lionessR: single sample network inference in R

Abstract

BackgroundIn 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 LIONESS was originally made available as a function within the PANDA (Passing Attributes between Networks for Data Assimilation) regulatory network reconstruction framework.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 describe lionessR, 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 of lionessR to 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 developed lionessR, an open source R package to model single sample networks. We show how lionessR can be used to inform us on potential precision medicine applications in cancer. The lionessR package 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/lionessR and at: http://bioconductor.org/packages/lionessR.

dyngen: a multi-modal simulator for spearheading new single-cell omics analyses

“…By default, LIONESS uses PANDA [42] to infer GRNs, but since dyngen does not produce motif data and motif data is required by PANDA, PANDA is inapplicable in this context. Instead, we used the lionessR [43] implementation of LIONESS, which uses by default the Pearson correlation as a NI method. We marked results from this implementation as “LIONESS + Pearson”.…”

New Cross-Talks between Pathways Involved in Grapevine Infection with ‘Candidatus Phytoplasma solani’ Revealed by Temporal Network Modelling

“…The purpose of network reconstruction is to explore higher-order feature (gene) interactions, attempting to overcome the independence assumption adopted many times in conventional statistical analysis. The field of network reconstruction has shown great promise for the analysis of biological data sets; namely, the methods such as LionessR [ 28 ], RAVEN [ 29 , 30 ], WGCNA and community-based reconstruction [ 31 ] were shown to accurately reconstruct yeast and human metabolic networks, offering novel insights into the space of potentially interesting biomarkers and new pathways.…”

Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine

“…LIONESS is an approach developed by Kuijjer et al in the context of gene regulatory networks. 14 , 18 …”

“…33 Our R implementations for LIONESS and ssPCC are available at under the software tab. Original R package for LIONESS by Kuijjer et al ( 18 ) can be also obtained at github.com/kuijjerlab/lionessR and bioconductor.org/packages/lionessR .…”