Fast Bayesian Inference for Gene Regulatory Networks Using ScanBMA

Overview

Journal BMC Syst Biol

Publisher Biomed Central

Specialty Biology

Date 2014 Apr 19

PMID 24742092

Citations 38

Authors

William Chad Young

Adrian E Raftery

Ka Yee Yeung

Affiliations

Soon will be listed here.

Abstract

Background: Genome-wide time-series data provide a rich set of information for discovering gene regulatory relationships. As genome-wide data for mammalian systems are being generated, it is critical to develop network inference methods that can handle tens of thousands of genes efficiently, provide a systematic framework for the integration of multiple data sources, and yield robust, accurate and compact gene-to-gene relationships.

Results: We developed and applied ScanBMA, a Bayesian inference method that incorporates external information to improve the accuracy of the inferred network. In particular, we developed a new strategy to efficiently search the model space, applied data transformations to reduce the effect of spurious relationships, and adopted the g-prior to guide the search for candidate regulators. Our method is highly computationally efficient, thus addressing the scalability issue with network inference. The method is implemented as the ScanBMA function in the networkBMA Bioconductor software package.

Conclusions: We compared ScanBMA to other popular methods using time series yeast data as well as time-series simulated data from the DREAM competition. We found that ScanBMA produced more compact networks with a greater proportion of true positives than the competing methods. Specifically, ScanBMA generally produced more favorable areas under the Receiver-Operating Characteristic and Precision-Recall curves than other regression-based methods and mutual-information based methods. In addition, ScanBMA is competitive with other network inference methods in terms of running time.

Citing Articles

Effect of model space priors on statistical inference with model uncertainty.

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From time-series transcriptomics to gene regulatory networks: A review on inference methods.

Marku M, Pancaldi V PLoS Comput Biol. 2023; 19(8):e1011254.

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SIGNET: Transcriptome-wide Causal Inference for Gene Regulatory Networks.

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Improving gene regulatory network inference and assessment: The importance of using network structure.

Escorcia-Rodriguez J, Gaytan-Nunez E, Hernandez-Benitez E, Zorro-Aranda A, Tello-Palencia M, Freyre-Gonzalez J Front Genet. 2023; 14:1143382.

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