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

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Date
2020Type
- Journal Article
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Cited 77 times in
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Cited 76 times in
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Abstract
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. Show more
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https://doi.org/10.3929/ethz-b-000455470Publication status
publishedExternal links
Journal / series
Nature CommunicationsVolume
Pages / Article No.
Publisher
Nature Publishing GroupMore
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Citations
Cited 77 times in
Web of Science
Cited 76 times in
Scopus
ETH Bibliography
yes
Altmetrics