Research

'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns

Trevor Hastie^1,2, Robert Tibshirani^2,1 , Michael B Eisen³, Ash Alizadeh⁴, Ronald Levy⁵, Louis Staudt⁶, Wing C Chan⁷, David Botstein⁸ and Patrick Brown⁴

¹  Department of Statistics, Sequoia Hall, Stanford University, Stanford, CA 94305, USA

²  Department of Health Research and Policy, Sequoia Hall, Stanford University, Stanford, CA 94305, USA

³  Life Sciences Division, Lawrence Orlando Berkeley National Laboratories, and Department of Molecular and Cell Biology, University of California, Berkeley, CA 94305, USA

⁴  Department of Biochemistry, Stanford University, Stanford, CA 94305, USA

⁵  Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA

⁶  Metabolism Branch, DCS, National Cancer Institute, Bethesda, MD 20892, USA

⁷  Department of Pathology, University of Nebraska Medical Center, Omaha, NE 68198, USA

⁸  Department of Genetics, Stanford University, Stanford, CA 94305, USA

author email corresponding author email

Genome Biology 2000, 1:research0003.1-0003.21doi:10.1186/gb-2000-1-2-research0003

Published:	4 August 2000

Subject areas: Bioinformatics, Genome studies, Methods, Cancer, Medicine

Abstract

Background

Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings.

Results

We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival.

Conclusions

The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.