A Poisson-based adaptive affinity propagation clustering for SAGE data.
Serial analysis of gene expression (SAGE) is a powerful tool to obtain gene expression profiles. Clustering analysis is a valuable technique for analyzing SAGE data. In this paper, we propose an adaptive clustering method for SAGE data analysis, namely, PoissonAPS. The method incorporates a novel clustering algorithm, Affinity Propagation (AP). While AP algorithm has demonstrated good performance on many different data sets, it also faces several limitations. PoissonAPS overcomes the limitations of AP using the clustering validation measure as a cost function of merging and splitting, and as a result, it can automatically cluster SAGE data without user-specified parameters. We evaluated PoissonAPS and compared its performance with other methods on several real life SAGE datasets. The experimental results show that PoissonAPS can produce meaningful and interpretable clusters for SAGE data.
Tang
DongMing
D
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China. tdm_yc@uestc.edu.cn
Zhu
QingXin
Q
Yang
Fan
F
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D015723:Gene Library; D006801:Humans; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics*; D016012:Poisson Distribution; D012333:RNA, Messenger / Q000235:genetics; D012160:Retina / Q000378:metabolism; D012984:Software
D012333:RNA, Messenger