RT Journal
A1 Theilhaber, Joachim
A1 Connolly, Timothy
A1 Roman-Roman, Sergio
A1 Bushnell, Steven
A1 Jackson, Amanda
A1 Call, Kathy
A1 Garcia, Teresa
A1 Baron, Roland
T1 Finding Genes in the C2C12 Osteogenic Pathway by k-Nearest-Neighbor Classification of Expression Data
JF Genome Research 
JO Genome Research 
YR 2002 
FD January 01 
VO 12 
IS 1 
SP 165 
OP 176 
DO 10.1101/gr.182601 
UL http://genome.cshlp.org/content/12/1/165.abstract 
AB 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 ∼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.[Online supplementary material available at www.genome.org]