Studying temporal dynamics and regulatory mechanisms of single cells with a unified framework and multi-omics data

单细胞基因组学技术使研究人员能够研究单细胞之间的差异。按顺序 为了了解每个细胞如何在整个活着的有机体中发挥作用，细胞需要在 无论是时间还是空间。研究人员想要了解每一个细胞的全面情况，包括它的 当前单元状态和预测的未来状态，以及它在时间段内如何与相邻单元交互 动力学。因此，在获得一定数量的单细胞知识后，步骤是从“部件”到 “完整”。这项建议讨论了可以为时间动力学研究带来的进展 以及细胞之间的空间相互作用。这项提议的主题是“融合”。 现有的单元格数据集成方法主要集中在两个场景：1)集成相同类型的数据(例如， RNA-Seq数据)；2)集成在同一细胞上执行的多种类型的数据，这是 也称为多模式数据或多组学数据。本提案强调了以下概念和方法： 整合多组学数据以了解细胞时间动力学和调控机制，同时 考虑到数据模式之间的依赖性，这在当前方法中是很少的。一种新的 “问题集成”的概念，其中相关的计算公式可以与 提供了细胞某一方面的一致和更一般的图像，这里也给出了。 为了研究细胞的不同方面，已经提出了不同的计算问题，例如， 细胞聚集、细胞轨迹推断、基因调控网络(GRN)推断等。 统一或连接相关的计算问题，以便统一的框架可以涉及或 输出以前在多个单独的计算问题中使用的信息。在……里面 特别是，细胞时间动力学分析的统一框架，涉及相关的计算 任务，并提出了相应的解决方案。 到目前为止，多组学集成方法通常将集成的数据部署到细胞类型的聚类细胞上 身份证明。很少有数据集成方法被设计用于具有连续总体的时态分析， 或者学习像GRN这样的生物机制。因此，这里提出的另一个方向是推断出 同时具有基因表达(scRNA-seq)和染色质可及性(scATAC-seq)数据的细胞； 轨迹，染色质可及性对基因表达的影响可以被研究，GRN可以被 在考虑到这一影响的同时进行了重建。 事实上，一个基因的表达水平由多种因素决定：它的转录因子(Tf)，它的染色质 可达性和一个细胞通过细胞-细胞相互作用从其他邻近细胞接收的信号。因此， 关于单元的另一个需要考虑的重要维度是单元的空间位置。现建议： ？重建模拟细胞间调控相互作用的广义GRN。

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.