Network-enabled gene expression analysis

摘要: Although genome-scale expression experiments are performed routinely in biomedical research, methods of analysis remain simplistic and their interpretation challenging. The conventional approach is to compare the each gene, one at a time, between treatment groups. This implicitly treats gene levels as independent, but they fact highly interdependent, exploiting this enables substantial power gains be realized. We assume that information on dependence structure set genes available form Bayesian network (directed acyclic graph), derived from external resources. show how analyze data conditional network. Genes whose directly affected by may identified using tests for independence treatment, parents apply two datasets: hepatotoxicity study rats PPAR pathway, other effects smoking epithelial transcriptome, global transcription factor proposed method straightforward, simple implement, gives rise gains, assist relating experimental results underlying biology.