Expression Clustering

Abstract

Clustering of genes and samples is a powerful means to discover biological patterns within a gene expression data set. Clustering of genes enables discovery of co-expressed gene modules, which is useful in revealing co-regulated genes. Clustering of samples enables discovery of cell types, patient groupings, and developmental trajectories of cells. A variety of clustering algorithms and software packages have been developed for gene expression clustering. This article aims to provide an overview of key clustering methods, and demonstrate their practical applications in bioinformatics.