RT Journal Article
SR Electronic
T1 fastBMA: Scalable Network Inference and Transitive Reduction
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 099036
DO 10.1101/099036
A1 Ling-Hong Hung
A1 Kaiyuan Shi
A1 Migao Wu
A1 William Chad Young
A1 Adrian E. Raftery
A1 Ka Yee Yeung
YR 2017
UL http://biorxiv.org/content/early/2017/01/06/099036.abstract
AB 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/).AUCarea under the curveAUROCarea under receiver operator curveAUPRarea under precision recallBMABayesian model averagingiBMAiterative Bayesian model averagingBICBayesian information criterionEMEstimation Maximization