RESUMO
A supervised classification scheme for analyzing microarray expression data, based on the k-nearest-neighbor method coupled to noise-reduction filters, has been used to find genes involved in the osteogenic pathway of the mouse C2C12 cell line studied here as a model for in vivo osteogenesis. The scheme uses as input a training set embodying expert biological knowledge, and provides internal estimates of its own misclassification errors, which furthermore enables systematic optimization of the classifier parameters. On the basis of the C2C12-generated expression data set with 34,130 expression profiles across 2 time courses, each comprised of 6 points, and a training set containing known members of the osteogenic, myoblastic, and adipocytic pathways, 176 new genes in addition to 28 originally in the training set are selected as relevant to osteogenesis. For this selection, the estimated sensitivity is 42% and the posterior false-positive rate (fraction of candidates that are spurious) is 12%. The corresponding sensitivity and false-positive rate for detection of myoblastic genes are 9% and 31%, respectively, and only 4% and approximately 100%, respectively, for adipocytic genes, in accordance with an experimental design that predominantly stimulated the osteogenic pathway. Validation of this selection is provided by examining expression of the genes in an independent biological assay involving mouse calvaria (skull bone) primary cell cultures, in which a large fraction of the 176 genes are seen to be strongly regulated, as well as by case-by-case analysis of the genes on the basis of expert domain knowledge. The methodology should be generalizable to any situation in which enough a priori biological knowledge exists to define a training set.
