Computational approaches to intuitively analyze and visualize single-cell data
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In this thesis, I will focus on developing computational methods that deliver intuitive and interpretable visualization of single-cell data. The first chapter describes a software named Cluster-to-Gate (C2G) that can visualize existing clustering results of flow/mass cytometry data in the format of 2D gating hierarchy. Though C2G presents a way to visualize and interpret clustering results, the visualization is still data-driven and does not involve human-knowledge. To overcome the limitation of C2G, the second chapter describes a framework that can learn gating approach from existing publications to build a knowledge-graph. This knowledge-graph can automatically suggest the order of marker usage and gating hierarchy for the new data set, which can be used to gate the cell population. The obtained cell populations are immediately matched to some cell types in the knowledge graph, which makes them more interpretable. The third chapter describes a novel algorithm (GLaMST) to reconstruct lineage tree of B-cell receptor genes from high throughput sequencing data. This algorithm outperforms state-of-art in both accuracy and speed.