Novel Strategy to Quantitate Delayed Aging by Caloric Restriction

项目总结 父母应用程序建议整合来自对照的单细胞成像、ATAC-SEQ和RNA-SEQ数据 饮食小鼠和卡路里限制(CR)饮食小鼠。这些实验将提供大量的 不仅适用于AI/ML应用的多维和多模式数据，实际上还需要AI/ML 理解海量数据集的方法。然而，这些数据集来自3种不同类型的 测量(显微成像、染色质可获得性和基因表达) (成像和测序)是非常不同的。目前，所有三个数据流都是以传统方式处理的 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理、过滤和质量控制(QC)成像数据，ATAC-SEQ和 RNA序列数据。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。为了满足这些要求 挑战，我们将实施AnnData格式，该格式提供了广泛的计算效率 功能包括稀疏数据支持、Scanpy和一个PyTorch接口。以确保 Ann来自非常不同的模式(成像、ATAC-SEQ、RNA-SEQ)的数据可以一起分析，我们将进行测试 运行已经开发的计算算法，最适合高效地吸收和组合多个 组学数据，以确定推动衰老的关键因素。我们将结合来自IMAGING、ATAC-SEQ、 和rna-seq使用贝叶斯方法集成多组学数据和双曲线嵌入与原理 选择最适合的曲率和尺寸的标准。为了进一步测试嵌入数据的实用性，我们 将使用VAE和GAN测试CNN在嵌入式数据集合上的运行，以确保终端就绪和 从不同的AI/ML应用程序获得初步反馈。最后，我们将建立一个电子储存库，用于 将Python脚本和AnnData对象和结构直接传播到广泛的研究中 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.