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

单细胞多模式数据的机器学习分析，用于了解细胞类型功能基因组学和基因调控

• 批准号: 10505191
• 负责人: DAIFENG WANG
• 金额: $ 121.96万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10505191
• 关键词: Address; Automobile Driving; BRAIN initiative; Biological; Biological Markers; Brain; Brain Diseases; Cells; Characteristics; Communities; Complex; Computing Methodologies; Data; Data Set; Electrophysiology (science); Future; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genes; Goals; Human; Investigation; Lead; Learning; Link; Machine Learning; Maps; Methods; Modality; Molecular; Morphology; Mus; Neurosciences; Output; Pathway interactions; Phenotype; Regulator Genes; Regulatory Element; Techniques; Work; base; bioinformatics tool; cell type; data integration; deep learning; deep learning model; differential expression; functional genomics; gene discovery; gene function; gene regulatory network; heterogenous data; improved; insight; multimodal data; multimodality; neural network; neural network architecture; novel; open source; patch sequencing; predictive modeling; single cell analysis; tool; transcription factor; transcriptomics

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.

项目摘要 了解细胞类型特异性基因功能，表达动力学和调控 复杂大脑中的机制仍然具有挑战性。为此，越来越多的单- BRAIN Initiative中的细胞多模式数据可以更好地理解分子和 发生在各种细胞表型中的细胞机制如电生理学， 转录组学和形态学。因此，许多计算方法已被应用于 整合这些多模态数据以发现基因、功能和交叉模态细胞类型。 然而，这些方法中的许多输出描述性结果，例如差异表达的 各种细胞类型的基因，几乎没有提供功能和调节机制的见解。的 来自不同研究的多模态数据可能会导致不一致和偏差， 理解机制的可解释性。单细胞的整合和分析是关键 多模态数据使用连贯的，生物学上可解释的方法来解决这些问题。 因此，该项目的目标是进行机器学习分析，以整合单一的 BRAIN Initiative中的细胞多模态数据，用于预测基因功能和基因 细胞表型的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BRAIN倡议项目， 模态数据集成并发现功能性生物标志物（例如，基因，调控元件， 途径）用于各种细胞类型和细胞表型。这些细胞类型的生物标志物将 提供了一个复杂的大脑机制，可能导致新的， 可检验的、机械的和转化的生物学假说。我们将有三个目标， 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对齐单细胞 多模态和揭示细胞轨迹与连续的表型变化，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， 网络的多模式特性。在目标3中，我们将深度学习分析应用于 根据多模态数据改进细胞表型预测并区分细胞类型基因的优先级 表型的调节机制。最后，我们所有的分析都将是开源的， 作为通用生物信息学工具公开提供。