Browsing by Author "Nebelung, Hanna"

ScRNA-seq captures a static picture of a cell's transcriptome including abundances of unspliced and spliced RNA. RNA velocity methods offer the opportunity to infer future RNA abundances and thus future states of a cell based on the temporal change of these unspliced and spliced RNA. Early RNA velocity methods have shed light on transcriptional dynamics in many biological processes. However, due to strict assumptions in the underlying model, these models are not reliable when analysing and inferring velocity for genes with complex expression dynamics such as genes with transcriptional boosts. These genes can for example be observed in erythropoietic and hematopoietic data. Several new RNA velocity methods have been proposed recently. Among these, veloVI and Pyro-Velocity both employ Bayesian methods to estimate the reaction rate and latent parameters. Thus the problem of estimating RNA velocity is turned into a posterior probability inference, that allows for more flexible inference of model parameters and the quantification of uncertainty. The objectives of this thesis were to investigate newly published RNA velocity methods, veloVI and Pyro-Velocity, in comparison to the established tool scVelo. To achieve this, we applied the methods to data obtained from scRNA-seq of healthy and ERCC6L2 disease bone marrow cells. ERCC6L2 disease can cause bone marrow failure with a risk of progression to acute myeloid leukemia with erythroid predominance. Specifically, we evaluated whether RNA velocity results reflect hematopoietic differentiation, if genes with transcriptional boosts affect the velocity results, and if RNA velocity analysis can indicate why erythropoiesis in ERCC6L2 disease is affected. We find that new RNA velocity methods can not produce velocity estimations that are fully in line with what is known of hematopoiesis in our data. Further, the results suggest that velocity estimations by veloVI are affected by genes with transcriptional boosts. Moreover, RNA velocity methods examined in this thesis are not robust and cannot reliably predict cell transitions based on the estimated velocity. Subsequently, velocity estimations for disease data such as ERCC6L2 disease must be evaluated carefully before drawing any conclusion about the differentiation process. In conclusion, this thesis highlights the need for models that can model complex transcription kinetics. Still, as this field is rapidly growing and promising new methods are being developed, improvement of RNA velocity analysis, in general, is possible.