01931nas a2200193   4500000000100000008004100001100001100042700000900053700001200062700000900074700000900083700001000092245009900102856004500201300001000246490000600256520146100262022001401723       2016                            d1 aYuan Z1 aJi J1 aZhang X1 aXu J1 aMa D1 aXue F00aA powerful weighted statistic for detecting group differences of directed biological networks.  uhttp://www.nature.com/articles/srep34159  a341590 v63 a<p>Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. Different physiological conditions such as cases and controls may manifest as different networks. Statistical comparison between biological networks can provide not only new insight into the disease mechanism but statistical guidance for drug development. However, the methods developed in previous studies are inadequate to capture the changes in both the nodes and edges, and often ignore the network structure. In this study, we present a powerful weighted statistical test for group differences of directed biological networks, which is independent of the network attributes and can capture the changes in both the nodes and edges, as well as simultaneously accounting for the network structure through putting more weights on the difference of nodes locating on relatively more important position. Simulation studies illustrate that this method had better performance than previous ones under various sample sizes and network structures. One application to GWAS of leprosy successfully identifies the specific gene interaction network contributing to leprosy. Another real data analysis significantly identifies a new biological network, which is related to acute myeloid leukemia. One potential network responsible for lung cancer has also been significantly detected. The source R code is available on our website.</p>
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