muscatWrapper

muscatWrapper: the R package containing wrapper functions for easier muscat analysis and downstream visualization (= DE analysis on single-cell transcriptomics data with complex multi-sample, multi-condition designs).

At the basis of muscatWrapper is the differential state analysis as implemented in the muscat R package (https://doi.org/10.1038/s41467-020-19894-4, https://bioconductor.org/packages/release/bioc/html/muscat.html). Muscat provides a framework for differential state/expression analyses based on aggregated “pseudobulk” data . 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 this package, we provide wrapper functions around muscat for ease-of-use and flexibility in tweaking parameters. Moreover, we provide some more advanced downstream visualizations of the results.

Installation of muscatWrapper

Installation typically takes a few minutes, depending on the number of dependencies that has already been installed on your pc.

You can install muscatWrapper (and required dependencies) from github with:

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

muscatWrapper is tested via Github Actions version control on Windows, Linux (Ubuntu) and Mac (most recently tested R version: R 4.1.0).

Learning to use muscatWrapper

In the following vignettes, you can find how to do a multi-sample multi-condition DE analysis with muscatWrapper:

• Multi-sample Multi-condition Differential Expression Analysis via Muscat: HNSCC application: vignette("basic_analysis", package="muscatWrapper")

In case you have a batch effect or covariates you want to correct for during the DE analysis and visualization, check:

• Multi-sample Multi-condition Differential Expression Analysis via Muscat: HNSCC application – Batch Correction:vignette("basic_analysis_batchcor", package="muscatWrapper")

For a detailed statistical analysis and interpretation of DE p-values, and the use of the empiricall null procedure, check:

• Multi-sample Multi-condition Differential Expression Analysis via Muscat: HNSCC application – Empirical Null procedure:vignette("basic_analysis_empnull", package="muscatWrapper")

If you would want to use other code for the multi-sample, multi-condition DE analysis (e.g. with the original muscat code, scran::pseudoBulkDGE, …), but still use the visualizations provided by this package, you can check the following vignette:

• muscatWrapper Visualization Preparation:vignette("visualization_preparation", package="muscatWrapper")

References

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