nichenetr: the R implementation of the NicheNet method. The goal of NicheNet is to study intercellular communication from a computational perspective. NicheNet uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths. This allows to predict ligand-receptor interactions that might drive gene expression changes in cells of interest.
We describe the NicheNet algorithm in the following paper: NicheNet: modeling intercellular communication by linking ligands to target genes.
Installation typically takes a few minutes, depending on the number of dependencies that has already been installed on your PC. You can install nichenetr (and required dependencies) from github with:
if(!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("saeyslab/nichenetr")
nichenetr was tested on both Windows and Linux (most recently tested R version: R 4.3.2)
NicheNet strongly differs from most computational approaches to study
cell-cell communication (CCC), as summarized conceptually by the figure
below (top panel: current ligand-receptor inference approaches;
bottom panel: NicheNet). Many approaches to study CCC from
expression data involve linking ligands expressed by sender cells to
their corresponding receptors expressed by receiver cells. However,
functional understanding of a CCC process also requires knowing how
these inferred ligand-receptor interactions result in changes in the
expression of downstream target genes within the receiver cells.
Therefore, we developed NicheNet to consider the gene regulatory effects
of ligands.
At the core of NicheNet is a prior knowledge model, created by integrating three types of databases—ligand-receptor interactions, signaling pathways, and transcription factor (TF) regulation—to form a complete communication network spanning from ligands to their downstream target genes (see figure below). Therefore, this model goes beyond ligand-receptor interactions and incorporates intracellular signaling and transcriptional regulation as well. As a result, NicheNet is able to predict which ligands influence the expression in another cell, which target genes are affected by each ligand, and which signaling mediators may be involved. By generating these novel types of hypotheses, NicheNet can drive an improved functional understanding of a CCC process of interest. Note that although we provide a pre-built prior model, it is also possible to construct your own model (see vignettes below).
- Assessing how well ligands expressed by a sender cell can predict changes in gene expression in the receiver cell
- Prioritizing ligands based on their effect on gene expression
- Inferring putative ligand-target links active in the system under study
- Inferring potential signaling paths between ligands and target genes of interest: to generate causal hypotheses and check which data sources support the predictions
- Validation of the prior ligand-target model
- Construction of user-defined prior ligand-target models
Moreover, we provide instructions on how to make intuitive visualizations of the main predictions (e.g., via circos plots as shown here below).
As input to NicheNet, users must provide cell type-annotated expression data that reflects a cell-cell communication (CCC) event. The input can be single-cell or sorted bulk data from human or mouse. As output, NicheNet returns the ranking of ligands that best explain the CCC event of interest, as well as candidate target genes with high potential to be regulated by these ligands. As an intermediate step, we extract the three features required for the analysis: a list of potential ligands, a gene set that captures the downstream effects of the CCC event of interest, and a background set of genes. Further explanation on each feature can be found in the introductory vignette.
The following vignettes contain the explanation on how to perform a basic NicheNet analysis on a Seurat object. This includes prioritizing ligands and predicting target genes of prioritized ligands. We recommend starting with the step-by-step analysis, but we also demonstrate the use of a single wrapper function. This demo analysis takes only a few minutes to run.
- Perform NicheNet analysis starting from a Seurat object:
step-by-step
analysis:
vignette("seurat_steps", package="nichenetr") - Perform NicheNet analysis starting from a Seurat
object:
vignette("seurat_wrapper", package="nichenetr")
Case study on HNSCC tumor which demonstrates the flexibility of NicheNet. Here, the gene set of interest was determined by the original authors, and the expression data is a matrix rather than a Seurat object.
- NicheNet’s ligand activity analysis on a gene set of
interest:
vignette("ligand_activity_geneset", package="nichenetr")
The following vignettes contain explanation on how to do some follow-up analyses after performing the most basic analysis:
- Prioritization of ligands based on expression
values:
vignette("seurat_steps_prioritization", package="nichenetr") - Inferring ligand-to-target signaling
paths:
vignette("ligand_target_signaling_path", package="nichenetr") - Assess how well top-ranked ligands can predict a gene set of
interest:
vignette("target_prediction_evaluation_geneset", package="nichenetr") - Single-cell NicheNet’s ligand activity
analysis:
vignette("ligand_activity_single_cell", package="nichenetr")
If you want to make a circos plot visualization of the NicheNet output to show active ligand-target links between interacting cells, you can check following vignettes:
- Seurat Wrapper + circos
visualization:
vignette("seurat_wrapper_circos", package="nichenetr"). - HNSCC case study + double circos
visualization:
vignette("circos", package="nichenetr").
People interested in building their own models or benchmarking their own models against NicheNet can read one of the following vignettes:
- Model construction:
vignette("model_construction", package="nichenetr") - Using LIANA ligand-receptor databases to construct the
ligand-target model:
vignette("model_construction_with_liana", package="nichenetr") - Model evaluation: target gene and ligand activity
prediction:
vignette("model_evaluation", package="nichenetr") - Parameter optimization via
NSGAII-R:
vignette("parameter_optimization", package="nichenetr")
Check the FAQ page at FAQ NicheNet:
vignette("faq", package="nichenetr")
20-06-2023:
- MultiNicheNet - a multi-sample, multi-condition extension of NicheNet - is now available on biorxiv and Github.
- MultiNicheNet uses an updated prior model (v2) consisting of additional ligand-receptor interactions from the Omnipath database and from Verschueren et al. (2020). We have now also updated the vignettes of NicheNet to use the new model instead.
- New functionality: we have included additional functions to prioritize ligands not only based on the ligand activity, but also on the ligand and receptor expression, cell type specificity, and condition specificity. This is similar to the criteria used in Differential NicheNet and MultiNicheNet. See the Prioritizing ligands based on expression values vignette for more information.
- Due to this more generalizable prioritization scheme, we will no longer provide support for Differential NicheNet.
- We included code for making a ligand-receptor-target circos plot in the Circos plot visualization vignette.
Differential NicheNet has been deprecated: we will not longer provide support or code fixes on Differential NicheNet and its vignettes. You may want to consider using the general prioritization scheme instead.
- Differential NicheNet analysis between niches of
interest:
vignette("differential_nichenet", package="nichenetr") - Differential NicheNet analysis between conditions of
interest:
vignette("differential_nichenet_pEMT", package="nichenetr")
In NicheNet v2, the mouse and human ligand-target models are uploaded separately so symbol conversion is not necessary. If you are still using the NicheNet v1 model, you can check the following vignette on how to convert the model (given in human symbols) to mouse symbols:
- Converting NicheNet’s model from human to mouse
symbols:
vignette("symbol_conversion", package="nichenetr")
12-01-2022: In the Liver Atlas paper from Guilliams et al.: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches, we used Differential NicheNet, an extension to the default NicheNet algorithm. Differential NicheNet can be used to compare cell-cell interactions between different niches and better predict niche-specific ligand-receptor (L-R) pairs. It was used in that paper to predict ligand-receptor pairs specific for the Kupffer cell niche in mouse and human.
The main difference between the classic NicheNet pipeline and the Differential NicheNet pipeline is that Differential NicheNet also uses the differential expression between the conditions/niches of the ligand-receptor pairs for prioritization in addition to the ligand activities. The classic NicheNet pipeline on the contrary uses only ligand acivity for prioritization (and shows differential expression only in visualizations).
So if you have data of multiple conditions or niches, and you want to include differential expression of the ligand-receptor pairs in the prioritization, we recommend you check out Differential NicheNet (update nichenetr to the 1.1.0 version). At the bottom of this page, you can find the links to two vignettes illustrating a Differential NicheNet analysis. We recommend these vignettes if you want to apply Differential NicheNet on your own data. If you want to see the code used for the analyses used in the Guilliams et al. paper, see https://github.com/saeyslab/NicheNet_LiverCellAtlas.
15-10-2019: Bonnardel, T’Jonck et al. used NicheNet to predict upstream niche signals driving Kupffer cell differentiation Stellate Cells, Hepatocytes, and Endothelial Cells Imprint the Kupffer Cell Identity on Monocytes Colonizing the Liver Macrophage Niche.
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods (2019) doi:10.1038/s41592-019-0667-5
Bonnardel et al. Stellate Cells, Hepatocytes, and Endothelial Cells Imprint the Kupffer Cell Identity on Monocytes Colonizing the Liver Macrophage Niche. Immunity (2019) doi:10.1016/j.immuni.2019.08.017
Guilliams et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell (2022) doi:10.1016/j.cell.2021.12.018
