Statistica Sinica 12(2002), 47-59

EXPLORATORY SCREENING OF GENES AND CLUSTERS

FROM MICROARRAY EXPERIMENTS

Robert Tibshirani$^1$, Trevor Hastie$^1$, Balasubramanian Narasimhan$^1$

Michael Eisen$^2$, Gavin Sherlock$^1$, Pat Brown$^1$ and David Botstein$^1$

$^1$Stanford University and $^2$University of California, Berkeley

Abstract: We discuss a method called ``cluster scoring'' for supervised learning from a set of gene expression experiments. Cluster scoring generalizes methods that rank individual genes based on their correlation with an outcome measure. It begins with a clustering of the genes, for example from hierarchical clustering, and then computes outcome scores both for individual genes and the average gene expression for each of the clusters. A permutation method is used to identify the significant subset of these scores. We illustrate the method on both simulated data, and data from a study of lymphoma.

Key words and phrases: Clustering, microarrays, supervised learning.