A powerful weighted statistic for detecting group differences of directed biological networks.

摘要: Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than single biomolecule. Different physiological conditions such as cases and controls may manifest different networks. Statistical comparison between biological networks can provide not only new insight the mechanism but statistical guidance for drug development. However, methods developed in previous studies are inadequate to capture changes both nodes edges, often ignore network structure. In this study, we present powerful weighted test group differences directed which independent attributes well simultaneously accounting structure through putting more weights on difference locating relatively important position. Simulation illustrate that method had better performance ones under various sample sizes structures. One application GWAS leprosy successfully identifies specific gene interaction contributing leprosy. Another real data analysis significantly network, related acute myeloid leukemia. potential responsible lung cancer has also been detected. The source R code available our website.