Nichenet for analysis of cell-cell communication
Nichenet for analysis of cell-cell communication
to be announced
General context
Today it is possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). These scRNA-seq datasets can provide information about which cell types are present within a tissue and how they may potentially interact with each other.
However, deciphering cell-cell communication from these datasets requires dedicated computational methods, such as NicheNet.
- Discuss how to analyze cell-cell communication from scRNA-seq data via the NicheNet analysis framework.
- Highlight the benefits and limitations of NicheNet compared to other approaches, and guide you in applying NicheNet to your datasets.
- Cover the most recent analysis features from NicheNet-v2.
- Show how to analyze intercellular communication in multi-sample, multi-condition scRNA-seq datasets with MultiNicheNet.
The training combines demonstrations and hands-on exercises in R. You will practice NicheNet analysis on an example dataset.
Participants should have some experience with R and know the basics of scRNA-seq data analysis. You can attend "R Introduction" and "Analysis of single-cell RNASeq data" to get sufficient background.
Contact janick.mathys@vib.be if you want to follow either of these introductory training sessions.
This course is part of the learning paths on Single cell data short reads and Single cell data long reads.
AI methods used
This course applies network-based computational modeling approaches implemented in NicheNet and MultiNicheNet to infer cell–cell communication, combining gene expression data with prior knowledge of signaling and gene regulatory networks. These methods use graph-based algorithms (e.g., network propagation and ranking) together with statistical models to predict ligand–target interactions and prioritize biologically relevant signals.