Novel Strategy to Quantitate Delayed Aging by Caloric Restriction

项目摘要 亲本应用提出整合来自对照的单细胞成像、ATAC-seq和RNA-seq数据。 节食小鼠和热量限制（CR）节食小鼠。这些实验将提供大量的 多维和多模态数据不仅适合AI/ML应用程序，而且实际上需要AI/ML 来理解庞大的数据集。然而，这些数据集来自3种不同类型的 测量（显微镜成像、染色质可及性和基因表达），其中2种方式 （成像和测序）是非常不同的。目前，所有这三种数据流都以传统方式处理 主要使用非常大的Excel文件;这种数据结构/格式不适合AI/ML集成。因此 需要单独的方法来清洁，过滤和成像数据的质量控制（QC），ATAC-seq和 RNA-seq数据下一个关键步骤是以类似的格式组织这些不同的数据集，以简化存储 以及使用AI/ML算法处理和启用下游集成和分析。满足这些 挑战，我们将实施AnnData格式，它提供了广泛的计算效率 功能包括稀疏数据支持、Scanpy和PyTorch接口等。确保 AnnData来自非常不同的模式（成像，ATAC-seq，RNA-seq）可以一起分析，我们将测试 运行已经开发的计算算法，最适合有效地吸收和联合收割机多， 组学数据，以确定驱动衰老的关键因素。我们将联合收割机AnnData对象从成像，ATAC-seq， 和RNA-seq使用贝叶斯方法整合多组学数据和双曲线嵌入， 选择最佳拟合曲率和尺寸的标准。为了进一步测试嵌入数据的实用性， 将在嵌入数据的集合上测试运行CNN与VAE和GAN，以确保端点准备就绪， 从不同的AI/ML应用程序中获取初始反馈。最后，我们将建立一个电子储存库， python脚本和AnnData对象和结构，用于直接传播到广泛的研究 社区

PROJECT SUMMARY The parental application proposed to integrate single cell imaging, ATAC-seq, and RNA-seq data from control diet mice and caloric restriction (CR) diet mice. These experiments will provide a large amount of multidimensional and multimodal data that not just suited for AI/ML applications, but in fact, require AI/ML approaches to make sense of the vast datasets. However, these datasets are coming from 3 different type of measurements (microscopic imaging, chromatin accessibility, and gene expression) of which 2 modalities (imaging and sequencing) are very different. At present all three data streams are handled in conventional ways primarily using very large Excel files; this data structure / format is not suitable for AI/ML integration. Hence the need for individual approaches to cleaning, filtering, and quality control (QC) of imaging data, ATAC-seq and RNA-seq data. Next critical step is to structure these diverse datasets in a similar format that streamlines storage and handling and enabling downstream integration and analyses using AI/ML algorithms. To meet these challenges, we will implement the AnnData format, which offers a broad range of computationally efficient features including, among others, sparse data support, Scanpy, and a PyTorch interface. To ensure that AnnData from very different modalities (imaging, ATAC-seq, RNA-seq) could be analyzed together, we will test run already developed computational algorithms that best suited to efficiently assimilate and combine multi- omics data to identify key factors that drive aging. We will combine AnnData objects from imaging, ATAC-seq, and RNA-seq using Bayesian methods for integrating multi-omics data and hyperbolic embedding with principled criteria for choosing the best-fitting curvature and dimension. To further test the utility of the embedded data we will test run CNN with VAEs and GAN on the ensemble of embedded data to ensure the endpoint readiness and to obtain initial feedback from different AI/ML applications. Finally, we will establish an electronic repository for python scripts and AnnData objects and structures for a straightforward dissemination to the broad research community.