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

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…

• NS
  National Science Foundation
• SV
  Silicon Valley Community Foundation

  Award: 2018-182828
• CZ
  Chan Zuckerberg Initiative

  Awards: 2018-182828, 182828
• SN
  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

  Awards: 182828, CRSII5_177208, 310030_175841
• UZ
  Universität Zürich