Mathematical Methods for scRNA-seq Data Analysis

Single-cell sequencing technologies have revolutionized our understanding of cell diversity and development. Since single-cell data contain information of up to thousands of genes and cells, new mathematical and computational methods to analyze it are in high demand. Dimensionality reduction methods are crucial for cell clustering and developmental trajectory inference tasks within the downstream analysis. This thesis presents two novel approaches to dimensionality reduction that leverage gene co-regulation information as given by correlation. The proposed methods improve cell clustering and developmental trajectory tasks while facilitating the analysis and interpretation of single-cell transcriptomic data.