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DiSiR: fast and robust method to identify ligand-receptor interactions at subunit level from single-cell
Milad R Vahid1, Andre H Kurlovs2, Tommaso Andreani3
1Sanofi R&D Data and Data Science, Artificial Intelligence & Deep Analytics, Omics Data Science, 450 Water Street, Cambridge, MA 02142, USA.
NAR Genomics and Bioinformatics
|March 27, 2023
Summary
DiSiR is a new software tool that analyzes cell-cell communication using single-cell RNA sequencing data. It identifies ligand-receptor interactions, outperforming existing methods and revealing disease-specific pathways.
Area of Science:
- Immunology
- Computational Biology
- Genomics
Background:
- Cell-cell communication is crucial, often mediated by ligand-receptor interactions.
- Single-cell RNA sequencing (scRNA-seq) reveals tissue heterogeneity.
- Current methods lack targeted pathway analysis and flexible interaction mapping.
Purpose of the Study:
- To develop DiSiR, a user-friendly software for analyzing multi-subunit ligand-receptor signaling pathways from scRNA-seq data.
- To enable the investigation of both known and novel ligand-receptor interactions.
- To provide a tool for querying specific signaling pathways and subunit interactions.
Main Methods:
- DiSiR utilizes a permutation-based framework to analyze scRNA-seq data.
- It assesses signaling pathways involving multi-subunit ligand-activated receptors.
- The method is validated against simulated and real biological datasets.
Main Results:
- DiSiR demonstrates superior performance compared to established methods like CellPhoneDB and ICELLNET.
- The software effectively infers ligand-receptor interactions from scRNA-seq data.
- It successfully identifies interactions beyond curated databases.
Conclusions:
- DiSiR offers a powerful and flexible approach to studying cell-cell communication via ligand-receptor interactions.
- Its application to COVID lung and rheumatoid arthritis datasets highlights potential disease-specific inflammatory pathway differences.
- DiSiR aids in generating biologically relevant hypotheses from scRNA-seq data.
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