RT Journal Article
SR Electronic
T1 XMRF: An R package to Fit Markov Networks to High-Throughput Genetics Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 032219
DO 10.1101/032219
A1 Ying-Wooi Wan
A1 Genevera I. Allen
A1 Yulia Baker
A1 Eunho Yang
A1 Pradeep Ravikumar
A1 Zhandong Liu
YR 2015
UL http://biorxiv.org/content/early/2015/11/18/032219.abstract
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