Gene expression clustering: a novel graph partitioning approach

Show full item record

Title: Gene expression clustering: a novel graph partitioning approach
Author: Chen, Yanhua; Dong, Ming; Rege, Manjeet
Abstract: In order to help understand how the genes are affected by different disease conditions in a biological system, clustering is typically performed to analyze gene expression data. In this paper, we propose to solve the clustering problem using a graph theoretical approach, and apply a novel graph partitioning model - Isoperimetric Graph Partitioning (IGP), to group biological samples from gene expression data. The IGP algorithm has several advantages compared to the wellestablished Spectral Graph Partitioning (SGP) model. First, IGP requires a simple solution to a sparse system of linear equations instead of the eigen-problem in the SGP model. Second, IGP avoids degenerate cases produced by spectral approach to achieve a partition with higher accuracy. Moreover, we integrate unsupervised gene selection into the proposed approach through two-way ordering of gene expression data, such that we can eliminate irrelevant or redundant genes in the data and obtain an improved clustering result. We evaluate our approach on several well-known problems involving gene expression profiles of colon cancer and leukemia subtypes. Our experiment results demonstrate that IGP constantly outperforms SGP and produces a better result that is closer to the original labeling of sample sets provided by domain experts. Furthermore, the clustering accuracy is improved significantly when IGP is integrated with the unsupervised gene (feature) selection.
Description: ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. …………………………………………………………………………………………………………………………………………………............................................. "Gene Expression Clustering: a Novel Graph Partitioning Approach," Proceedings of the IEEE International Joint Conference on Neural Networks. Held in Orlando, Florida: August 2007.
Record URI: http://hdl.handle.net/1850/8254
Date: 2007

Files in this item

Files Size Format View
MRegeConfProc08-2007.pdf 999.3Kb PDF View/Open

The following license files are associated with this item:

This item appears in the following Collection(s)

Show full item record

Search RIT DML


Advanced Search

Browse