Beyond Bioinformatics

Title

Finding distinct biclusters from background in gene expression matrices

Authors

Zhengpeng Wu ^{1, $}, Jiangni Ao ^{1, $}, Xuegong Zhang^1, *

Affiliation

¹Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China; ^$These authors contributed equally to this work

Email

zhangxg@tsinghua.edu.cn; * Corresponding author

Article Type

Prediction Model

Date

received December 15, 2007; accepted December 31, 2007; published online December 30, 2007

Biclustering, or the discovery of subsets of samples and genes that are homogeneous and distinct from the background, has become an important technique in analyzing current microarray datasets. Most existing biclustering methods define a bicluster type as a fixed (predefined) pattern and then trying to get results in some searching process. In this work, we propose a novel method for finding biclusters or 2-dimensional patterns that are significantly distinct from the background without the need for pre-defining a pattern within the bicluster. The method named Distinct 2-Dimensional Pattern Finder (D2D) is composed of an iterative reordering step of the rows and columns in the matrix using a new similarity measure, and a flexible scanning-and-growing step to identify the biclusters. Experiments on a large variety of simulation data show that the method works consistently well under different conditions, whereas the existing methods compared may work well under some certain conditions but fail under some other conditions. The impact of noise levels, overlapping degrees between clusters and different setting of parameters were also investigated, which indicated that the D2D method is robust against these factors. The proposed D2D method can efficiently discover many different types of biclusters given that they have distinctive features from the background. The computer program is available upon request.

gene expression matrices; simulation; biclusters; Distinct 2-Dimensional (D2D); noise

Citation

Wu, et al., Bioinformation 2(5): 207-215 (2007)

Edited by

A. M. Khan, T. W. Tan & S. Ranganathan

ISSN

0973-2063

Publisher

Biomedical Informatics Publishing Group

Copyright

Publisher

Copyright Transfer Agreement

The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.

License

This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.