Machine learning analyses of single-cell multi-modal data for understanding cell-type functional genomics and gene regulation

项目摘要 了解特定细胞类型的基因功能、表达动态和调控 复杂大脑中的机制仍然具有挑战性。为此，越来越多的单项- 大脑计划中的细胞多模式数据允许更好地理解分子和 发生在各种细胞表型中的细胞机制，如电生理学， 转录学和形态学。因此，许多计算方法被应用于 整合这些多模式数据以发现基因、功能和跨模式细胞类型。 然而，这些方法中的许多方法都输出描述性结果，例如差异表达 各种细胞类型的基因，几乎不能提供功能和调节机制的见解。这个 来自不同研究的多模式数据可能会导致不一致、偏见和缺乏 理解机制的可解释性。对单个细胞进行集成和分析是至关重要的 使用连贯的、生物学上可解释的方法来处理这些问题的多模式数据。 因此，本项目的目标是执行机器学习分析，以集成单一的 脑活动中用于预测基因功能和基因的细胞多模式数据 细胞表型调控网络和改进表型预测。我们的机器 该项目中的学习分析可以进一步服务于大脑倡议项目，使多个 模式数据集成并发现功能生物标记物(例如，基因，调节元件， 不同的细胞类型和细胞表型。这些细胞型生物标记物将 提供对复杂的大脑机制的更多了解，这些机制可能导致新的、 可测试的、机械论的和可翻译的生物学假说。我们将有三个目标来 完成这个项目。在目标1中，我们的目标是应用流形学习分析来比对单细胞 多种模式，并揭示具有持续表型变化的细胞轨迹，如基因 表达和电生理学。在目标2中，我们的目标是预测细胞类型的基因调控 用于多模式特征的网络。在目标3中，我们将把深度学习分析应用于 改进多模式数据的细胞表型预测，优先选择细胞类型基因 表型的调控机制。最后，我们所有的分析都将是开源的 可作为一般生物信息学工具公开使用。

Project Summary Understanding cell-type-specific gene functions, expression dynamics, and regulatory mechanisms in complex brains is still challenging. To this end, the increasing amount of single- cell multi-modal data in the BRAIN Initiative allows a better understanding of molecular and cellular mechanisms that occur in various cellular phenotypes such as electrophysiology, transcriptomics, and morphology. Many computational methods have thus been applied to integrate such multi-modal data for discovering genes, functions, and cross-modal cell types. However, many of these methods output descriptive results such as differentially expressed genes of various cell types, barely providing functional and regulatory mechanistic insights. The multi-modal data from different studies potentially give rise to inconsistency and bias and lack interpretability for understanding mechanisms. It is crucial to integrate and analyze single cell multi-modal data using coherent, biologically interpretable methods to address these problems. Thus, the objective of this project is to perform machine learning analyses to integrate single- cell multi-modal data in the BRAIN Initiative for predicting the gene functions and gene regulatory networks for cellular phenotypes and improving phenotype prediction. Our machine learning analyses in this project can further serve the BRAIN Initiative project to enable multi- modal data integration and discover functional biomarkers (e.g., genes, regulatory elements, pathways) for various cell types and cellular phenotypes. These cell-type biomarkers will provide an increased understanding of complex brain mechanisms that potentially lead to novel, testable, mechanistic, and translational biological hypotheses. We will have three aims to accomplish this project. In Aim 1, we aim to apply manifold learning analysis to align single-cell multi-modalities and reveal cell trajectories with continuous phenotypic changes such as gene expression and electrophysiology. In Aim 2, we aim to predict cell-type gene regulatory networks for multi-modal characteristics. In Aim 3, we will apply the deep learning analysis to improve cellular phenotype prediction from multi-modal data and prioritize cell-type gene regulatory mechanisms for phenotypes. Finally, all of our analyses will be open source and publicly available as general bioinformatics tools.