recon 0.0.2

ReCoN: [Reconstruction of multicellular systems from single-cell data to predict perturbation responses and cell programs coordination]

ReCoN is a new tool for reconstructing multicellular models.

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).

💡 Philosophy behind ReCoN
🧬 Cells do not act in isolation, but in a coordinated, dynamic system.

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🚀 Use cases

• Predicting treatment effects in multicellular systems
• Understanding multicellular program coordination
• Exploring intracellular and intercellular regulatory mechanisms
• Building GRNs through HuMMuS methodology

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📦 Installation

ReCoN is available as a Python package and can be installed through pip.

conda create -n recon python=3.10
conda activate recon
git clone https://github.com/cantinilab/ReCoN.git
pip install "./ReCoN[grn-lite]"
# pip install recon[grn-lite]

⚠️ To generate GRNs, ReCoN requires CellOracle and HuMMuS.
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:

pip install git+https://github.com/cantinilab/ReCoN.git
# pip install recon

📖 For installation issues, dependency conflicts, or runtime errors,
please check our dedicated Troubleshooting and FAQs guide.

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💊 Treatment effects on multicellular systems

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 restart (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.
(Trimbour et al., 2025 — Immune Dictionary and Heart Failure showcases)

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🧫 Multicellular program coordination

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.

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⚙️ Visualizing molecular cascades

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.

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🧬 Building GRNs with HuMMuS

HuMMuS (Trimbour et al., 2024) is a multilayer network method to build GRNs from single-cell RNA-seq and 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.

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📖 Citation

If you use ReCoN, please cite:

Trimbour R., Ramirez Flores R. O., Saez Rodriguez J., Cantini L. (2025).
ReCoN: Reconstructing multicellular models by integrating gene regulatory and cell communication networks.
bioRxiv. https://doi.org/_________

If you also use ReCoN to generate GRNs, cite:

Trimbour R., Ramirez Flores R. O., Saez Rodriguez J., Cantini L. (2025).
ReCoN: Reconstructing multicellular models by integrating gene regulatory and cell communication networks.
bioRxiv. https://doi.org/_________

Trimbour 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

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