The complexity of the statistical analysis with regard to immunomic microarray data is on a whole different level than that of genomic microarray data. The total number of genes in humans is estimated to be ;30,000; in comparison, the total number of different T cell receptors in humans, generated by somatic recombination, which recognize peptides within the context of a major histocompatibility molecule, is estimated to be on the order of 10 7 to 10 15 , and the number of B cell clonotypes, generated by somatic recombination of V(D)J genes, is estimated to be on the order of 10 12 . DNA is based on a four-letter ''alphabet,'' consisting of the four nucleotide bases A,G,C,T, whereas peptide epitopes are based on a 20-letter alphabet, consisting of the amino acids known to be involved in life processes. Clearly, the combinatorial complexity in the case of functional immunomics is several orders of magnitude higher than that of functional genomics. In addition, for functional genomics the number of interrogative features that need to be built on microarrays is on the order of 10 4 to 10 5 . In functional immunomics, the total number of interrogative features included in microarray analysis can be much larger and may be estimated as follows, in the case of peptide-MHC microarrays: analysis of MHC peptide-binding motifs [26] suggests that a core of nine amino acids within a peptide is sufficient for characterization of a T cell epitope. The total number of interrogative features would thus correspond to the number of possible nine-letter words based on a 20-letter alphabet, which is 20 9 ' 10 11 ; fortunately, only ,1% of these are able to bind to MHC molecules, which makes the number of interrogative features more manageable. In addition, B cells and T cells go through a process of clonal selection, where leukocytes that either do not react or react too strongly are eliminated, and dangerous clones that react with self antigens are deleted or anergized as part of the process of immune tolerance. The number of features is still very large, however, and methods to further reduce this number are essential; such methods include the prediction methods and immunomic databases mentioned previously in connection with epitope mapping, as well as the selection of peptides from specific genomes of pathogens, allergens, and self-antigens involved in human infirmities, such as tumor antigens, diabetes, and autoimmune diseases.