collectNET: a web server for integrated inference of cell-cell communication network

Area of Science:

• Computational biology
• Systems biology
• Bioinformatics

Background:

• Cell-cell communication (CCC) via ligand-receptor (L-R) pairs is crucial for multicellular organisms.
• Current methods for inferring CCC networks exhibit biases and inconsistencies.
• Understanding CCC is vital for disease mechanism research and targeted therapies.

Purpose of the Study:

• To develop a comprehensive web platform, collectNET, for analyzing CCC networks.
• To provide a reliable online inference service integrating multiple L-R databases and inference methods.
• To create a human CCC atlas for deeper insights into cellular interactions.

Main Methods:

• Developed collectNET, a web platform with efficient calculation, browsing, statistics, search, and visualization.
• Integrated three public L-R databases and three mainstream inference methods.
• Assembled a human CCC atlas comprising 126,785 significant communication pairs from 343,023 cells.

Main Results:

• collectNET offers a reliable and integrated approach to CCC network analysis.
• The platform provides a comprehensive human CCC atlas.
• Identified 126,785 significant cell-cell communication pairs.

Conclusions:

• collectNET aims to enhance researchers' understanding of cell development and differentiation.
• The platform addresses limitations in existing CCC inference methods.
• collectNET serves as a valuable resource for studying intercellular signaling pathways.