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LIANA - a LIgand-receptor ANalysis frAmework

Project description

LIANA: a LIgand-receptor ANalysis frAmework

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liana-py is still under heavy development - a stable alpha release will be created soon

LIANA is a Ligand-Receptor inference framework that enables the use of any LR method with any resource. This is its faster and memory efficient Python implementation, an R version is also available here.

For further information please check LIANA's documentation, and also tutorial.

Install LIANA

Install liana's most up-to-date version:

pip install git+https://github.com/saezlab/liana-py

Install liana's stable version:

pip install liana

Methods

The methods implemented in this repository are:

Ligand-Receptor Resources

The following CCC resources are accessible via this pipeline:

  • Consensus ($)
  • CellCall
  • CellChatDB
  • CellPhoneDB
  • Ramilowski2015
  • Baccin2019
  • LRdb
  • Kiroauc2010
  • ICELLNET
  • iTALK
  • EMBRACE
  • HPMR
  • Guide2Pharma
  • ConnectomeDB2020
  • CellTalkDB
  • MouseConsensus (#)

($1) LIANA's default Consensus resource was generated from several expert-curated resources, filtered to additional quality control steps including literature support, complex re-union/consensus, and localisation. (#) Consensus Resource converted to murine homologs.

Cite LIANA:

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 Also, if you use the OmniPath CCC Resource for your analysis, please cite:

Türei, D., Valdeolivas, A., Gul, L., Palacio‐Escat, N., Klein, M., Ivanova, O., Ölbei, M., Gábor, A., Theis, F., Módos, D. and Korcsmáros, T., 2021. Integrated intra‐and intercellular signaling knowledge for multicellular omics analysis. Molecular systems biology, 17(3), p.e9923. https://doi.org/10.15252/msb.20209923

Similarly, please consider citing any of the methods and/or resources implemented in liana, that were particularly relevant for your research!

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