CardamomOT¶
CardamomOT is a Python package for joint gene regulatory network (GRN) and cell trajectory inference from time-course single-cell RNA-seq data. It calibrates the parameters of a mechanistic model of gene expression — the calibrated network can then be simulated to reproduce and extrapolate the observed data.
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Three real-data applications.
Overview¶
CardamomOT combines optimal transport and mechanistic modelling to jointly infer:
Kinetic parameters (burst frequencies, degradation rates) from the marginal distributions at each time point
Gene regulatory interactions using an optimal-transport trajectory that aligns consecutive snapshots
Stochastic simulations of the inferred network for in-silico perturbation experiments
The package is described in:
Maugé Y. and Ventre E. (2026). CardamomOT: a mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling. doi: 10.64898/2026.03.31.715390