Studying temporal dynamics and regulatory mechanisms of single cells with a unified framework and multi-omics data - Single cell genomics technologies have allowed researchers to study differences between single cells. In order to understand how every cell functions in the whole living organism, cells need to be studied in the context of both time and space. Researchers would like to learn a comprehensive picture of each single cell, including its current cell state and predicted future state, and how it interacts with neighboring cells during the temporal dynamics. So the step after gaining a certain amount of knowledge of single cells is to go from “parts” to “whole”. This proposal discusses advances that can be brought to the study of both temporal dynamics and spatial interactions between cells. The theme of this proposal is “integration”. Existing integrative methods for single cell data focus on two scenarios: 1) integrate the same type of data (eg. RNA-Seq data) from multiple batches; 2) integrate multiple types of data performed on the same cells, which is also called multi-modality data or multi-omics data. This proposal highlights concepts and methods to integrate multi-omics data to understand cell temporal dynamics and regulatory mechanisms, while taking into account dependency between data modalities, which is rare in current methods. A new concept of “problem integration”, where related computational formulations can be connected to provide a consistent and more general picture of a certain aspect of cells, is also presented here. In order to study different aspects of a cell, different computational problems have been formulated, eg., clustering of cells, inference of cell trajectories, inference of gene regulatory networks (GRNs), etc. The idea of unifying or connecting related computational problems, such that a unified framework can involve or output the information that is previously used in multiple individual computational problems, is proposed. In particular, a unified framework for cell temporal dynamics analysis involving related computational tasks, is presented. So far the multi-omics integration methods often deploy the integrated data to cluster cells for cell type identification. Few methods on data integration are designed for temporal analysis with continuous populations, or to learn biological mechanisms like GRNs. So another direction proposed here is to infer the trajectory of cells with both gene-expression (scRNA-seq) and chromatin accessibility (scATAC-seq) data; with the inferred trajectory, the effect of chromatin accessibility on gene-expression can be studied, and GRNs can be reconstructed while taking into account this effect. In reality, a gene’s expression level is determined by multiple factors: its transcription factor (TF), its chromatin accessibility and the signal a cell receives from other neighboring cells through cell-cell interaction. Therefore, another important dimension to consider about the cells is the spatial location of cells. It is proposed to ßreconstruct a generalized GRN which models inter-cell regulatory interactions.
