Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
BMC Bioinformatics. 2008 Oct 27;9:458. doi: 10.1186/1471-2105-9-458.

Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies.

Author information

  • 1Department of Chemical Engineering, Princeton University, Princeton, NJ, USA. pete@titan.princeton.edu

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.

CONCLUSION:

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.

PMID:
18954459
[PubMed - indexed for MEDLINE]
PMCID:
PMC2605474
Free PMC Article

Images from this publication.See all images (5)Free text

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for BioMed Central Icon for PubMed Central
    Loading ...
    Write to the Help Desk