Microarray experiments generate large datasets with expression values for thousands of genes, but not more than a few dozens of samples. A challenging task with these data is to reveal groups of genes which act together and whose collective expression is strongly associated with an outcome variable of interest. To find these groups, we suggest the use of supervised algorithms: these are procedures which use external information about the response variable for grouping the genes. We present Pelora, an algorithm based on PEnalized LOgistic Regression Analysis, that combines gene selection, gene grouping and sample classification in a supervised, simultaneous way. With an empirical study on six different microarray datasets, we show that Pelora identifies gene groups whose expression centroids have very good predictive potential and yield results that can keep up with state-of-the-art classification methods based on single genes. Thus, our gene groups can be beneficial in medical diagnostics and prognostics, but they may also provide more biological insights into gene function and regulation.

Is available as an R-Package called supclust from CRAN. There is also a Windows binary version available.

The full results from our empirical study are available as PDF (100k) and PS (160k).

Our first ideas about finding predictive gene groups from microarray data were published under the heading Supervised Clustering of Genes. Further information is available here.