multinichenetr: the R package for differential cell-cell communication analysis from single-cell transcriptomics data with complex multi-sample, multi-condition designs. The goal of this toolbox is to study differences in intercellular communication between groups of samples of interest (eg patients of different disease states).
You can read all about MultiNicheNet in the following preprint: https://www.biorxiv.org/content/10.1101/2023.06.13.544751v1
The main goal of the MultiNicheNet package is to find which ligand-receptor interactions are differentially expressed and differentially active between conditions of interest, such as patient groups. Compared to the normal NicheNet workflow, MultiNicheNet is more suited to tackle complex experimental designs such as those with multiple samples and conditions, and multiple receiver cell types of interest.
In the MultiNicheNet approach, we allow the user to prioritize differential cell-cell communication events (ligand-receptor interactions and downstream signaling to target genes) based on the following criteria:
- Upregulation of the ligand in a sender cell type and/or upregulation of the receptor in a receiver cell type - in the condition of interest.
- Sufficiently high expression levels of ligand and receptor in many samples of the same group (to mitigate the influence of outlier samples).
- Cell-type and condition specific expression of the ligand in the sender cell type and receptor in the receiver cell type (to mitigate the influence of upregulated but still relatively weakly expressed ligands/receptors)
- High NicheNet ligand activity, to further prioritize ligand-receptor pairs based on their predicted effect of the ligand-receptor interaction on the gene expression in the receiver cell type
MultiNicheNet combines all these criteria in a single prioritization score, which is also comparable between all sender-receiver pairs. This way, MultiNicheNet extends on the prioritization done by NicheNet, which is only based on the ligand activity score.
Users can customize the weights of these different factors to prioritize some of these criteria stronger, or neglect them altogether.
At the basis of MultiNicheNet for defining differentially expressed ligands, receptors and target genes, is the the differential state analysis as discussed by muscat, which provides a framework for cell-level mixed models or methods based on aggregated “pseudobulk” data (https://doi.org/10.1038/s41467-020-19894-4, https://bioconductor.org/packages/release/bioc/html/muscat.html). We use this muscat framework to make inferences on the sample-level (as wanted in a multi-sample, multi-condition setting) and not the classic cell-level differential expression analysis of Seurat (Seurat::FindMarkers), because muscat allows us to overcome some of the limitations of cell-level analyses for differential state analyses. Some of these limitations include: a bias towards samples with more cells of cell type, a lack of flexibility to work with complex study designs, and a too optimistic estimation of the statistical power since the analysis is done at the cell-level and not at the sample level.
In the future, we might extend the differential expression analyses options to include other frameworks than muscat.
- Prioritizing the most important ligand-receptor interactions from different sender-receiver pairs between different sample groups, according to criteria such as condition specificity, cell-type specificity, ligand activity (= downstream signaling activity), and more.
- Finding differential expressed ligand-receptor interactions from different sender-receiver pairs between different sample groups (Differential Ligand-Receptor network inference).
- Predicting the most active ligand-receptor interactions in different sample groups based on predicted signaling effects (NicheNet ligand activity analysis).
- Predicting specific downstream affected target genes of ligand-receptor links of interest (NicheNet ligand-target inference).
- Predicting intercellular signaling networks, connecting ligands to ligand- or receptor-encoding target genes in other cell types, enabling predictions concerning intercellular cascade and feedback mechanisms.
Installation typically takes a few minutes, depending on the number of dependencies that has already been installed on your pc.
You can install multinichenetr (and required dependencies) from github with:
# install.packages("devtools")
devtools::install_github("saeyslab/nichenetr")
devtools::install_github("saeyslab/multinichenetr")
multinichenetr is tested via Github Actions version control on Windows, Linux (Ubuntu) and Mac (most recently tested R version: R 4.3.0.).
We provide several vignettes demonstrating the different types of analysis that can be performed with MultiNicheNet, and the several types of downstream visualizations that can be created.
We recommend users to start with the following vignette, which demonstrates the different steps in the analysis without too many details yet. This is the recommended vignette to learn the basics of MultiNicheNet.
- MultiNicheNet analysis: MIS-C threewise comparison -
step-by-step:
vignette("basic_analysis_steps_MISC", package="multinichenetr")
This vignette provides an example of a comparison between 3 groups. The following vignettes demonstrate how to analyze cell-cell communication differences in other settings. For sake of simplicity, these vignettes also use a MultiNicheNet wrapper function, which encompasses the different steps demonstrated in the previous vignette. These vignettes are the best vignettes to learn how to apply MultiNicheNet to different datastes for addressing different questions.
- MultiNicheNet analysis: MIS-C pairwise comparison - wrapper
function:
vignette("pairwise_analysis_MISC.Rmd", package="multinichenetr") - MultiNicheNet analysis: MIS-C threewise comparison - wrapper
function:
vignette("threewise_analysis_MISC", package="multinichenetr") - MultiNicheNet analysis: SCC paired analysis - wrapper
function:
vignette("paired_analysis_SCC", package="multinichenetr") - MultiNicheNet analysis: anti-PD1 Breast cancer multifactorial
comparison - wrapper
function:
vignette("multifactorial_analysis_BreastCancer", package="multinichenetr") - MultiNicheNet analysis: Integrated lung atlas analysis - correct
for batch effects to infer differences between IPF and healthy
subjects - wrapper
function:
vignette("batch_correction_analysis_LungAtlas", package="multinichenetr")
The next vignette will cover the different steps in more detail, showcasing some additional recommended quality checks and visualizations
- MultiNicheNet analysis: MIS-C threewise comparison - step-by-step
with all details:
vignette("detailed_analysis_steps_MISC", package="multinichenetr")
That vignettes checks as well for the DE analysis p-value distributions. In case these are suboptimal, pointing to violations to some model assumptions, we recommend to use empirical p-values as discussed in the Methods section of the paper and demonstrated in the following vignette:
- MultiNicheNet analysis: anti-PD1 Breast cancer multifactorial
comparison - step-by-step with all
details:
vignette("detailed_analysis_steps_empirical_pvalues", package="multinichenetr")
When applying MultiNicheNet on datasets with many samples and cell types, it is recommended to run the analysis on HPC infrastructure.You can have a look at following scripts to see how we split up the analysis in two parts: 1) running MultiNicheNet and saving necessary output and plots; and 2) interpreting the results and generating visualizations.
- Even though it is stated in the vignettes, many reported issues arise because names of celltypes, groups/conditions, and/or samples are not syntactically valid. Before reporting your issue, make sure you satisfy this condition and other conditions described in the vignettes. In the latest version of MultiNicheNet, input checks are run to check this and give an understandable error message.
- It is required that each sample is uniquely assigned to only one condition/group of interest. See the vignettes about paired and multifactorial analysis to see how to define your analysis input when you have multiple samples and conditions per patient. In the latest version of MultiNicheNet, input checks are run to check this and give an understandable error message.
- We strongly recommend having at least 4 samples in each of the groups/conditions you want to compare. With less samples, the benefits of performing a pseudobulk-based DE analysis are less clear and non-multi-sample tools for differential cell-cell communication might be better alternatives.
Browaeys, R. et al. MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data. (preprint)
Crowell, H.L., Soneson, C., Germain, PL. et al. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat Commun 11, 6077 (2020). https://doi.org/10.1038/s41467-020-19894-4
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