Multi-metric and multi-substructure biclustering analysis for gene expression data

Sun-Yuan Kung, Man Wai Mak, Ilias Tagkopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

A good number of biclustering algorithms have been proposed for grouping gene expression data. Many of them have adopted matrix norms to define the similarity score of a bicluster. We shall show that almost all matrix metrics can be converted into vector norms while preserving the rank equivalence. Vector norms provide a much more efficient vehicle for biclustering analysis and computation. The advantages are two folds: ease of analysis and saving of computation. Most existing biclustering algorithms have also implicitly assumed the use of univariate (i.e., single metric) evaluation for identifying biclusters. Such an approach however overlooks the fundamental principle that genes (even though they may belong to the same gene group) (1) may be subdivided into different substructures; and (2) they may be co-expressed via a diversity of coherence models (a gene may participate in multiple pathways that may or may not be co-active under all conditions). The former leads to the adoption of a multi-substurcture analysis, while the latter to the multivariate analysis. This paper will show that the proposed multivariate and multi-subscluster analysis is very effective in identifying and classifying biologically relevant groups in genes and conditions. For example, it has successfully yielded highly discriminant and accurate classification based on known ribosomal gene groups.

Original languageEnglish (US)
Title of host publicationProceedings - 2005 IEEE Computational SystemsBioinformatics Conference, CSB 2005
Pages123-134
Number of pages12
DOIs
StatePublished - Dec 1 2005
Event2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005 - Stanford, CA, United States
Duration: Aug 8 2005Aug 11 2005

Publication series

NameProceedings - 2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005
Volume2005

Other

Other2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005
CountryUnited States
CityStanford, CA
Period8/8/058/11/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Medicine(all)

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    Kung, S-Y., Mak, M. W., & Tagkopoulos, I. (2005). Multi-metric and multi-substructure biclustering analysis for gene expression data. In Proceedings - 2005 IEEE Computational SystemsBioinformatics Conference, CSB 2005 (pp. 123-134). [1498014] (Proceedings - 2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005; Vol. 2005). https://doi.org/10.1109/CSB.2005.40