What Are the Most Widely Used Tools for Cell–Cell Communication Analysis in Single-Cell Sequencing?
Single-cell sequencing enables the investigation of cell–cell communication, offering insights into intercellular signaling and interactions among diverse cell types. A variety of computational tools have been developed to facilitate such analyses. Below are several widely adopted tools for analyzing intercellular communication using single-cell transcriptomic data:
CellPhoneDB
A computational framework designed to infer cell–cell communication from single-cell transcriptomic profiles by evaluating statistically significant ligand–receptor interactions. It includes a curated database of known ligand–receptor pairs and estimates the specificity and significance of interactions between cell types.
Website: https://www.cellphonedb.org/
NicheNet
A tool for modeling intercellular signaling networks that predicts how ligands expressed by sender cells influence gene expression in receiver cells. It integrates prior knowledge on ligand–target regulatory potential with single-cell expression data to infer signal transduction pathways.
Website: https://github.com/saeyslab/nichenetr
iTALK
An R package for identifying and visualizing ligand–receptor-mediated communication events in single-cell RNA-seq data. It supports both custom and built-in ligand–receptor databases and offers several visualization options to explore communication patterns across cell types.
Website: https://github.com/Coolgenome/iTALK
scTensor
A tensor decomposition-based analytical tool that detects multi-dimensional ligand–receptor interactions across cell types. It employs hierarchical tensor factorization to uncover complex patterns of communication from single-cell transcriptomes.
Website: https://github.com/rikenbit/scTensor
The tools listed above represent only a subset of available methods for single-cell communication analysis. Tool selection should be guided by factors such as data characteristics, experimental context, and specific research objectives. In many cases, applying multiple tools in parallel may help achieve more robust and biologically meaningful interpretations.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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