Identification of activated transcription factors from microarray gene expression data of Kampo medicine-treated mice.

We propose an approach to identify activated transcription factors from gene expression data using a statistical test. Applying the method, we can obtain a synoptic map of transcription factor activities which helps us to easily grasp the system's behavior. As a real data analysis, we use a case-control experiment data of mice treated by a drug of Kampo medicine remedying degraded myelin sheath of nerves in central nervous system. Kampo medicine is Japanese traditional herbal medicine. Since the drug is not a single chemical compound but extracts of multiple medicinal herb, the effector sites are possibly multiple. Thus it is hard to understand the action mechanism and the system's behavior by investigating only few highly expressed individual genes. Our method gives summary for the system's behavior with various functional annotations, e.g. TFAs and gene ontology, and thus offer clues to understand it in more holistic manner.