Mass distributed clustering: a new algorithm for repeated measurements in gene expression data

Abstract

The availability of whole-genome sequence data and high-throughput techniques such as DNA microarray enable researchers to monitor the alteration of gene expression by a certain organ or tissue in a comprehensive manner. The quantity of gene expression data can be greater than 30,000 genes per one measurement, making data clustering methods for analysis essential. Biologists usually design experimental protocols so that statistical significance can be evaluated; often, they conduct experiments in triplicate to generate a mean and standard deviation. Existing clustering methods usually use these mean or median values, rather than the original data, and take significance into account by omitting data showing large standard deviations, which eliminates potentially useful information. We propose a clustering method that uses each of the triplicate data sets as a probability distribution function instead of pooling data points into a median or mean. This method permits truly unsupervised clustering of the data from DNA microarrays.