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 cell2cell-Tensor to obtain intercellular communication programmes across samples and conditions
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

For further information please check LIANA's API documentation.

We also refer users to the Cell-cell communication chapter in the best-practices book from Theis lab, as it provides an overview of the common limitations and assumptions in CCC inference from (dissociated single-cell) transcriptomics data.

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!

schae211 / liana-py