LIANA+ provides an all-in-one framework for cell-cell communication inference

Area of Science:

• Computational Biology
• Genomics
• Systems Biology

Background:

• Single-cell and spatial transcriptomics generate vast datasets for studying intercellular communication.
• Existing methods offer partial views of complex cell-cell signaling networks.
• A unified framework is needed to integrate diverse signaling mediators.

Purpose of the Study:

• To present LIANA+, a scalable computational framework for decoding coordinated intercellular and intracellular signaling events.
• To provide a comprehensive solution for analyzing cell-cell communication from single- and multi-condition transcriptomic data.
• To integrate diverse molecular mediators and multi-omics data for a holistic view of signaling.

Main Methods:

• LIANA+ is a scalable framework built upon an extensive knowledge base.
• It extends and unifies established methodologies for cell-cell communication inference.
• The framework analyzes both single-cell and spatially resolved transcriptomic data, including multi-condition datasets.

Main Results:

• LIANA+ enables the decoding of coordinated inter- and intracellular signaling.
• It supports the analysis of diverse molecular mediators, including those from multi-omics data.
• The framework provides a comprehensive, all-in-one solution for intercellular communication inference.

Conclusions:

• LIANA+ offers a powerful and scalable approach to decipher complex cell-cell communication networks.
• The framework enhances the study of intercellular signaling by integrating multiple data types and methods.
• LIANA+ serves as a valuable resource for researchers in computational biology and genomics.