PK-means: A new algorithm for gene clustering

Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. Gene cluster analysis is found useful for discovering the function of gene because co-expressed genes are likely to share the same biological function. K-means is one of well-known clustering methods. However, it is sensitive to the selection of an initial clustering and easily becoming trapped in a local minimum. Particle-pair optimizer (PPO) is a variation on the traditional particle swarm optimization (PSO) algorithm, which is stochastic particle-pair based optimization technique that can be applied to a wide range of problems. In this paper we bridges PPO and K-means within the algorithm PK-means for the first time. Our results indicate that PK-means clustering is generally more accurate than K-means and Fuzzy K-means (FKM). PK-means also has better robustness for it is less sensitive to the initial randomly selected cluster centroids. Finally, our algorithm outperforms these methods with fast convergence rate and low computation load.

[1]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Anbupalam Thalamuthu,et al.  Gene expression Evaluation and comparison of gene clustering methods in microarray analysis , 2006 .

[3]  Jiang Lai.  A Strategy of Particle-Pair for Vector Quantization in Image Coding , 2007 .

[4]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[5]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[6]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[7]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[8]  Tao Jiang,et al.  Algorithmic Approaches to Clustering Gene Expression Data , 2002 .

[9]  T. K. Baker,et al.  Temporal gene expression analysis of monolayer cultured rat hepatocytes. , 2001, Chemical research in toxicology.

[10]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[11]  Pascal Nsoh,et al.  Large-scale temporal gene expression mapping of central nervous system development , 2007 .

[12]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[13]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[14]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[16]  D. Botstein,et al.  The transcriptional program of sporulation in budding yeast. , 1998, Science.

[17]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[18]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[19]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .