LagCI Enables Inference of Temporal Causal Relationships from Dense Multi-Omic Time Series

Inferring causal relationships from time-series data is critical for uncovering the dynamics of biological regulation. However, in multi-omics studies, this task is often hampered by sparse temporal sampling and the limitations of existing methods. To address this, we developed Lagged-Correlation Based Causal Inference (lagCI), a computational framework designed to identify time-lagged associations by combining comprehensive lag-correlation profiling with a robust statistical filtering scheme. Rather than relying on simple cross-correlation, lagCI analyzes the entire correlation profile and applies a quality-scoring system to filter out spurious associations that often plague high-dimensional datasets.

We first tested lagCI on wearable physiological data, where it successfully captured the well-known causal link between physical activity and heart rate, even accounting for variations in lag times between individuals. Moving to high-frequency human multi-omics, we used lagCI to build a directed network of 1,624 molecules connected by over 157,000 predicted interactions. This network didn’t just mirror established biology (such as cytokine-hormone crosstalk); it also pointed to specific molecular hubs that seem to orchestrate the timing of metabolic and immune responses.

Overall, lagCI provides a data-driven way to extract temporal insights from dense longitudinal omics. We’ve made the tool available as an R package with multiple interfaces to ensure it’s accessible for both bioinformaticians and clinicians.