Fuzzy c-means clustering with prior biological knowledge.
We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike traditional clustering methods, our method is capable of assigning genes to multiple clusters, which is a more appropriate representation of the behavior of genes. Two datasets of yeast (Saccharomyces cerevisiae) expression profiles were applied to compare our method with other state-of-the-art clustering methods. Our experiments show that our method can produce far better biologically meaningful clusters even with the use of a small percentage of Gene Ontology annotations. In addition, our experiments further indicate that the utilization of prior knowledge in our method can predict gene functions effectively. The source code is freely available at http://sysbio.fulton.asu.edu/gofuzzy/.
Tari
Luis
L
School of Computing and Informatics, Department of Computer Science and Engineering, Ira A. Fulton School of Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ 85287-8809, USA. luis.tari@asu.edu
Baral
Chitta
C
Kim
Seungchan
S
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D030541:Databases, Genetic; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods*; D005796:Genes / Q000502:physiology*; D005800:Genes, Fungal / Q000502:physiology; D020407:Internet; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software