al.  Co-clustering documents and words using bipartite spectral graph partitioning , 2001, KDD '01.

[33]  Roger E Bumgarner,et al.  Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. , 2001, Science.

[34]  E. Dougherty,et al.  Gene-expression profiles in hereditary breast cancer. , 2001, The New England journal of medicine.

[35]  Philip S. Yu,et al.  Clustering by pattern similarity in large data sets , 2002, SIGMOD '02.

[36]  T. Poggio,et al.  Prediction of central nervous system embryonal tumour outcome based on gene expression , 2002, Nature.

[37]  Philip S. Yu,et al.  /spl delta/-clusters: capturing subspace correlation in a large data set , 2002, Proceedings 18th International Conference on Data Engineering.

[38]  Pavel Berkhin,et al.  Learning Simple Relations: Theory and Applications , 2002, SDM.

[39]  Markus Hegland,et al.  Algorithms for Association Rules , 2002, Machine Learning Summer School.

[40]  Richard M. Karp,et al.  Discovering local structure in gene expression data: the order-preserving submatrix problem , 2002, RECOMB '02.

[41]  Pierre Baldi,et al.  DNA Microarrays and Gene Expression - From Experiments to Data Analysis and Modeling , 2002 .

[42]  E. Lander,et al.  MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia , 2002, Nature Genetics.

[43]  Roded Sharan,et al.  Discovering statistically significant biclusters in gene expression data , 2002, ISMB.

[44]  T. M. Murali,et al.  Extracting Conserved Gene Expression Motifs from Gene Expression Data , 2002, Pacific Symposium on Biocomputing.

[45]  Philip S. Yu,et al.  Enhanced biclustering on expression data , 2003, Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings..

[46]  Inderjit S. Dhillon,et al.  Information-theoretic co-clustering , 2003, KDD '03.

[47]  Wei Wang,et al.  OP-cluster: clustering by tendency in high dimensional space , 2003, Third IEEE International Conference on Data Mining.

[48]  Joseph T. Chang,et al.  Spectral biclustering of microarray data: coclustering genes and conditions. , 2003, Genome research.

[49]  Bart De Moor,et al.  Biclustering microarray data by Gibbs sampling , 2003, ECCB.

[50]  Daphne Koller,et al.  Decomposing Gene Expression into Cellular Processes , 2002, Pacific Symposium on Biocomputing.

[51]  René Peeters,et al.  The maximum edge biclique problem is NP-complete , 2003, Discret. Appl. Math..

[52]  Suvrit Sra,et al.  Minimum Sum-Squared Residue based clustering of Gene Expression Data , 2004 .

[53]  Inderjit S. Dhillon,et al.  Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data , 2004, SDM.