Name
Title | ||
|---|---|---|
A Log-Linear Graphical Model for inferring genetic networks from high-throughput sequencing data |
Abstract | ||
|---|---|---|
Gaussian graphical models are often used to infer gene networks based on microarray expression data. Many scientists, however, have begun using high-throughput sequencing technologies to measure gene expression. As the resulting high-dimensional count data consists of counts of sequencing reads for each gene, Gaussian graphical models are not optimal for modeling gene networks based on this discrete data. We develop a novel method for estimating high-dimensional Poisson graphical models, the Log-Linear Graphical Model, allowing us to infer networks based on high-throughput sequencing data. Our model assumes a pair-wise Markov property: conditional on all other variables, each variable is Poisson. We estimate our model locally via neighborhood selection by fitting 1-norm penalized log-linear models. Additionally, we develop a fast parallel algorithm permitting us to fit our graphical model to high-dimensional genomic data sets. We illustrate the effectiveness of our methods for recovering network structure from count data through simulations and a case study on breast cancer microRNA networks. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/BIBM.2012.6392619 | Bioinformatics and Biomedicine |
Keywords | DocType | ISBN |
gaussian graphical model,genetic network,high-throughput sequencing data,count data,discrete data,log-linear graphical model,gene expression,high-dimensional count data,gene network,genomic data set,microarray expression data,graphical model,graphical models,bioinformatics,rna,genetics,gaussian processes,micrornas,genomics,markov processes,cancer | Conference | 978-1-4673-2558-5 |
Citations | PageRank | References |
8 | 0.95 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Genevera I. Allen | 1 | 89 | 11.18 |
| Zhandong Liu | 2 | 61 | 5.39 |