Spectral biclustering of microarray cancer data : co-clustering genes and conditions

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to simultaneously find "marker genes" that are differentially expressed in particular “conditions”. We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up or down regulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. Furthermore, these eigenvectors can be readily identified by commonly used linear-algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which it is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g. direct application of SVD or normalized cuts to raw data).

[1]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

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

[3]  Gene H. Golub,et al.  Matrix computations , 1983 .

[4]  T. Raghavan,et al.  Nonnegative Matrices and Applications , 1997 .

[5]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[9]  D. Botstein,et al.  Exploring the new world of the genome with DNA microarrays , 1999, Nature Genetics.

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

[11]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Yair Weiss,et al.  Segmentation using eigenvectors: a unifying view , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  N. Sampas,et al.  Molecular classification of cutaneous malignant melanoma by gene expression profiling , 2000, Nature.

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

[15]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Joshua M. Stuart,et al.  MICROARRAY EXPERIMENTS : APPLICATION TO SPORULATION TIME SERIES , 1999 .

[17]  E. Winzeler,et al.  Genomics, gene expression and DNA arrays , 2000, Nature.

[18]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

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

[20]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Neal S. Holter,et al.  Fundamental patterns underlying gene expression profiles: simplicity from complexity. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

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

[23]  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.

[24]  Ash A. Alizadeh,et al.  'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns , 2000, Genome Biology.

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

[26]  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.

[27]  M. Gerstein,et al.  Genomic and proteomic analysis of the myeloid differentiation program. , 2001, Blood.

[28]  Trevor Hastie,et al.  Imputing Missing Data for Gene Expression Arrays , 2001 .

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

[30]  M K Kerr,et al.  Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Richard M. Karp,et al.  CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts , 2001, ISMB.

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

[33]  Isabelle Guyon,et al.  A Stability Based Method for Discovering Structure in Clustered Data , 2001, Pacific Symposium on Biocomputing.

[34]  M. Gerstein,et al.  Systematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons. , 2002, Genome research.