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A simple and robust algorithm for microarray data clustering based on gene population‐variance ratio metric
Author(s) -
Chatterjee Soumyadeep,
Bhattacharjee Kasturi,
Konar Amit
Publication year - 2009
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.200800219
Subject(s) - cluster analysis , data mining , metric (unit) , variance (accounting) , microarray analysis techniques , computer science , gene chip analysis , population , algorithm , microarray , biology , artificial intelligence , gene , genetics , gene expression , engineering , operations management , demography , accounting , sociology , business
With the advent of the microarray technology, the field of life science has been greatly revolutionized, since this technique allows the simultaneous monitoring of the expression levels of thousands of genes in a particular organism. However, the statistical analysis of expression data has its own challenges, primarily because of the huge amount of data that is to be dealt with, and also because of the presence of noise, which is almost an inherent characteristic of microarray data. Clustering is one tool used to mine meaningful patterns from microarray data. In this paper, we present a novel method of clustering yeast microarray data, which is robust and yet simple to implement. It identifies the best clusters from a given dataset on the basis of the population of the clusters as well as the variance of the feature values of the members from the cluster‐center. It has been found to yield satisfactory results even in the presence of noisy data.