Supervised Gene Clustering with Penalized Logistic Regression

Marcel Dettling and Peter Bühlmann

May 2003

Abstract: 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 co-regulated genes whose collective expression is strongly associated with an outcome variable of interest. To find these groups, we suggest the use of supervised clustering algorithms: these are procedures which use external information about the response variables for clustering the genes. We present Pelora, an algorithm based on penalized logistic regression analysis, that combines gene selection, supervision, gene clustering and sample classification in a single step. With an empirical study on six different microarray datasets, we show that Pelora identifies gene clusters whose expression centroids have excellent predictive potential and yield results that are superior to state-of-the-art classification methods based on single genes. Thus, our clusters can be beneficial in medical diagnostics and prognostics, but they can also be very useful for functional genomics by providing insights into gene function and regulation.

Download: Compressed Postscript (115 Kb) / PDF (258 Kb).