Granger causality
Definition
Granger causality is a statistical method for determining whether one time-series variable can predict another, establishing directional relationships in temporal data. Originally developed for econometrics, it tests if past values of variable X provide statistically significant information about future values of variable Y beyond what Y's own history provides. In biological systems, Granger causality helps infer regulatory relationships from time-series data like gene expression profiles, calcium imaging, or neural recordings. Unlike correlation, it captures temporal precedence and directional influence, making it valuable for constructing causal network models. However, it identifies predictive relationships rather than true mechanistic causality, requiring careful interpretation alongside domain knowledge and experimental validation.
Visualize Granger causality in Nodes Bio
Researchers can use Nodes Bio to visualize Granger causality networks derived from time-series omics data, displaying directed edges between genes, proteins, or metabolites where temporal precedence suggests regulatory influence. Node size can represent predictive strength, edge thickness can indicate statistical significance, and network clustering can reveal coordinated regulatory modules that respond sequentially to perturbations or developmental signals.
Visualization Ideas:
- Directed gene regulatory networks showing temporal precedence relationships from time-course expression data
- Neural connectivity maps displaying Granger-causal relationships between brain regions from fMRI or electrophysiology recordings
- Metabolic pathway flows illustrating predictive relationships between metabolite concentrations in dynamic flux analysis
Example Use Case
A systems biology team studying inflammatory response in macrophages collects RNA-seq data at multiple timepoints after LPS stimulation. They apply Granger causality analysis to identify which transcription factors precede and predict downstream cytokine expression. The resulting directed network reveals that STAT1 expression changes consistently precede IL-6 and TNF-α upregulation by 30-60 minutes, suggesting a regulatory cascade. This temporal ordering helps prioritize STAT1 as an early intervention target for modulating inflammatory responses in sepsis.