It combines both gene regulatory networks and cell communication networks to explore the molecular coordinations between multiple cell types — all at once.
ReCoN uses heterogeneous multilayer networks and integrates several layers of information into a complex network, ready to be explored and analyzed.
Both the GRNs and intercellular networks are inferred from single-cell RNA-seq data (and optionally scATAC-seq).
You can check our preprint here for more details! 😊
https://doi.org/10.64898/2026.01.20.700561
💡 Philosophy behind ReCoN
🧬 Cells do not act in isolation, but in a coordinated, dynamic system.
- Predicting treatment effects in multicellular systems
- Understanding multicellular program coordination
- Exploring intracellular and intercellular regulatory mechanisms
- Building GRNs through HuMMuS methodology
ReCoN is available as a Python package and can be installed through pip.
conda create -n recon python=3.10
conda activate recon
pip install recon[grn-lite]
Since CellOracle needs older dependencies, we recommend using our lite branch of CellOracle.
If you generate GRNs externally, install ReCoN without GRN dependencies to use newer Python versions:
# choose your favourite python version !
conda create -n recon python=3.12
pip install recon📖 For installation issues, dependency conflicts, or runtime errors,
please check our dedicated Troubleshooting and FAQ guide.
ReCoN predicts how a treatment (e.g., a drug) affects the molecular state of each cell type in a multicellular context (e.g., organ, tumor microenvironment).
It captures:
- Direct effects — treatment–receptor binding
- Indirect effects — through intercellular communication
Two components of treatment effect:
- Direct effect — caused by direct binding of receptors of a cell type
- Indirect effect — mediated by other cell types secreting ligands that modulate the focal cell
ReCoN models these with random walk with restarts (RWR).
The parameter α ∈ [0, 1] sets the weight of the direct effect (α) vs indirect effect (1-α).
Why indirect effects matter
Neighboring cells can secrete ligands in response to a treatment, altering signaling in the focal cell.
Our evaluation showed indirect effect dominance (α = 0.8) gave the best performance.
(in Trimbour et al., 2026 — Immune Dictionary and Heart Failure showcases)
How do surrounding cells regulate and get impacted by the state of a given cell type?
ReCoN highlights key molecules and cell types involved in coordination.
ReCoN reconstructs intercellular cascades driving specific transcriptomic states, including:
- Intracellular regulators (receptors, TFs)
- Intercellular signals (ligands and their regulators)
This provides a comprehensive view of regulation and helps identify new targets.
HuMMuS (Trimbour et al., 2024) is a multilayer network method to build GRNs from single-cell RNA-seq and single-cell ATAC-seq.
ReCoN integrates a Python implementation of HuMMuS, using CellOracle for prior TF–DNA–gene links.
The multilayer (TFs, DNA regions, target genes) is then processed to infer the final GRN.
If you use ReCoN, please cite:
Trimbour R., Ramirez Flores R. O., Saez Rodriguez J., Cantini L. (2026).
Modelling multicellular coordination by bridging cell-cell communication and intracellular regulation through multilayer networks.
bioRxiv. https://doi.org/10.64898/2026.01.20.700561
If you also use ReCoN to generate GRNs, cite:
Trimbour R., Ramirez Flores R. O., Saez Rodriguez J., Cantini L. (2026).
Modelling multicellular coordination by bridging cell-cell communication and intracellular regulation through multilayer networks.
bioRxiv. https://doi.org/10.64898/2026.01.20.700561Trimbour R., Deutschmann I. M., Cantini L. (2024).
HuMMuS: Inferring gene regulatory networks through heterogeneous multilayer networks.
Bioinformatics, 40(3), btae143. https://doi.org/10.1093/bioinformatics/btae143