In recent years, the data-driven inference of cell-cell communication (CCC), specifically when using single-cell transcriptomics data, has enabled the study of coordinated biological processes across cell types. Yet, as the capabilities to generate large single-cell and spatial transcriptomics datasets continue to increase, together with the interest in studying intercellular programmes, the need to easily and robustly decipher CCC is essential. Here, we integrate our tools, LIANA and Tensor-cell2cell, to enable identification of intercellular programmes across multiple samples and contexts. We show how our unified framework facilitates the choice of method to infer cell-cell communication and the application of factor analysis to obtain and summarize biological insights.
Quickstart and step-by-step tutorials in both Python and R are available here.
Tensor-cell2cell is available at: https://github.com/earmingol/cell2cell
LIANA is available in:
Don't hesitate to open issues to this github repository and also refer to the FAQ section of the documentation.
To cite this work: Baghdassarian, H. M., Dimitrov, D., Armingol, E., Saez-Rodriguez, J. & Lewis, N. E. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. Cell Reports Methods, 4(4), 100758 (2024).
To cite LIANA: Dimitrov, D., Türei, D., Garrido-Rodriguez, M., Burmedi, P.L., Nagai, J.S., Boys, C., Ramirez Flores, R.O., Kim, H., Szalai, B., Costa, I.G. and Valdeolivas, A. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nature Communications, 13(1), p.3224 (2022).
To cite Tensor-cell2cell: Armingol, E., Baghdassarian, H.M., Martino, C., Perez-Lopez, A., Aamodt, C., Knight, R. and Lewis, N.E. Context-aware deconvolution of cell–cell communication with Tensor-cell2cell. Nature Communications, 13(1), p.3665 (2022).