Windowed Granger causal inference strategy improves discovery of gene regulatory networks.
Justin D FinkleJia J WuNeda BagheriPublished in: Proceedings of the National Academy of Sciences of the United States of America (2018)
Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information. We address this limitation by introducing Sliding Window Inference for Network Generation (SWING), a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays. We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene-gene interactions that are distinct from baseline methods. By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene-gene influences.
Keyphrases
- high throughput
- single cell
- genome wide
- copy number
- transcription factor
- electronic health record
- genome wide identification
- big data
- small molecule
- high resolution
- data analysis
- risk assessment
- dna methylation
- machine learning
- health information
- multidrug resistant
- gram negative
- genome wide analysis
- climate change
- human health
- loop mediated isothermal amplification