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Article
fastBMA: Scalable Network Inference and Transitive Reduction
GigaScience
  • Ling-Hong Hung
  • Kaiyuan Shi
  • Migao Wu
  • William Chad Young
  • Adrian E. Raftery
  • Ka Yee Yeung, University of Washington Tacoma
Publication Date
10-1-2017
Document Type
Article
Abstract

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 computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series 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 (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. 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/).

DOI
10.1093/gigascience/gix078
Version
open access
Citation Information
Ling-Hong Hung, Kaiyuan Shi, Migao Wu, William Chad Young, et al.. "fastBMA: Scalable Network Inference and Transitive Reduction" GigaScience Vol. 6 Iss. 10 (2017)
Available at: http://works.bepress.com/ky-yeung/23/