Abstract
An unsupervised data clustering method, called the local maximum clustering (LMC) method, is proposed for identifying clusters in experiment data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distribution. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchic clustering method, the -mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al. (1999).
Highlights
Data analysis is a key step in obtaining information from large-scale gene expression data
As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al [12]
This work proposed the local maximum clustering (LMC) method and evaluated its performance as compared with some typical clustering methods through designed model data sets. This clustering method is an unsupervised one and can generate hierarchic cluster structures with minimum input. It allows a magnitude property of research interest to be chosen for clustering
Summary
Data analysis is a key step in obtaining information from large-scale gene expression data. Many analysis methods and algorithms have been developed for the analysis of the gene expression matrix [1, 2, 3, 4, 5, 6, 7, 8, 9]. A reasonable hypothesis is that genes with similar expression profiles, that is, genes that are coexpressed, may have something in common in their regulatory mechanisms, that is, they may be coregulated. By clustering together genes with similar expression profiles, one can find groups of potentially coregulated genes and search for putative regulatory signals. They can be divided into two categories: supervised and unsupervised methods. Some widely used methods in this category are the hierarchic clustering method [6], the K-mean clustering method [10], and the self-organized map clustering method [9, 11]
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