/nichenet_training

Primary LanguageR

NicheNet Training

Welcome to the NicheNet training repository. This repository contains the materials and code as presented at the VIB training Study intercellular communication with NicheNet as presented on the 31st of May 2024.

Objectives of the training

This training discusses how to analyze cell-cell communication from scRNA-seq data via the NicheNet analysis framework. The benefits and limitations of NicheNet are highlighted and compared to other approaches. The materials guide the course participant in applying NicheNet to their datasets. Most recent analysis features from NicheNet-v2 are also be covered. Finally, the materials also include how to analyze intercellular communication in multi-sample multi-condition scRNA-seq datasets with MultiNicheNet.

Required skills

Participants should have some experience with R and know the basics of scRNA-seq data analysis.

Installation

NicheNicheNet and MultiNicheNet are implemented as R packages. To run the code in this repository, you will need the following software installed:

To install the required R packages, run the following code in your R console:

tools <- c("BiocManager", "devtools", "circlize", "DiagrammeR", "tidyverse")

for (tool in tools){
  if(!requireNamespace(tool, quietly = TRUE)) { 
    install.packages(tool)  
  } 
}

if(!requireNamespace("ComplexHeatmap", quietly = TRUE)) { 
  BiocManager::install("ComplexHeatmap", update = FALSE) 
} 

devtools::install_github("saeyslab/nichenetr")

Docker container

If you are familiar with Docker, we provide an RStudio image with NicheNet installed at:

docker pull csangara/nichenetr:latest

This can be run with:

docker run -p 8787:8787 -e PASSWORD=bioc -v /path/to/repo/:/path/to/repo/ csangara/nichenetr:latest

Then, go to localhost:8787 in your web browser and login with the user rstudio and password bioc.

For Linux users, you will likely need to provide the USER and USERID to avoid permission issues (see here):

docker run  -p 8787:8787 -e USER=$USER -e USERID=$UID -e PASSWORD=bioc -v /path/to/repo/:/path/to/repo/ csangara/nichenetr:latest

You will have to instead login with the user $USER.

Downloading additional files

To easily access the tutorial files, we recommend you to clone this repository:

git clone https://github.com/saeyslab/nichenet_training.git

Then open the nichenet_training.Rproj in RStudio, which will set nichenet_training/ as the working directory. When creating a new folder to store the networks and Seurat objects, we assume that you are in this directory.

NicheNet networks

Running NicheNet always requires general input data (prior knowledge networks). These files can be downloaded from https://zenodo.org/records/7074291.

You can also download them via the command line with:

wget https://zenodo.org/api/records/7074291/files-archive

Once the files have been downloaded, create a networks/ folder and extract the files there. You can run the script check_network_files.R to see if all required networks are correctly downloaded.

Case studies

We provide three NicheNet case studies in the vignettes/ folder that are further explained below. The Seurat objects used in each study can be downloaded at the following links: [1] [2] [3]

Or via the command line:

wget https://zenodo.org/record/3531889/files/seuratObj.rds
wget https://zenodo.org/records/11400203/files/seurat_obj_lite.rds
wget https://zenodo.org/records/5840787/files/seurat_obj_subset_integrated_zonation.rds

Create a new data/ folder and place the objects there.

1. Case-Control: Studying immune cell interactions in lymph nodes after viral infection (case_control_example)

In this example we use mouse NICHE-seq data from Medaglia et al. (2017) to explore intercellular communication in the T cell area in the inguinal lymph node before and 72 hours after lymphocytic choriomeningitis virus (LCMV) infection. Specifically, we will prioritize which ligands can best explain the downstream changes after LCMV infection in CD8 T cells as the receiver population.

2. Case-Control: Studying TME interactions in the context of anti-PD1 immunotherapy (MultiNicheNet_demo)

The vignette for the case control example can be adapted for a scRNA-seq data set from breast cancer biopsies of patients receiving anti-PD1 immune-checkpoint blockade therapy. Bassez et al. (2021) collected from each patient one tumor biopsy before anti-PD1 therapy (“pre-treatment”) and one during subsequent surgery (“on-treatment”) A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Based on additional scTCR-seq results, they identified one group of patients with clonotype expansion as response to the therapy (“E”) and one group with only limited or no clonotype expansion (“NE”). Specifically, we want to identify the most important interactions driving pre-therapy differences in CD4 T cells between expander and non-expander patients.

3. Cell localization & differentiation: Studying the Kupffer cell niche (differentiation_example)

This vignettes uses a subset of the mouse liver scRNA-seq data generated by Guilliams et al. (2022) and focuses on identifying communication signals that determine Kupffer cell identity. Specifically, we will prioritize which ligands can best explain the differences between Kupffer cells and other liver macrophages.

References and resources

The slides are available in the slides folder.

The training materials are based on the following publications: