Abstract
BackgroundTechnological advances in medicine have led to a rapid proliferation of high-throughput “omics” data. Tools to mine this data and discover disrupted disease networks are needed as they hold the key to understanding complicated interactions between genes, mutations and aberrations, and epi-genetic markers.ResultsWe developed an R software package, XMRF, that can be used to fit Markov Networks to various types of high-throughput genomics data. Encoding the models and estimation techniques of the recently proposed exponential family Markov Random Fields (Yang et al., 2012), our software can be used to learn genetic networks from RNA-sequencing data (counts via Poisson graphical models), mutation and copy number variation data (categorical via Ising models), and methylation data (continuous via Gaussian graphical models).ConclusionsXMRF is the only tool that allows network structure learning using the native distribution of the data instead of the standard Gaussian. Moreover, the parallelization feature of the implemented algorithms computes the large-scale biological networks efficiently. XMRF is available from CRAN and Github (https://github.com/zhandong/XMRF).
Highlights
Technological advances in medicine have led to a rapid proliferation of high-throughput “omics” data
We introduce an R software package, XMRF, that encodes models and estimation techniques for fitting exponential family Markov Networks to high-throughput genomics data as well as software to pre-process genetic data and visualize the resulting genetic networks
To estimate the network structures from different types of high-throughput genomics data, our package consists of one main function, the XMRF() function, for which many families of distributions are possible, XMRF(..., method="GGM"): the GGM for family of Gaussian graphical models, the ISM for Ising models, Poisson graphical models (PGM), TPGM, SPGM, local Poisson graphical model (LPGM) for Poisson families of models as described in [8, 10]
Summary
We developed an R software package, XMRF, that can be used to fit Markov Networks to various types of high-throughput genomics data. Encoding the models and estimation techniques of the recently proposed exponential family Markov Random Fields (Yang et al, 2012), our software can be used to learn genetic networks from RNA-sequencing data (counts via Poisson graphical models), mutation and copy number variation data (categorical via Ising models), and methylation data (continuous via Gaussian graphical models)
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