Approcci basati sulla teoria delle reti all'analisi di dati epigenomici a singola cellula

The impressive technological progress in single-cell sequencing has led to an abundance of data availability as well as multiple innovative sequencing methods. This data type asks for suitable computational tools to discover the patterns and latent structures that drive the entire system. This project is a contribution to the study of existing and new methods for epigenomic and multi-omics data analysis: I focused my work on ATAC-seq (Assay for Transposase-Accessible Chromatin sequencing) and SNARE-seq (single- nucleus chromatin accessibility and mRNA expression sequencing) data. After reproducing in Python the results of “Cistopic,” the LDA-based R library considered state-of-the-art in epigenetic data analysis, I applied an hSBM algorithm to the datasets. This process resulted in a comprehensive analysis of the two approaches, validated on recent datasets and datasets already deeply studied by a multitude of research groups. ​