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nichenetr

📖 Documentation: https://zaoqu-liu.github.io/nichenetr/

Overview

NicheNet is a computational framework for modeling intercellular communication by linking extracellular ligands to downstream intracellular gene regulatory changes. Unlike conventional ligand-receptor inference methods that solely identify potential receptor-ligand interactions based on co-expression patterns, NicheNet integrates prior knowledge of signaling pathways and gene regulatory networks to predict which ligands from sender cells are most likely to affect gene expression in receiver cells.

Key Publications

Browaeys R, Saelens W, Saeys Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nature Methods 17, 159-162 (2020). doi:10.1038/s41592-019-0667-5

Unraveling cell-cell communication with NicheNet by inferring active ligands from transcriptomics data. Nature Protocols (2024). doi:10.1038/s41596-024-01121-9

Installation 

install.packages("nichenetr", repos = "https://zaoqu-liu.r-universe.dev")

From GitHub 

if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}
devtools::install_github("Zaoqu-Liu/nichenetr")

System Requirements

  • R version: >= 4.0.0
  • Operating systems: Windows, macOS, Linux
  • Dependencies: Seurat (v4/v5), tidyverse, igraph, Matrix, Rcpp

Quick Start 

library(nichenetr)
library(Seurat)
library(tidyverse)

# Load pre-built prior model
ligand_target_matrix <- readRDS(url("https://zenodo.org/record/7074291/files/ligand_target_matrix_nsga2r_final_mouse.rds"))
lr_network <- readRDS(url("https://zenodo.org/record/7074291/files/lr_network_mouse_21122021.rds"))
weighted_networks <- readRDS(url("https://zenodo.org/record/7074291/files/weighted_networks_nsga2r_final_mouse.rds"))

# Run NicheNet analysis
nichenet_output <- nichenet_seuratobj_aggregate(
  seurat_obj = seurat_obj,
  receiver = "Receiver_CellType",
  sender = c("Sender_CellType1", "Sender_CellType2"),
  condition_oi = "Treatment",
  condition_reference = "Control",
  condition_colname = "condition",
  ligand_target_matrix = ligand_target_matrix,
  lr_network = lr_network,
  weighted_networks = weighted_networks
)

Tutorials

Vignette Description
Algorithm Overview Comprehensive overview of the NicheNet algorithm
Step-by-step Analysis Detailed walkthrough with Seurat
Seurat Wrapper Streamlined analysis using wrapper functions

See the full documentation for more vignettes.

Data Resources

Pre-built models available at Zenodo:

  • Ligand-Target Matrix (Human/Mouse)
  • Weighted Networks (Human/Mouse)
  • Ligand-Receptor Networks (Human/Mouse)

Citation 

@article{browaeys2020nichenet,
  title={NicheNet: modeling intercellular communication by linking ligands to target genes},
  author={Browaeys, Robin and Saelens, Wouter and Saeys, Yvan},
  journal={Nature Methods},
  volume={17},
  pages={159--162},
  year={2020},
  doi={10.1038/s41592-019-0667-5}
}

License

GPL-3.0

Contact

For questions, please open an issue on GitHub.