Modeling cell-to-cell gene expression variability from DNA sequencesKojanec, Patrik (Avtor) Curk, Tomaž (Mentor) Sanguinetti, Guido (Komentor) scRNA-seqgene expression variabilitydeep learningCell-to-cell variability is often associated with cell differentiation in embryo development or cancer outbursts. Although some of the variability in single-cell RNA sequencing (scRNA-seq) experiments is derived from technical noise, a significant proportion is still attributed to the biological processes within the cell. In this Master's thesis, we propose a novel approach to predict cell-to-cell gene expression variability and mean expression directly from the DNA sequence. For this purpose, we use the Enformer, a deep learning transformer model, to embed the DNA sequence into a more favorable feature space, from which we predict the mean expression and overdispersion of scRNA gene expression. We evaluated our approach on the mouse and human data gathered with two scRNA-seq protocols. Our approach can explain up to 60% and 25% of the variance of overdispersion in mouse and human datasets, respectively. Furthermore, in the thesis, we address the changes in the performance of our models caused by the differences in the scRNA-seq protocols.20222022-09-29 11:45:00Magistrsko delo/naloga141443sl