Fuzzy clustering is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy clustering method for microarray data analysis. An advantage of the method is that it used a combination of the fuzzy c-means and the principal component analysis to identify the groups of genes that show similar expression patterns. It allows a gene to belong to more than a gene expression pattern with different membership grades. The method is suitable for the analysis of large amounts of noisy microarray data.
KimS.LeeJ.BaeJ.Effect of data normalization on fuzzy clustering of DNA microarray data. BMC Bioinformatics, 2006, 7, 134.
2.
WangJ.BoT. H.JonassenI.MyklebostO.HovigE.Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data. BMC Bioinformatics, 2003, 4, 60.
3.
AsyaliM. H.AlciM.Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods. Bioinformatics, 2005, 21(5), 644–649.
4.
GaschA. P.EisenM. B.Exploring the conditional coregulation of yeast gene expression through fuzzy means clustering. Genome Biology, 2002, 3(11), research 0059.1–0059.22.
5.
FuL.MedicoE.FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformatics, 2007, 8, 3.
6.
SanguinettiG.MiloM.RattrayM.LawrenceN. D.Accounting for probe-level noise in principal component analysis of microarray data. Bioinformatics, 2005, 21(19), 3748–3754.
7.
SchölkopfB.SmolaA.MüllerK.-R.Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10, 1299–1319.
8.
MikaS.SchölkopfB.SmolaA. J.MüllerK.-R.ScholzM.RätschG.Kernel PCA and denoising in feature spaces. In Advances in neural information processing systems, vol. 11 (Eds KearnsM. S.SollaS. A.CohnD. A.), 1999, pp. 536–542 (MIT Press, Cambridge, Massachusetts).
9.
LiuC.Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Analysis Mach. Intelli., 2004, 26(5), 572–581.
10.
Shawe-TaylorJ.CristianiniN.Kernel methods for pattern analysis, 2004 (Cambridge University Press, Cambridge).
BezdekJ. C., Pattern recognition with fuzzy objective function, 1981 (Plenum, New York).
13.
BezdekJ. C., Some non-standard clustering algorithms. In Developments in numerical ecology, NATO Advanced Study Institute Series, vol. G14, (Eds LegendreP.LegendreL.), 1987, pp. 225–287 (Springer-Verlag, New York).
BezdekJ. C.HatawayR. J.SabinM. J.TuckerW. T.Convergence theory for fuzzy c-means: Counterexamples and repairs. IEEE Trans. Systems Man. Cybernetics, 1987, 17, 873–877.
16.
SpellmanP. T.SherlockG.ZhangM. Q.IyerV. R.AndersK.EisenM. B.BrownP. O.BotsteinD.FutcherB.Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology Cell, 1998, 9(12), 3273–3297.
