NicheNetPy: the python implementation of the NicheNet method. (ported from NicheNetR) 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.
pip install nichenetpy
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. We provide a pre-built prior model, it is also possible to construct your own model (see notebooks 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
- 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 notebooks.
A very basic tutorial for people who are unfamiliar with python can be found here.
The following notebooks contain the explanation on how to perform a basic NicheNet analysis on an AnnData 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.
- Perform NicheNet analysis starting from an AnnData object: step-by-step analysis
- Perform NicheNet analysis starting from an AnnData object: wrapper
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 an AnnData object.
The following notebooks explain how to do some follow-up analyses:
- Prioritization of ligands based on expression values
- Inferring ligand-to-target signaling paths
- Assess how well top-ranked ligands can predict a gene set of interest
- Single-cell NicheNet’s ligand activity 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 notebooks:
People interested in building their own models or benchmarking their own models against NicheNet can read the following notebooks:
- Model construction
- Parameter optimization
- Using LIANA ligand-receptor databases to construct the ligand-target model
- Model evaluation: target gene and ligand activity prediction
For a comparison between Seurat's FindAllMarkers (which is ported into nichenetpy) and Scanpy's rank_genes_groups, see the following notebook:
Documentation is available at NicheNetPy docs
- Check the FAQ pages at FAQ NicheNetPy and FAQ Nichenet
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