Abstract
Genes that are expressed in the same subset of cells potentially constitute a module regulated by shared cis-regulatory elements and a distinct set of transcription factors. Identifying such units is an important entry point to the molecular study of cell differentiation. We developed a general method to classify cell type-specific genes from expressed sequence tag (EST) data, and we optimized it for identification of smooth muscle cell (SMC)-specific genes. Expression profiles were derived from the quantitative distribution of EST data in mouse, and genes were classified based on their profile similarity to known reference genes, in this case smooth muscle myosin heavy chain. A large majority (>90%) of known SMC-specific genes were identified, together with novel candidates. Extensive experimental validation confirmed SMC-specific expression of candidates, for example, lipoma preferred partner (LPP) and a novel SMC-specific putative monoamine oxidase, SMAO. Our method performed considerably better than other computational methods in an objective cross validation comparison. The total number of SMC-specific genes is estimated to be approximately 50.