bio-temporal-genomics-temporal-grn

Version Compatibility

Reference examples tested with: numpy 1.26+, pandas 2.2+, statsmodels 0.14+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Temporal Gene Regulatory Network Inference

"Infer causal regulatory relationships from my time-series expression data" → Identify time-delayed TF-target regulatory edges from bulk temporal expression using Granger causality testing, dynGENIE3 tree-based ODE inference, or dynamic Bayesian networks.

• Python: statsmodels.tsa.stattools.grangercausalitytests() for Granger causality
• R: dynGENIE3::dynGENIE3() for ODE-based GRN inference from time series

Infers directed, time-delayed regulatory relationships from bulk time-series expression data. Captures how transcription factor activity propagates through gene regulatory networks over time.

Core Workflow

1. Select candidate regulators (transcription factors) and target genes
2. Prepare lagged expression matrices from time-series data
3. Apply temporal inference method (Granger, dynGENIE3, or DBN)
4. Filter significant regulatory edges by statistical threshold
5. Compare networks across conditions or time windows

Granger Causality (Python/statsmodels)

Goal: Identify time-delayed regulatory relationships between transcription factors and target genes from time-series expression data.

Approach: Test pairwise Granger causality between TF-target pairs by checking whether past TF expression values improve prediction of future target levels, then correct for multiple testing across all tested pairs.

Tests whether past values of gene X improve prediction of gene Y beyond past values of Y alone. Significant Granger causality suggests X temporally influences Y.