LionessR: single sample network inference in R

Marieke L Kuijjer,Kimberly Glass,Ping-Han Hsieh,John Quackenbush

doi:10.1186/s12885-019-6235-7

Marieke L Kuijjer, Kimberly Glass + Show 2 more

Open Access

https://doi.org/10.1186/s12885-019-6235-7

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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 LIONESS was originally made available as a function within the PANDA (Passing Attributes between Networks for Data Assimilation) regulatory network reconstruction framework.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/lionessRand at: http://bioconductor.org/packages/lionessR.

Highlights

In 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
While We recently developed LIONESS, or Linear Interpolagene expression profiles give us a snapshot of the state tion to Obtain Network Estimates for Single Samples [12], of a cell or tissue, network inference algorithms give an as a way of using population-level networks to estimate estimate of the extent to which genes or gene products the corresponding network in each individual sample
Application of lionessR to a bone cancer dataset As an example, we performed an analysis applying lioness() to a gene expression dataset from 53 high-grade osteosarcoma biopsies [16] (Gene Expression Omnibus accession number GSE42352), which is included with the package

Summary

Results

Application of lionessR to a bone cancer dataset As an example, we performed an analysis applying lioness() to a gene expression dataset from 53 high-grade osteosarcoma biopsies [16] (Gene Expression Omnibus accession number GSE42352), which is. We performed a differential correlation network analysis comparing shortversus long-term metastasis-free survival (MFS) to understand co-regulation differences between the groups and to search for potential therapeutic targets. For this demonstration, we separated patients into two groups based on those who developed metastases within five years (n = 19) and those who did not (n = 34). STAT1 is a transcription factor in the interferon signaling pathway—a pathway known to be involved in osteosarcoma, and for which targeted treatment options are available [22] This indicates that individual patient correlation network analysis with lionessR can pinpoint potential candidates for personalized medicine. We previously identified differential gene regulation in the absence of differential expression by analyzing LIONESS

Conclusions

Background