Joint Centre for Statistics and Centre for Engineering Biology Research Day RNA velocity provides an approach for inferring cellular state transitions from single-cell RNA sequencing (scRNA-seq) data, in particular from the number of molecules of spliced and unspliced mRNA. The unspliced mRNA content is a leading indicator of spliced mRNA, meaning that it is a predictor of the spliced mRNA content in the cell's near future. This causal relationship can be usefully exploited to identify directions of differentiation pathways. Mathematically, it is modelled by first order differential equations for spliced and unspliced mRNA concentration, and the derivative of the spliced mRNA concentration is the RNA velocity. Many related methods have been developed, with the two most popular implementations were released in 2017–2018: velocyto [1], which introduced the method for scRNA-seq data, and scVelo [2], which extended it to fit a more sophisticated dynamical model. However, the reliability and relevance of such velocity estimates has been called into question [3-4], resulting in a flurry of recent improvements to address the challenges [5-9]. With many methods developed in parallel, it remains unclear how they compare and whether any of them address all the issues identified by critics. The main focus of the discussion will be on the (lack of) reliability of RNA velocity models, their underlying assumptions, how to use the information derived from them, how to estimate the uncertainty of the output and how to improve these methods. Organised by Linus Schumacher (Linus.Schumacher@ed.ac.uk) and Natalia Bochkina (N.Bochkina@ed.ac.uk) Jun 27 2023 09.30 - 15.00 Joint Centre for Statistics and Centre for Engineering Biology Research Day Recent developments in statistical modelling of RNA velocity Seminar Room 1, Chrystal MacMillan Building, George Square Registration
Joint Centre for Statistics and Centre for Engineering Biology Research Day RNA velocity provides an approach for inferring cellular state transitions from single-cell RNA sequencing (scRNA-seq) data, in particular from the number of molecules of spliced and unspliced mRNA. The unspliced mRNA content is a leading indicator of spliced mRNA, meaning that it is a predictor of the spliced mRNA content in the cell's near future. This causal relationship can be usefully exploited to identify directions of differentiation pathways. Mathematically, it is modelled by first order differential equations for spliced and unspliced mRNA concentration, and the derivative of the spliced mRNA concentration is the RNA velocity. Many related methods have been developed, with the two most popular implementations were released in 2017–2018: velocyto [1], which introduced the method for scRNA-seq data, and scVelo [2], which extended it to fit a more sophisticated dynamical model. However, the reliability and relevance of such velocity estimates has been called into question [3-4], resulting in a flurry of recent improvements to address the challenges [5-9]. With many methods developed in parallel, it remains unclear how they compare and whether any of them address all the issues identified by critics. The main focus of the discussion will be on the (lack of) reliability of RNA velocity models, their underlying assumptions, how to use the information derived from them, how to estimate the uncertainty of the output and how to improve these methods. Organised by Linus Schumacher (Linus.Schumacher@ed.ac.uk) and Natalia Bochkina (N.Bochkina@ed.ac.uk) Jun 27 2023 09.30 - 15.00 Joint Centre for Statistics and Centre for Engineering Biology Research Day Recent developments in statistical modelling of RNA velocity Seminar Room 1, Chrystal MacMillan Building, George Square Registration
Jun 27 2023 09.30 - 15.00 Joint Centre for Statistics and Centre for Engineering Biology Research Day Recent developments in statistical modelling of RNA velocity
