Unsupervised decision trees structured by gene ontology (GO-UDTs) for the interpretation of microarray data

Abstract

Unsupervised data mining of microarray gene expression data is a standard approach for finding relevant groups of genes as well as samples. Clustering of samples is important for finding e.g. disease subtypes or related treatments. Unfortunately, most sample-wise clustering methods do not facilitate the biological interpretation of the results. We propose a novel approach for microarray samplewise clustering that computes dendrograms with Gene Ontology terms annotated to each node. These dendrograms resemble decision trees with simple rules which can help to find biologically meaningful differences between the sample groups. We have applied our method to a gene expression data set from a study of prostate cancer. The original clustering which contains clinically relevant features is well reproduced, but in addition our unsupervised decision tree rules give hints for a biological explanation of the clusters.