muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data.
> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)
书目信息
作者摘要（按来源许可复用）
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.
合规说明
本页保存的是来源文献书目信息及其在 CC-BY 许可下公开的作者摘要。除去除来源 HTML 标签和规范化空白外，摘要未作内容改写。本页不代表 GeniOmics 的医学建议；原文版权、署名和许可仍归原权利人，请通过原始记录核对最新版本、更正或撤稿状态。