Methodologies for Identifying Cell Types from Single-Cell RNA Sequencing Data:

This presentation addresses the development of a software solution for clustering individual cells based on scRNA-seq data. The approach focuses on designing innovative methodologies that explore the intrinsic structure of the data, leveraging graph-based techniques, deep learning, and Bayesian models. In addition, a comparison is made with popular tools used in scRNA-seq analysis and with traditional clustering methods for high-dimensional data. As a result, a software demo is presented that implements the most effective methodologies developed throughout the project.