Nov 18, 2015

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

BioRxiv : the Preprint Server for Biology
Ying-Wooi WanZhandong Liu

Abstract

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

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Mentioned in this Paper

Computer Software
Biological Markers
Protein Methylation
Genes
Projections and Predictions
Genetic Markers
Human RNA Sequencing
Genomics
Sequencing
Methylation

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