Dr. Khaled Sayed (ECECS – University of New Haven)
In the rapidly advancing field of genomics, Single-Cell RNA Sequencing (scRNA-seq) has emerged as a transformative technology, allowing us to gain unprecedented insights into the intricacies of cellular heterogeneity at a single-cell resolution.
This talk introduces the fundamental aspects of scRNA-seq data analysis, providing a comprehensive overview of essential methodologies and techniques. The presentation will begin by discussing quality control measures that are vital in ensuring the reliability of scRNA-seq data, including data preprocessing steps and outlier detection. Feature selection and dimensionality reduction techniques are introduced as critical tools for managing the high dimensionality inherent in single-cell data, enabling the extraction of meaningful biological information while reducing noise. Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are presented as dimensionality reduction techniques that facilitate data visualization and clustering, aiding in the exploration of cellular diversity. Furthermore, the talk introduces pseudotime analysis, a powerful approach for ordering individual cells along developmental trajectories, shedding light on dynamic cellular processes. Lastly, trajectory inference methods are introduced, providing insights into cellular differentiation and developmental pathways, and enhancing our understanding of cell fate determination.