A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression.
Despite the widespread application of microarray imaging for biomedical imaging research, barriers still exist regarding its reliability for clinical use. A critical major problem lies in accurate spot segmentation and the quantification of gene expression level (mRNA) from the microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots with complex shapes such as donuts and scratches. Clustering approaches such ask-means and mixture models were introduced to overcome this difficulty, which use the hard labeling of each pixel. In this paper, we apply fuzzy clustering approaches for spot segmentation, which provides soft labeling of the pixel. We compare several fuzzy clustering approaches for microarray analysis and provide a comprehensive study of these approaches for spot segmentation. We show that possiblisticc-means clustering (PCM) provides the best performance in terms of stability criterion when testing on both a variety of simulated and real microarray images. In addition, we compared three statistical criteria in measuring gene expression levels and show that a new asymptotically unbiased statistic is able to quantify the gene expression level more accurately.
School of Computing and Engineering, University of Missouri, Kansas City, MO 64110, USA.
Yu-Ping
YP
Wang
School of Computing and Engineering, University of Missouri, Kansas City, MO 64110, USA.
Maheswar
M
Gunampally
Department of Mathematics and Statistics, Kansas City, MO 64110, USA.
Jie
J
Chen
Children's Mercy Hospital and Clinics, School of Medicine, University of Missouri, Kansas City, MO 64108, USA.
Douglas
D
Bittel
Children's Mercy Hospital and Clinics, School of Medicine, University of Missouri, Kansas City, MO 64108, USA.
Merlin G
MG
Butler
Department of Human Molecular Genetics, Baylor College of Medicine, Houston, TX 77005, USA.
Wei-Wen
WW
Cai
fuzzy clustering; image segmentation; microarray; microarray gridding; segmentation