nichenetr

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.

Major updates (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.

Introduction to NicheNet

The figure below shows a graphical representation of the NicheNet workflow. Interactions inferred from several complementary ligand-receptor, signaling and gene regulatory data sources were aggregated in respective integrated networks from which ligand-target regulatory potential scores were calculated. This model of prior information on potential ligand-target links can then be used to infer active ligand-target links between interacting cells. NicheNet prioritizes ligands according to their activity (i.e., how well they predict observed changes in gene expression in the receiver cell) and looks for affected targets with high potential to be regulated by these prioritized ligands.

We offer the option to use the prebuilt prior model (such that the network integration steps should not be repeated), or to create and use your own prior model (see reference to detailed vignette below).

NicheNet strongly differs from most current computational approaches to study intercellular communication. Current approaches study intercellular communication from (single-cell) expression data by linking ligands expressed by sender cells to their corresponding receptors expressed by receiver cells. However, functional understanding of a cellular communication process also requires knowing how these inferred ligand-receptor interactions result in changes in the expression of downstream target genes within the receiver cells. To address this need, we developed NicheNet. Contrary to existing approaches, NicheNet looks at gene regulatory effects of ligands because the used prior knowledge goes beyond ligand-receptor interactions and incorporates intracellular signaling and transcriptional regulation as well. As a result, NicheNet allows 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 cell-cell communication process of interest. The figure below summarizes the conceptual differences between most current ligand-receptor network inference approaches (top panel) and NicheNet (bottom panel) and visualizes the power of NicheNet in prioritizing ligand-receptor interactions based on gene expression effects.

Main functionalities of nichenetr

Specific functionalities of this package include:

• 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).

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:

# install.packages("devtools")
devtools::install_github("saeyslab/nichenetr")

nichenetr was tested on both Windows and Linux (most recently tested R version: R 4.0.0)

Learning to use nichenetr

To learn using nichenetr, read one of the following vignettes explaining several types of analyses:

Following vignette contains the explanation on how to perform a basic NicheNet analysis. This includes prioritizing ligands and predicting target genes of prioritized ligands. This demo analysis takes only a few minutes to run:

• NicheNet’s ligand activity analysis on a gene set of interest: predict active ligands and their target genes: vignette("ligand_activity_geneset", package="nichenetr")

To facilitate the use of NicheNet on single-cell data, we demonstrate the use of NicheNet on a Seurat object in following vignettes. One demonstrates the use of a single wrapper function, the other demonstrates what’s behind the wrapper (recommended).

• Perform NicheNet analysis starting from a Seurat object:vignette("seurat_wrapper", package="nichenetr")
• Perform NicheNet analysis starting from a Seurat object: step-by-step analysis:vignette("seurat_steps", package="nichenetr")

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, you can check following vignettes:

• Circos plot visualization to show active ligand-target links between interacting cells:vignette("circos", package="nichenetr").
• Seurat Wrapper + Circos visualization:vignette("seurat_wrapper_circos", package="nichenetr").

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

• Model construction: vignette("model_construction", package="nichenetr")
• Model evaluation: target gene and ligand activity prediction: vignette("model_evaluation", package="nichenetr")
• Parameter optimization via mlrMBO: vignette("parameter_optimization", package="nichenetr")

Deprecated vignettes

Differential NicheNet has been deprecated; 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")

FAQ

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

Previous updates

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.

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