

This meansĬoNet is a network-based analysis tool, capable of finding non-random co-occurrence patterns in networks. If you disable this cookie, we will not be able to save your preferences. Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.

Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful. This website uses cookies so that we can provide you with the best user experience possible. This website uses cookies to provide you with the best browsing experience. If they are found to be significant, the objects can be used as input to further analysis. The objects in one or more samples can then be sorted based on their relevance to each other. In the ecology example, CoNet can help biologists to detect significant covariates that predict the incidence of diseases.ĬoNet allows a researcher to identify covariate variables that correspond to high risks of the incidences of diseases.ĬoNet will produce a high level graph that summarizes the significant relationships between objects in two or more samples.Ī statistical significance test can also be performed on the results. Its results can be used as input to further analysis.ĬoNet can sort objects in different samples based on their significance.ĬoNet can be used in a regression scenario as well as in an ecological scenario.ĬoNet can be applied to determine the significant predictors of one or more dependent variables. It has an ordering that allows it to simulate a random walk.ĬoNet can use transaction tables to group commonly correlated objects.ĬoNet can use Bonferroni-type and step-up type p-value significance tests.

It searches both up- and down-stream for new edges.ĬoNet is thus a global search algorithm in which object co-occurrences are investigated at any graph distance, and the search is driven by the most significant connections.ĬoNet searches the whole graph at a time. It uses a model-based filter in order to minimize false-positive and false-negative co-occurrences. It is an edge-weighted graph search algorithm. Specially built as a Cytoscape plugin, CoNet is able to detect significant non-random patterns of co-occurrence.ĬoNet can find both copresence and mutual exclusion patterns in incidence and abundance data.Īlthough it was designed with ecological data in mind, CoNet can be applied in general to infer relationships between objects observed in different samples.
