Take our Survey

Reference: Dimaggio PA Jr, et al. (2008) Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies. BMC Bioinformatics 9:458

Reference Help

Abstract


ABSTRACT: BACKGROUND: The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters. RESULTS: In this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. Cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices to generate biclusters. The performance of OREO is tested on (a) metabolite concentration data, (b) an image reconstruction matrix, (c) synthetic data with implanted biclusters, and gene expression data for (d) colon cancer data, (e) breast cancer data, as well as (f) yeast segregant data to validate the ability of the proposed method and compare it to existing biclustering and clustering methods. CONCLUSIONS: We demonstrate that this rigorous global optimization method for biclustering produces clusters with more insightful groupings of similar entities, such as genes or metabolites sharing common functions, than other clustering and biclustering algorithms and can reconstruct underlying fundamental patterns in the data for several distinct sets of data matrices arising in important biological applications.

Reference Type
Journal Article
Authors
Dimaggio PA Jr, McAllister SR, Floudas CA, Feng XJ, Rabinowitz JD, Rabitz HA
Primary Lit For
Additional Lit For
Review For

Interaction Annotations


Increase the total number of rows showing on this page by using the pull-down located below the table, or use the page scroll at the table's top right to browse through the table's pages; use the arrows to the right of a column header to sort by that column; filter the table using the "Filter" box at the top of the table; click on the small "i" buttons located within a cell for an annotation to view further details about experiment type and any other genes involved in the interaction.

Interactor Interactor Type Assay Annotation Action Modification Phenotype Source Reference

Gene Ontology Annotations


Increase the total number of rows showing on this page using the pull-down located below the table, or use the page scroll at the table's top right to browse through the table's pages; use the arrows to the right of a column header to sort by that column; filter the table using the "Filter" box at the top of the table.

Gene Gene Ontology Term Qualifier Aspect Method Evidence Source Assigned On Annotation Extension Reference

Phenotype Annotations


Increase the total number of rows showing on this page using the pull-down located below the table, or use the page scroll at the table's top right to browse through the table's pages; use the arrows to the right of a column header to sort by that column; filter the table using the "Filter" box at the top of the table; click on the small "i" buttons located within a cell for an annotation to view further details.

Gene Phenotype Experiment Type Mutant Information Strain Background Chemical Details Reference

Regulation Annotations


Increase the total number of rows displayed on this page using the pull-down located below the table, or use the page scroll at the table's top right to browse through the table's pages; use the arrows to the right of a column header to sort by that column; to filter the table by a specific experiment type, type a keyword into the Filter box (for example, “microarray”); download this table as a .txt file using the Download button or click Analyze to further view and analyze the list of target genes using GO Term Finder, GO Slim Mapper, SPELL, or YeastMine.

Regulator Target Experiment Assay Construct Conditions Strain Background Reference