The tools should provide methods for structure detection, model fitting, class prediction, and class assignment. The various methods must be evaluated against test and real data sets. Computational analysis of expression data can be broadly divided into class discovery and class prediciton. Clustering methods useful for class discovery include SOMs, hierarchical clustering, and mixture model clustering. Projection methods for class discovery include multidimensional scaling, principal component analysis and sammon maps.
Projection methods are useful as a complement to interpreting results based on other methods.
For class prediction the investigator starts with known and sees how well it fits unknown through classification and supervised learning. SOMs attempt to find relevant patterns, such that similar genes end up in the same cluster, and resulting in drastic data reduction. The data is first filtered to eliminate genes with no significant change across the samples. As an example, Dr. Tamayo presented clustering results on medulloblastoma and glioma - two clusters were chosen, and the spots clustered essentially into the two tumor types. In myeloid and lymphoid leukemia, two clusters also separated out the tumor types.