LIANA+ is a scalable framework that integrates and extends existing methods and knowledge to study cell-cell communication in single-cell, spatially-resolved, and multi-modal omics data. It is part of the scverse ecosystem, and relies on AnnData & MuData objects as input.
We welcome suggestions, ideas, and contributions! Please use do not hesitate to contact us, open issues, and check the contributions guide.
A set of extensive vignettes can be found in the LIANA+ documentation.
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Q: Bivariate or unsupervised, multi-variate, and multi-view analysis?
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Bivariate:
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Q: Are you interested in identifying the subregions of interactions (i.e., local interactions)?
- Yes: Check the Local Bivariate Metrics
- No: Check the Global Bivariate Metrics
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Q: Are you interested in identifying the subregions of interactions (i.e., local interactions)?
- Unsupervised: Multi-view learning
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Bivariate:
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Q: Are you interested in comparing CCC across samples?
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Yes:
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Q: Are you interested in a specific contrast?
- Yes: Differential Contrasts and Downstream Signalling
- No: Unsupervised Cross-conditional LR inference with MOFA+ or Tensor-cell2cell
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Q: Are you interested in a specific contrast?
- No: Steady-state Ligand-Receptor inference
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Yes:
- Spatial: Integrating Multi-Modal Spatially-Resolved Technologies
- Non-Spatial: Integrating Multi-Modal Single-Cell Technologies
For further information please check LIANA's API documentation.
Dimitrov D., Schäfer P.S.L, Farr E., Rodriguez Mier P., Lobentanzer S., Badia-i-Mompel P., Dugourd A., Tanevski J., Ramirez Flores R.O. and Saez-Rodriguez J. LIANA+ provides an all-in-one framework for cell–cell communication inference. Nat Cell Biol (2024). https://doi.org/10.1038/s41556-024-01469-w
Dimitrov, D., Türei, D., Garrido-Rodriguez M., Burmedi P.L., Nagai, J.S., Boys, C., Flores, R.O.R., Kim, H., Szalai, B., Costa, I.G., Valdeolivas, A., Dugourd, A. and Saez-Rodriguez, J. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nat Commun 13, 3224 (2022). https://doi.org/10.1038/s41467-022-30755-0
Similarly, please consider citing any of the methods and/or resources implemented in liana, that were particularly relevant for your research!
