Novel cell state discovery from higher-order interactions in single cell expression data

从单细胞表达数据中的高阶相互作用发现新的细胞状态

• 批准号: 2671060
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2671060
• 关键词: Novel; cell; state; discovery; higher

Objectives:The aim of this project is to investigate higher-order gene interactions in single cell RNA sequencing data and examine how these interaction networks define novel cell states across developmental or disease trajectories. In particular, we aim to quantify how a set of transcription factors and their target genes interact differently under various biological conditions leading to different cell states.Novel Science:Current approaches of investigating complex biological systems often rely on using machine learning to fit parametric models, with a major drawback being that they potentially miss the ground truth, if the restrictions of the parametric fits are not aligned with the real data-generating biological process. This project will utilize novel statistical techniques for the non-parametric, multi-order estimation of symmetric interactions developed by A. Khamseh and S. Beentjes, motivated by the Targeted Learning paradigm originated by Mark Van Der Laan. The project will expand on the interaction analysis pipeline by A. Jansma, which has already shown promising results in identifying both cell types and states in an unsupervised manner, with clustering based on gene expression dependency networks. The methods generated throughout this project will expand on this work to give a more comprehensive and deeper description of mechanisms present in complex biological systems, which allows for quantitative and biologically-interpretable comparison of cellular systems at different time-points, and/or states.

目的：本项目的目的是研究单细胞RNA测序数据中的高阶基因相互作用，并研究这些相互作用网络如何在发育或疾病轨迹中定义新的细胞状态。特别是，我们的目标是量化一组转录因子和它们的靶基因如何在不同的生物条件下不同地相互作用，导致不同的细胞states.Novel Science：目前研究复杂生物系统的方法通常依赖于使用机器学习来拟合参数模型，其主要缺点是，如果参数拟合的限制与真实的数据生成生物过程不一致，则它们可能会错过基本事实。本计画将利用新颖的统计技术，对对称交互作用进行非参数、多阶估计。Khamseh和S.本杰思的动机来自于马克·货车·德·兰的目标学习范式。该项目将扩展A. Jansma已经在以无监督的方式识别细胞类型和状态方面显示出有希望的结果，并基于基因表达依赖网络进行聚类。在整个项目中产生的方法将扩展这项工作，以更全面和更深入地描述复杂生物系统中存在的机制，这允许在不同时间点和/或状态下对细胞系统进行定量和生物学可解释的比较。