fastBMA: Scalable Network Inference and Transitive Reduction
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Abstract
BACKGROUND:
Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a novel and computationally efficient method for eliminating redundant indirect edges in the network.
FINDINGS:
We evaluated the performance of fastBMA on synthetic data and experimental genome-wide yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory efficient, parallel and distributed application that scales to human genome wide expression data. A 10,000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster.
CONCLUSIONS:
fastBMA is a significant improvement over its predecessor ScanBMA. It is orders of magnitude faster and more accurate than other fast network inference methods such as LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable timeframe. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub ( https://github.com/lhhunghimself/fastBMA ), as part of the updated networkBMA Bioconductor package ( https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html ) and as ready-to-deploy Docker images ( https://hub.docker.com/r/biodepot/fastbma/ ).
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Now published in GigaScience doi: 10.1093/gigascience/gix078
Ling-Hong Hung 1Institute of Technology, Box 358426, University of Washington, Tacoma, WA.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteKaiyuan Shi 1Institute of Technology, Box 358426, University of Washington, Tacoma, WA.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteMigao Wu 1Institute of Technology, Box 358426, University of Washington, Tacoma, WA.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteWilliam Chad Young 2Department of Statistics, Box 354320, University of Washington, Seattle, WA.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteAdrian E. Raftery 2Department of Statistics, Box …
Now published in GigaScience doi: 10.1093/gigascience/gix078
Ling-Hong Hung 1Institute of Technology, Box 358426, University of Washington, Tacoma, WA.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteKaiyuan Shi 1Institute of Technology, Box 358426, University of Washington, Tacoma, WA.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteMigao Wu 1Institute of Technology, Box 358426, University of Washington, Tacoma, WA.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteWilliam Chad Young 2Department of Statistics, Box 354320, University of Washington, Seattle, WA.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteAdrian E. Raftery 2Department of Statistics, Box 354320, University of Washington, Seattle, WA.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteKa Yee Yeung 1Institute of Technology, Box 358426, University of Washington, Tacoma, WA.Find this author on Google ScholarFind this author on PubMedSearch for this author on this site
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/gix078 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.100807 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.100809
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