A Graph-Based Method for Clustering of Gene Expression Data with Detection of Functionally Inactive Genes and Noise
Noise that presents in gene expression data creates trouble in clustering for many clustering algorithms, and it is also observed that some non-functional genes may be present in the gene expression data that should not be the part of any cluster. A solution of this problem first removes the functionally inactive genes or noise and then clusters the remaining genes. Based on this solution, a graph-based clustering algorithm is proposed in this article which first identified the functionally inactive genes or noise and after that clustered the remaining genes of gene expression data. The proposed method is applied to a cell cycle data of yeast, and the results show that it performs well in identification of highly co-expressed gene clusters in the presence of functionally inactive genes and noise.
KeywordsClustering Gene expression data Data mining
- 3.Young, W.C., Yeung, K.Y., Raftery, A.E.: Model-based clustering with data correction for removing artifacts in gene expression data. arXiv:1602.06316
- 9.Sharan, R., Shamir, R.: Click: a clustering algorithm with applications to gene expression analysis. In: Proceedings of International Conference on Intelligent Systems for Molecular Biology, vol. 8, p. 16 (2000)Google Scholar
- 16.Brock, G., Pihur, V., Datta, S., Datta, S., et al.: clvalid, an r package for cluster validation. J. Stat. Softw. (2008)Google Scholar
- 18.Hosseininasab, S.M.E., Ershadi, M.J.: Optimization of the number of clusters: a case study on multivariate quality control results of segment installation. Int. J. Adv. Manuf. Technol. 1–7 (2013)Google Scholar