Algorithms For Modeling Gene Regulation And Determining Cell Type Using Single Cell Molecular Profiles

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.

The Mouse Nervous System provides a comprehensive account of the central nervous system of the mouse. The book is aimed at molecular biologists who need a book that introduces them to the anatomy of the mouse brain and spinal cord, but also takes them into the relevant details of development and organization of the area they have chosen to study. The Mouse Nervous System offers a wealth of new information for experienced anatomists who work on mice. The book serves as a valuable resource for researchers and graduate students in neuroscience. Systematic consideration of the anatomy and connections of all regions of the brain and spinal cord by the authors of the most cited rodent brain atlases A major section (12 chapters) on functional systems related to motor control, sensation, and behavioral and emotional states A detailed analysis of gene expression during development of the forebrain by Luis Puelles, the leading researcher in this area Full coverage of the role of gene expression during development and the new field of genetic neuroanatomy using site-specific recombinases Examples of the use of mouse models in the study of neurological illness

Background: Cells from our immune system detect and kill pathogens to protect our body against many diseases. However, current methods for determining cell types have some major limitations, such as being time-consuming and with low throughput rate, etc. These problems stack up and hinder the deep exploration of cellular heterogeneity. Immune cells that are associated with cancer tissues play a critical role in revealing the stages of tumor development. Identifying the immune composition within tumor microenvironments in a timely manner will be helpful to improve clinical prognosis and therapeutic management for cancer. Single-cell RNA sequencing (scRNA-seq), an RNA sequencing (RNA-seq) technique that focuses on a single cell level, has provided us with the ability to conduct cell type classification. Although unsupervised clustering approaches are the major methods for analyzing scRNA-seq datasets, their results vary among studies with different input parameters and sizes. However, in supervised machine learning methods, information loss and low prediction accuracy are the key limitations. Methods and Results: Genes in the human genome align to chromosomes in a particular order. Hence, we hypothesize incorporating this information into our model will potentially improve the cell type classification performance. In order to utilize gene positional information, we introduce chromosome-based neural network, namely ChrNet, a novel chromosome-specific re-trainable supervised learning method based on a one-dimensional convolutional neural network (1D-CNN). The model's performance was evaluated and compared with other supervised learning architectures. Overall, the ChrNet showed highest performance among the 3 models we benchmarked. In addition, we demonstrated the advantages of our new model over unsupervised clustering approaches using gene expression profiles from healthy, and tumor infiltrating immune cells. The codes for our model are packed into a Python package publicly available online on Github. Conclusions: We established an innovative chromosome-based 1D-CNN architecture to extract scRNA-seq expression information for immune cell type classification. It is expected that this model can become a reference architecture for future cell type classification methods.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.

This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology.

"Cellular differentiation is an important process where progenitor cells progressively develop into mature cells with specialized functions. Understanding the molecular characteristics and underlying regulatory mechanisms of cell fate is a central goal in biological research. Advances in single-cell sequencing technology enable the exploration of cellular differentiation at unprecedented resolution. In this thesis, I focus on characterizing and modeling of cellular differentiation using machine learning approaches. First, I present a random forest approach to identify the most discriminant genes for different cell populations in the developing brain. This method was able to identify key gene markers that revealed dorsal-ventral patterning in a heterogeneous class of progenitors present in a mouse developmental time-series dataset. Next, as cellular differentiation is marked by continuous changes in gene expression and is not well described by static cell populations, I present a framework to model the dynamics of cell fate decisions based on ordinary differential equations (ODE). I train this model on previously reported trajectory data for neural differentiation, and show that the model is able to interpolate and predict the gene expression dynamics across unobserved regions in this trajectory and extend the system dynamics for neural differentiation data. Finally, by training the model on datasets that contain rate of change information for each gene (RNA velocity), I demonstrate that the model has the capacity to predict the effects of gene deletions to the cell's overall gene expression profile with a prediction accuracy of 90%. In summary, the Neural ODE method has the ability to learn the gene regulatory dynamics from single cell data and predict the dynamics of individual genes as well as perturbation response"--

Each chapter presents a detailed background of the described method, its theoretical foundations, and its applicability to different biomedical material. Updated chapters describe either the most popular methods or those processes that have evolved the most since the past edition. Additionally, a large portion of the volume is devoted to clinical cytometry. Particular attention is paid to applications of cytometry in oncology, the most rapidly growing area. Contains 56 extensive chapters authored by world authorities on cytometry Covers a wide range of topics, including principles of cytometry and general methods, cell preparation, tandardization and quality assurance, cell proliferation, apoptosis, cell-cell/cell-environmental interactions, cytogenetics and molecular genetics, cell function and differentiation, experimental and clinical oncology, microorganisms, and infectious diseases Describes in-depth the essential methods and scientific principles of flow and laser scanning cytometry and illustrates how they can be applied to the fields of biology and medicine Complements the first and second editions on flow cytometry in the Methods in Cell Biology series and includes new sections on technology principles