Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

Abstract

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which combined can deploy multiple existing methods and resources, to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this protocol, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step-by-step in both Python and R, and we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This protocol typically takes ~1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of ~63k cells, 10 cell types, and 12 samples.

Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

“…While our proposed approach enables the inference of tissue-level coordinated responses across cell types in distinct contexts, the connection of these processes to cell-cell communication events remains an open challenge. Applications of group factor analysis with MOFA including views measuring the co-expression of ligands and receptors from pairs or groups of cells to infer cell-cell communication programs are possible, analogous to the work of Armingol et al, 2022 ; Baghdassarian et al, 2023 , as shown in the tutorials of our cell-cell communication tool LIANA+ ( Dimitrov et al, 2023 ) ( https://liana-py.readthedocs.io/en/latest/notebooks/mofatalk.html ). Alternatively, the estimation of multicellular programs could be further used to inform the inference of mechanistic network models connecting inter- and intra-cellular signaling events.…”

Sctensor detects many-to-many cell–cell interactions from single cell RNA-sequencing data

“…Third, there are differences in the related tools. Tensor-cell2cell assumes their text file for input, it only supports major species such as mouse and human, and it seems to assume to be used with LIANA [72,73], another CCI tool of the authors. On the other hand, scTensor supports 124 species (September 5, 2024) in the Bioconductor package LRBase, and can be combined with various other single-cell packages via the SingleCellExperiment object and Seurat (see Implementations and Fig.…”

LIANA+: an all-in-one cell-cell communication framework

“…For cross-conditional, dissociated single-cell data, LIANA+ leverages higher-order dimensionality reduction approaches to decompose CCC events into intercellular programs, as previously demonstrated with Tensor-cell2cell 32,50 . Besides Tensor-cell2cell, we propose an alternative unsupervised approach that leverages the MOFA+ multi-view framework (Methods) 51 .…”