2024 Theses Doctoral

Computational Methods for Inferring Mechanisms of Biological Heterogeneity in Single-Cell Data

Persad, Sitara Camini

Single-cell sequencing techniques, such as single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq), have revolutionized our understanding of cellular diversity and function. Genetic and epigenetic factors influence phenotypic heterogeneity in ways that are just beginning to be understood. In this work, we develop methods for inferring mechanisms of biological heterogeneity in single-cell data, with particular applications to cancer biology.

First, we develop a kernel archetype analysis method for overcoming noise and sparsity in single-cell data by aggregating single cells into high-resolution cell states. We show that the proposed approach captures robust and biologically meaningful cell states and enables the inference of epigenetic regulation of phenotypic heterogeneity. In the second part of this thesis, we develop methods for linking genotypic and phenotypic information, first by using aggregated single-cell RNA sequencing and a hidden Markov model to infer copy number variation. We demonstrate that aggregation improves copy number inference over existing approaches.

We then integrate DNA sequencing with single-cell RNA sequencing to infer copy number profiles in a rapid autopsy of a patient with metastatic pancreatic cancer. We develop a scalable algorithm for inferring phylogenetic relationships between cells from noisy copy number profiles. We show that our approach more accurately recovers phylogenetic relationships between cells and apply it to understand the relationship between genotype and phenotype in metastatic cancer. Finally, we develop a metric for quantifying the extent to which genotype determines phenotype in lineage tracing data. We show that it more accurately quantifies phenotypic plasticity compared to existing approaches. Altogether, these methods can be used to help uncover the mechanisms underlying phenotypic heterogeneity in biological systems.