TDAC: co-expressed gene pattern finding using attribute clustering

Abstract

A number of clustering methods introduced for analysis of gene expression data for extracting potential relationships among the genes are studied and reported in this paper. An effective unsupervised method (TDAC) is proposed for simultaneous detection of outliers and biologically relevant co-expressed patterns. Effectiveness of TDAC is established in comparison to its other competing algorithms over six publicly available benchmark gene expression datasets in terms of both internal and external validity measures. Main attractions of TDAC are: (a) it does not require discretisation, (b) it is capable of identifying biologically relevant gene co-expressed patterns as well as outlier genes(s), (c) it is cost-effective in terms of time and space, (d) it does not require the number of clusters a priori, and (e) it is free from the restrictions of using any proximity measure.