Introduction

Gene expression data is usually arranged in a matrix such that each row represents a gene and each column corresponds to an experimental condition. The knowledge which genes co-regulate with one another under a particular set of experimental conditions is demanded because this provides information for scientists to determine the genes which participate in the same biological processes. The classical clustering methods fail to extract these relations because they are only able to either partition genes using the whole column data or cluster columns using the whole row data. Recently, biclustering is investigated to discover the underlined relations. The biclustering approaches can group a subset of genes in a subset of experimental conditions with respect to some similarity scores. In [1], we propose a new efficient algorithm based on parallel coordinate (PC) formulation for biclustering. By using different similarity criteria, it is possible to detect biclusters of the following types: constant, constant rows, constant columns, additive and multiplicative types. An explanation of the proposed biclustering algorithm can be found in the supplementary page while some experimental results for yeast S. cerevisiae are given in the result page. BiVisu is a software tool which implements our proposed biclustering algorithm. It is also a software for visualizing the detected biclusters in a 2D setting using PC plots. Currently, it is available in a Matlab program.


Features

The biclustering algorithm and visualization of BiVisu are both based on parallel coordinate (PC) plots. The BiVisu has an interactive graphical user interface (GUI) that users can analyze and refine their biclustering results in a convenient way. Main features of BiVisu are listed below,

Click here for an overview. A step-by-step example can be found through this link.


Download

Matlab version of BiVisu is available here. The artificial and real gene expression data used in [1] can be downloaded through the following link


References

  1. K.-O. Cheng, N.-F. Law, and W.-C. Siu and A. W.-C. Liew, Identification of Coherent Patterns in Gene Expression Data Using an Efficient Biclustering Algorithm and Parallel Coordinate Visualization, BMC Bioinformatics , vol. 9, article 210, April 2008, doi: 10.1186/1471-2105-9-210.
  2. Y. Cheng and G.M. Church, Biclustering of Expression Data, Proceedings, Conference on Intelligent Systems for Molecular Biology , pp. 93 - 103, 2000.
  3. L. Teng and L.-W. Chan, Biclustering Gene Expression Profiles by Alternately Sorting with Weighted Correlated Coefficient, Proceedings of IEEE International Workshop on Machine Learning for Signal Processing , pp. 289 - 294, 2006.
  4. A. Prelic et al. , A Systematic Comparison and Evaluation of Biclustering Methods for Gene Expression Data, Bioinformatics , vol. 22, no. 9, pp. 1122-1129, 2006.
  5. S.C. Madeira and A.L. Oliveira, Biclustering Algorithms for Biological Data Analysis: A Survey, IEEE/ACM Trans. Computational Biology and Bioinformatics , vol. 1, no. 1, pp. 24-45, 2004.
  6. C.I. Castillo-Davis and D.L. Hartl, GeneMerge - post-genomic analysis, data mining, and hypothesis testing, Bioinformatics , vol.19, no.7, pp.891-892, 2003.

Contact

Dr. Bonnie Law: ennflaw@polyu.edu.hk