AbstractBackgroundIn biomedical research, network inference algorithms are typically used to infer complex association patterns between biological entities, such as between genes or proteins, using data from a population. This resulting aggregate network, in essence, averages over the networks of those individuals in the population. LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) is a method that can be used together with a network inference algorithm to extract networks for individual samples in a population. The method’s key characteristic is that, by modeling networks for individual samples in a data set, it can capture network heterogeneity in a population. LIONESS was originally made available as a function within the PANDA (Passing Attributes between Networks for Data Assimilation) regulatory network reconstruction framework. However, the LIONESS algorithm is generalizable and can be used to model single sample networks based on a wide range of network inference algorithms.ResultsIn this software article, we describelionessR, an R implementation of LIONESS that can be applied to any network inference method in R that outputs a complete, weighted adjacency matrix. As an example, we provide a vignette of an application oflionessRto model single sample networks based on correlated gene expression in a bone cancer dataset. We show how the tool can be used to identify differential patterns of correlation between two groups of patients.ConclusionsWe developedlionessR, an open source R package to model single sample networks. We show howlionessRcan be used to inform us on potential precision medicine applications in cancer. ThelionessRpackage is a user-friendly tool to perform such analyses. The package, which includes a vignette describing the application, is freely available at:https://github.com/kuijjerlab/lionessRand at:http://bioconductor.org/packages/lionessR.