Identification of direction in gene networks from expression and methylation View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-12

AUTHORS

David M Simcha, Laurent Younes, Martin J Aryee, Donald Geman

ABSTRACT

BACKGROUND: Reverse-engineering gene regulatory networks from expression data is difficult, especially without temporal measurements or interventional experiments. In particular, the causal direction of an edge is generally not statistically identifiable, i.e., cannot be inferred as a statistical parameter, even from an unlimited amount of non-time series observational mRNA expression data. Some additional evidence is required and high-throughput methylation data can viewed as a natural multifactorial gene perturbation experiment. RESULTS: We introduce IDEM (Identifying Direction from Expression and Methylation), a method for identifying the causal direction of edges by combining DNA methylation and mRNA transcription data. We describe the circumstances under which edge directions become identifiable and experiments with both real and synthetic data demonstrate that the accuracy of IDEM for inferring both edge placement and edge direction in gene regulatory networks is significantly improved relative to other methods. CONCLUSION: Reverse-engineering directed gene regulatory networks from static observational data becomes feasible by exploiting the context provided by high-throughput DNA methylation data.An implementation of the algorithm described is available at http://code.google.com/p/idem/. More... »

PAGES

118

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1752-0509-7-118

DOI

http://dx.doi.org/10.1186/1752-0509-7-118

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051434111

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24182195


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51 schema:description BACKGROUND: Reverse-engineering gene regulatory networks from expression data is difficult, especially without temporal measurements or interventional experiments. In particular, the causal direction of an edge is generally not statistically identifiable, i.e., cannot be inferred as a statistical parameter, even from an unlimited amount of non-time series observational mRNA expression data. Some additional evidence is required and high-throughput methylation data can viewed as a natural multifactorial gene perturbation experiment. RESULTS: We introduce IDEM (Identifying Direction from Expression and Methylation), a method for identifying the causal direction of edges by combining DNA methylation and mRNA transcription data. We describe the circumstances under which edge directions become identifiable and experiments with both real and synthetic data demonstrate that the accuracy of IDEM for inferring both edge placement and edge direction in gene regulatory networks is significantly improved relative to other methods. CONCLUSION: Reverse-engineering directed gene regulatory networks from static observational data becomes feasible by exploiting the context provided by high-throughput DNA methylation data.An implementation of the algorithm described is available at http://code.google.com/p/idem/.
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