# XMRF: An R package to Fit Markov Networks to High-Throughput Genetics Data

## TL;DR

XMRF is an R package that helps analyze complex genetic interactions using different types of high-throughput genomics data.

## Contribution

XMRF introduces a flexible tool for fitting Markov networks to various omics data types using exponential family models.

## Key findings

- XMRF supports Poisson graphical models for RNA-sequencing count data.
- The package can model mutation and copy number variation data using Ising models.
- Methylation data can be analyzed using Gaussian graphical models in XMRF.

## Abstract

Technological 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.

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).

XMRF is available from the CRAN Project and Github at: https://github.com/zhandong/XMRF