Affiliations: [a] School of Computer Science and Technology, Southwest University for
Nationalities, Chengdu, Sichuan, China | [b] School of Mathematics, Southwest Jiaotong University,
Chengdu, Sichuan, China
Correspondence:
[*]
Corresponding author: Dongming Tang, School of Computer Science and Technology, Southwest University for Nationalities, Chengdu, Sichuan, China. E-mail:tdm_2010@swjtu.edu.cn
Abstract: Serial analysis of gene expression
(SAGE) is an efficient technique to produce a snapshot of the messenger RNA
population in a sample. Clustering method has been widely used for SAGE data
mining. Clustering SAGE data into different pattern groups can help to find
potentially unknown functional gene groups in SAGE dataset. By incorporating
a new published measurement (maximal information coefficient, MIC) into
hierarchical clustering techniques, we present a clustering method named
MicClustSAGE. The MIC can measure the pair-wise correlation coefficients
between SAGE libraries. The presented method significant improvements the
ability of clustering method in detecting specially tissue pattern of SAGE.
In addition, we compared the results obtained by our method and hierarchical
clustering with Pearson correlation. The experimental results exhibit the
performance of the proposed method on several real-life SAGE datasets.
Keywords: Serial analysis of gene expression, clustering, maximal information coefficient
