Multiconstrained gene clustering based on generalized projections.
BACKGROUND
Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem.


RESULTS
We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The generalized projector iteratively projects the clustering solution onto these sets in order to find a consistent solution included in the intersection set that satisfies all constraints. Compared with previous MGC methods, POCS can integrate multiple constraints from different nature without distorting the original constraints. To evaluate the clustering solution, we also propose a new performance measure referred to as Gene Log Likelihood (GLL) that considers genes having more than one function and hence in more than one cluster. Comparative experimental results show that our POCS-based gene clustering method outperforms current state-of-the-art MGC methods.


CONCLUSIONS
The POCS-based MGC method can successfully combine multiple constraints from different nature for gene clustering. Also, the proposed GLL is an effective performance measure for the soft clustering solutions.
Zeng
Jia
J
School of Computer Science and Technology, Soochow University, Suzhou 215006, China. j.zeng@ieee.org
Zhu
Shanfeng
S
Liew
Alan Wee-Chung
AW
Yan
Hong
H
D000465:Algorithms; D016000:Cluster Analysis; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D016013:Likelihood Functions; D005810:Multigene Family; D010363:Pattern Recognition, Automated / Q000379:methods