Pattern discovery in expression profiling data.

In expression profiling studies, it is often necessary to identify groups of genes with similar expression profiles in a variety of samples, and/or groups of samples with similar expression profiles. Each profile can be expressed as a single data point in a space with the same number of dimensions as there are parameters in the profiles. In this way, pattern discovery among expression profiles is translated into pattern discovery in the spatial distribution of data points. Hierarchical clustering is useful for clustering similarly behaving genes or samples at local levels and for displaying the results in a simple color-coded manner. K-means clustering can be used for discovery of well-defined clusters. Principal component analysis and self-organizing maps can be used for dimensionality reduction, thereby facilitating visualization of major trends in data sets.