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nichenetr

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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 of nichenetr

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)

Overview of NicheNet

<details> <summary> <h3> Background </h3> </summary>

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. <br><br> <img src="vignettes/images/comparison_other_approaches_2.jpg" width="450" /> <br><br>

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).

<img src="vignettes/images/nichenet_prior_model.png" style="width:70.0%" />

</details> <details> <summary> <h3> Main functionalities of nichenetr </h3> </summary>

Moreover, we provide instructions on how to make intuitive visualizations of the main predictions (e.g., via circos plots as shown here below).

<br><br> <img src="vignettes/images/circos_plot_adapted.jpg" width="600" />

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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.

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Learning to use nichenetr

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.

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.

The following vignettes contain explanation on how to do some follow-up analyses after performing the most basic analysis:

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:

People interested in building their own models or benchmarking their own models against NicheNet can read one of the following vignettes:

FAQ

Check the FAQ page at FAQ NicheNet: vignette("faq", package="nichenetr")

<details> <summary> <h2> Previous updates </h2> </summary>

20-06-2023:

<h5> Deprecated vignettes </h5>

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.

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:

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.

</details>

References

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