Whole-genome expression analysis: challenges beyond clustering

Measuring the expression of most or all of the genes in a biological system raises major analytic challenges. A wealth of recent reports uses microarr ay expression data to examine diverse biological phenomena - from basic pro cesses in model organisms to complex aspects of human disease. After an ini tial flurry of methods for clustering the data on the basis of similarity, the field has recognized some longer-term challenges. Firstly, there are ef forts to understand the sources of noise and variation in microarray experi ments in order to increase the biological signal. Secondly, there are effor ts to combine expression data with other sources of information to improve the range and quality of conclusions that can be drawn. Finally, techniques are now emerging to reconstruct networks of genetic interactions in order to create integrated and systematic models of biological systems.