muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

By Helena L. Crowell, Charlotte Soneson, Pierre-Luc Germain, D. Calini, L. Collin, Catarina Rapôso, Dheeraj Malhotra, Mark D. Robinson. Nature Communications, 2020

376 citations recorded by the source corpus.

Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package. Single-cell transcriptomics enhanced our ability to profile heterogeneous cell populations. It is not known which statistical frameworks are performant to detect subpopulation-level responses. Here, the authors developed a simulation framework to evaluate various methods across a range of scenarios.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a32071ad0414bbde089ac3b6f0e479831ab80a27

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In Molecular Biology

Citations in the corpus

Comparison and evaluation of statistical error models for scRNA-seq (2021, positive): “Data was simulated with muscat [79] using”
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, positive): “Pseudo-bulk analysis by edgeR has been shown to give better performance than other methods for single-cell differential expression analysis in the presence of replicate samples [ 50 ].”