Archivio Istituzionale della RicercaThe analysis of RNA data plays a crucial role in understanding cellular differentiation. One widely-used methodology for analyzing RNA data is scVelo. However, in this paper, we show that, among other issues of scVelo, the current model formalization suffers from identifiability problems. We propose a Bayesian version of scVelo with modifications that address these issues.
Analyzing RNA data with scVelo: identifiability issues and a Bayesian implementation / Sabbioni, Elena; Bibbona, Enrico; Mastrantonio, Gianluca; Sanguinetti, Guido. - ELETTRONICO. - (2023), pp. 538-543. (Intervento presentato al convegno SIS 2023 - Statistical Learning, Sustainability and Impact Evaluation tenutosi a Ancona (ITA) nel 21/06/2023-23/06/2023).
Analyzing RNA data with scVelo: identifiability issues and a Bayesian implementation
Sabbioni,Elena;Bibbona,Enrico;Mastrantonio Gianluca;
2023
Abstract
The analysis of RNA data plays a crucial role in understanding cellular differentiation. One widely-used methodology for analyzing RNA data is scVelo. However, in this paper, we show that, among other issues of scVelo, the current model formalization suffers from identifiability problems. We propose a Bayesian version of scVelo with modifications that address these issues.
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https://hdl.handle.net/11583/2982276