Computational methods to decode cellular heterogeneity

Cellular identity and heterogeneity are the key to multiple aspects of normal development and physiology, but are disrupted in diseases such as cancer. Cellular identities are encoded in the epigenome, which controls the stability of gene expression programmes. However, the ways in which the epigenetic machinery controls cellular heterogeneity are unknown, in part because of a historical lack of methods to study cellular heterogeneity at its fundamental unit, the cell. Recent development of single-cell omics technologies now allow us to study heterogeneity at the cellular level. In this thesis, multiple omics technologies including single-cell sequencing will be applied to understand the role of epigenome in the control of heterogeneity, especially in cancer patients. The work in this thesis will include analysis of multiple types of omics data, and the development of novel computational methods to quantify and understand heterogeneity in single-cell omics datasets.