domains / families / science / temporal-grn

temporal-grn

separate

Infers directed, time-delayed gene regulatory edges from BULK time-series expression using Granger causality (statsmodels VAR F-test), dynGENIE3 (tree ensembles regressing ODE-derived derivatives; Random Forests by default, Extra-Trees optional), and dynamic Bayesian networks (bnlearn). Use when the output is a RANKED HYPOTHESIS list for perturbation validation, not validated causal edges; deciding Granger vs dynGENIE3 vs DBN by timepoint count and linearity; sizing maxlag against the n>3*maxlag+1 degrees-of-freedom floor; handling stationarity/differencing before Granger; restricting regulators to known TFs; and comparing network rewiring across conditions at matched edge density. Not for single-cell pseudotime GRNs (see gene-regulatory-networks/scenic-regulons) or static co-expression (see gene-regulatory-networks/coexpression-networks).

When to use

• Inferring directed, time-delayed gene regulatory edges
• Ranking TF->target edges from bulk temporal expression
• Prioritizing perturbation experiments
• Deciding Granger vs dynGENIE3 vs DBN
• Sizing maxlag against degrees-of-freedom floor
• Handling stationarity/differencing before Granger
• Restricting regulators to known TFs
• Comparing network rewiring across conditions

When not to use

• Single-cell pseudotime GRNs
• Static co-expression
• Validated causal edges

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