Supervised clustering of genes

Abstract

Background: We focus on microarray data where experiments monitor gene expression in different tissues and where each experiment is equipped with an additional response variable such as a cancer type. Although the number of measured genes is in the thousands, it is assumed that only a few marker components of gene subsets determine the type of a tissue. Here we present a new method for finding such groups of genes by directly incorporating the response variables into the grouping process, yielding a supervised clustering algorithm for genes.

Results: An empirical study on eight publicly available microarray datasets shows that our algorithm identifies gene clusters with excellent predictive potential, often superior to classification with state-of-the-art methods based on single genes. Permutation tests and bootstrapping provide evidence that the output is reasonably stable and more than a noise artifact.

Conclusions: In contrast to other methods such as hierarchical clustering, our algorithm identifies several gene clusters whose expression levels clearly distinguish the different tissue types. The identification of such gene clusters is potentially useful for medical diagnostics and may at the same time reveal insights into functional genomics.

Figure 1

Lymphoma data. Average cluster expression shaped for the separation of response class 1 (FL), versus response classes 0 and 2 (DLBCL and CLL) on the x-axis, and formed for discrimination of class 2 versus classes 0 and 1 on the y-axis.

Figure 2

Histograms showing the empirical distribution of scores (left) and margins (right) for the leukemia dataset (AML/ALL distinction), based on 1,000 bootstrap replicates with permuted response variables. The dashed vertical lines mark the values of score and margin with the original response variables.