Cell – Cell Communication and Gene Regulation Team
Transcriptomics and beyond
The Cell – Cell Communication and Gene Regulation team of SaeysLab develops open-source algorithms that leverage single-cell transcriptomics data to further our biological understanding of how cells cooperate to perform various functions.
How can prior biological knowledge and various omics technologies be combined to infer cell-cell communication processes, both within and across samples of healthy and diseased tissue?
To further advance the field of single-cell transcriptomics, this team also performs benchmarks of various algorithms and pipelines. What are the most suitable methods, for example to infer a trajectory or perform spatial deconvolution?
Highlighted cell-cell communication and gene regulation papers
Unraveling cell-cell communication with NicheNet by inferring active ligands from transcriptomics data
Chananchida Sang-aram, Robin Browaeys, Ruth Seurinck & Yvan Saeys
arXiv 2024
MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data
Robin Browaeys, Jeroen Gilis, Chananchida Sang-Aram, Pieter De Bleser, Levi Hoste, Simon Tavernier, Diether Lambrechts, Yvan Saeys & Ruth Seurinck
bioRxiv 2023
NicheNet: modeling intercellular communication by linking ligands to target genes
Robin Browaeys, Wouter Saelens & Yvan Saeys
Nature methods 2019
A comparison of single-cell trajectory inference methods
Wouter Saelens, Robrecht Cannoodt, Helena Todorov, Yvan Saeys
Nature Biotechnology 2019