OverDBC: A new density-based clustering method with the ability of detecting overlapped clusters from gene expression data

Abstract

Using microarray techniques, it is possible to measure the expression levels of thousands of genes under several experimental conditions. Extracting information from microarray data is an important problem in Bioinformatics scope. Producing overlapping clusters is a major issue in clustering methods. While most of the research in this area has focused on clustering using disjoint cluster, many real microarray datasets and as a result many gene regulatory networks have inherently overlapping partitions. Genes have more than one function by coding for proteins that participate in multiple metabolic pathways. So, Overlapped clusters have an important role in discovering the relationship between genes and finding overlap gene regulatory networks. Recent proposed clustering methods rely on the search of optimal disjoint clusters. In this paper, we propose a new density based clustering (OverDBC) with a bound on the number of overlap clusters. OverDBC allows genes membership in a restricted number of clusters where the total number of clusters is unbounded. We define closeness as a new concept for finding core genes along with the density concept. We compare OverDBC with DBscan (a non-overlapping density-based clustering) algorithm. We prove that OverDBC may be significantly better than non-overlapping clustering in microarray data.