PT  - JOURNAL ARTICLE
AU  - Ying-Wooi Wan
AU  - Genevera I. Allen
AU  - Yulia Baker
AU  - Eunho Yang
AU  - Pradeep Ravikumar
AU  - Zhandong Liu
TI  - XMRF: An R package to Fit Markov Networks to High-Throughput Genetics Data
AID  - 10.1101/032219
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 032219
4099  - http://biorxiv.org/content/early/2015/11/18/032219.short
4100  - http://biorxiv.org/content/early/2015/11/18/032219.full
AB  - Motivation 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.Results 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).Availability XMRF is available from the CRAN Project and Github at: https://github.com/zhandong/XMRF