Migliaccio, Francesco (2025) Graphical models for count data in scRNA-seq gene network inference and other computational methods for omics data analysis. [Tesi di dottorato]

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Abstract

In recent years, high-throughput sequencing technologies have revolutionized biological research, enabling large-scale projects that generate vast amounts of omics data. Among these, the advances in scRNA-seq transcriptomics, both in quality and in cost reduction, have dramatically enhanced our ability to dissect cellular heterogeneity, providing unprecedented insights into gene-gene interactions within distinct cell types. Scientists use this type of data, particularly in heterogeneous samples, to acquire a deep understanding of regulatory mechanisms whose disruption can be associated with diseases. This new perspective, together with the large availability of cell population data bid for the development of ad hoc integration methods. This thesis focuses on developing novel methods to leverage the rich information embedded in publicly available scRNAseq datasets. The proposed methods hold the potential to advance our understanding of gene regulatory networks in complex biological systems, with broad implications for research in areas such as neuronal differentiation and cancer biology.

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