Integrative Approaches for Spatial and Multi-Omics data

基因组技术与现代数据分析相结合是揭示各种生物学问题新见解的有力方法。最近，这些方法已经扩展到空间维度，在二维组织样本的每个点上测量数千个基因。然而，很少有工具能够将大量生物数据的分析与空间信息相结合。相反，空间分析留给生物学家亲自注释。因此，在开发空间多组学分析工具方面存在空白，这些工具可以学习生物学上有意义的潜在变量并使用这些变量进行推理和预测。空间多组学由多个模态的计数矩阵组成。一些模态具有部分重叠的变量，即具有计数矩阵和空间信息的模态，而另一些模态具有自然分组的变量。因此，我们可以认为空间多组学是由潜在变量过程产生的，该过程捕获了模态内部和模态之间变量的共现、非线性相互作用和自然分组。我的研究项目的首要长期目标是：1)开发变革性的统计和机器学习方法，将复杂的、跨多种模式的真实数据集成在一起；2)在表征其性能方面提高知识水平。例如，我最近的研究表明，概率潜在狄利克雷分配识别了细菌群落中代表的功能同义词变量。该发现基金的主要目标是开发综合方法来发现空间多组学中的潜在变量，并使用这些变量建立贝叶斯层次模型进行预测和推理。该研究将我们目前的方法发展扩展到微生物组多组学，从而实现潜在变量和空间特征的提取。本研究的短期目标是：1。研究空间多组学的概率图形模型，包括半监督方法；开发贝叶斯层次模型用于预测和推理；研究空间多组学整合方法的贝叶斯抽样方法和模型评估。我们从我的合作者那里获得了人类大脑和肠道微生物组的空间多组学数据。拥有两个数据集将使我们能够利用领域专家的知识、参考模型和仿真研究来评估综合方法的性能。这些研究将为揭示生物异质性和空间特征提供新的分析工具，用于空间多组学的预测和推理。这些工具将作为开源库提供，以方便它们的使用。我们的工作可能会改变多种数据模式，包括空间。因此，这项工作将影响许多学科，如生物学，生态学，农业，金融。

Genomic technologies combined with modern data analytics are powerful approaches to revealing new insights into various biological questions. Recently, those approaches have expanded to the spatial dimension where thousands of genes are measured at each point across a 2D tissue sample. However, few tools exist to integrate the analysis of large amounts of biological data with spatial information. Instead, the spatial analyses are left to hands-on annotation by an expert biologist. Thus, there is a gap in developing analysis tools for spatial multi-omics that learn biologically meaningful-latent variables and use those variables for inference and prediction. Spatial multi-omics consists of count matrices from multiple modalities. Some modalities have partially overlapping variables, a modality with count matrix and spatial information, while others have a natural grouping of variables. Thus, we can consider that spatial multi-omics is generated from an underlying latent variable process that captures co-occurrence, non-linear interaction, and natural grouping of variables within and across modalities. The overarching long-term goal of my research program is to 1) develop transformative statistical and machine learning methods that integrate complex, real-world data across multiple modalities, 2) advance knowledge in characterizing their performance. For example, my recent studies have shown that probabilistic latent Dirichlet allocation identifies functional synonyms variables represented in bacterial communities. The main objective of this Discovery grant is to develop integrative approaches to discover latent variables in spatial multi-omics and use those variables to build Bayesian hierarchical models for prediction and inference. The studies extend our current methods development into microbiome multi-omics, leading to latent variable and spatial features extraction. The short-term objectives of this research are to 1. Investigate probabilistic graphical models for spatial multi-omics, including semi-supervised methods, 2. Develop Bayesian hierarchical models for prediction and inference, 3. Investigate Bayesian sampling methods and model assessment for spatial multi-omics integrative approaches. We have spatial multi-omics data from my collaborators for the human brain and gut microbiome. Having two datasets will allow us to evaluate the performance of the integrative approaches using domain experts' knowledge, reference models, and simulation studies. The studies will lead to new analytical tools for uncovering biological heterogeneity and spatial features for prediction and inference in spatial multi-omics. The tools will be available as open-source libraries to facilitate their use. Our work is potentially transformative to multiple modalities of data, including space. Thus, this work will impact many disciplines such as biology, ecology, agriculture, finance.