Algorithms for modeling gene regulation and determining cell type using single-cell molecular profiles

Abstract

Single-cell genomic technologies are helping us answer key biological questions that have long remained elusive. How do a single cell and a single genome generate such complex multicellular organisms as humans? More specifically, how do these cells orchestrate specific transcriptional programs depending on their cell type? New technologies like single-cell RNA-seq and single-cell ATAC-seq allow us to examine the transcription and regulation of individual cells as they develop; however, these methods have important limitations. A primary limitation with all single-cell data is data sparsity, which must be overcome computationally to extract useful information from these experiments. In this dissertation, I present two algorithms designed to overcome the sparsity of single-cell data and allow biological discovery. I first introduce Cicero for single-cell chromatin accessibility data, which is both an algorithm that calculates co-accessibility scores to assign distal regulatory elements to genes, and a software system that adapts existing single-cell RNA-seq analysis techniques for use with single-cell chromatin accessibility data. In Chapter 2, I apply Cicero to an in vitro myoblast differentiation assay and find evidence for the use of ”chromatin hubs” during myogenesis. In Chapter 3, I apply Cicero to single-cell ATAC-seq data from mouse bone marrow and recapitulate known patterns of hematopoiesis and known cis-regulation of the b-globin locus. In Chapter 4, I introduce a second algorithm, Garnett, which uses single-cell expression data to train and apply automated cell type classifiers. The accuracy of this technology is demonstrated with data from various single-cell RNA-seq methods and tissue sources. In a final chapter, I reflect on the development of software for biological applications and future directions for this work.