LIANA: a LIgand-receptor ANalysis frAmework

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.

Tutorials & Documentation

LIANA's basic tutorial in dissociated single-cell data
LIANA with MOFA. Using MOFA to infer intercellular communication programmes across samples and conditions, as initially proposed by cell2cell-Tensor
Multicellular programmes with MOFA. Using MOFA to obtain coordinates gene expression programmes across samples and conditions, as done in Ramirez et al., 2023
LIANA with cell2cell-Tensor to extract intercellular communication programmes across samples and conditions. Extensive tutorials combining LIANA & cell2cell-Tensor are available here.
We also refer users to the Cell-cell communication chapter in the best-practices guide from Theis lab. There we provide an overview of the common limitations and assumptions in CCC inference from (dissociated single-cell) transcriptomics data.

For further information please check LIANA's API documentation.

Install LIANA

Install liana's stable version:

pip install liana

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

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

Methods

The methods implemented in this repository are:

CellPhoneDBv2
NATMI
Connectome
SingleCellSignalR
CellChat (+)
1-vs-rest expression LogFC score
Geometric Mean - ligand-receptor geometric mean with pvalues obtained via the permutation approach implemented by CellPhoneDBv2
rank_aggregate of the predictions calculated with the RobustRankAggregate method

(+) A resource-independent adaptation of the CellChat LR inference functions.

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 (#)

($) 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!