CalR: A toolkit and repository for experiments of energy homeostasis using indirect calorimetry

项目摘要 现代间接量热系统允许高密度多维时间序列测量 影响体重和能量平衡的成分。间接量热法是用来理解 影响体重变化的因素--例如因癌症恶病质而导致的体重减轻，或 肥胖会导致体重增加。这些间接量热系统产生了大量需要时间的原始数据- 在格式化、数据清理、质量控制和可视化方面耗费大量人工操作。超越数据 间接量热实验的处理、分析需要专门的统计处理来说明 脂肪质量和瘦质量对代谢率的不同贡献。令人惊讶的是，不存在任何工具或资源 来解决这些缺点。由于不同的实验，很少进行实验之间的比较 具有不同测量单位的仪器类型和对数据的特殊统计处理。致信地址 在这种迫切的需求下，我们建议创建一个免费的在线工具CALR，它可以快速有效地帮助科学家 通过为可重复研究和网站提供标准化方法来分析间接量热数据 存储和聚合数据集。我们推出的CALR的初步版本是一个用户友好但复杂的版本 使用图形用户界面从不同仪器导入数据文件的Web工具，快速可视化 实验结果，并进行基本的统计分析。经过几年的迭代开发，在 除了来自用户调查的建设性反馈外，很明显，额外的功能和统计 需要开发方法来实现这个项目的潜力。这项研究的广泛目标是 开发一个提供现代工具的框架，用于分析影响身体的生理数据 重量。这些建立在现有CALR软件基础上的新工具将继续免费提供给科学工作者 社区。具体目标将建立该框架的三个方面：1)新的分析功能旨在 改进对体重变化的预测，自动确定代谢灵活性，改进 代谢率的测定，以及更好的可视化。2)采用新的统计方法 协变量转化为分析和一个模块，用于确定代谢实验的统计能力。3) 开发一个储存库，其中可以存储间接量热数据，并使其能够广泛地供 大规模的分析。对于每个目标，我们将开发并彻底测试每个组件，之后我们将 提供一个功能强大的在线网络工具，与使用我们免费软件的数千名科学家分享 站台。这些努力将提取更多细微差别的信息，并将改善对每个信息的解释 为体重影响疾病进程的所有领域的用户进行实验。

Project Summary Modern indirect calorimetry systems allow for high-density multi-dimensional time-series measurements of components affecting body weight and energy homeostasis. Indirect calorimetry is used for understanding the factors influencing pathogenic changes to body weight—examples include weight loss with cancer cachexia, or weight gain with obesity. These indirect calorimetry systems generate a flood of raw data that requires time- consuming manual manipulation for formatting, data cleaning, quality control, and visualization. Beyond data handling, analysis of indirect calorimetry experiments requires specialized statistical treatment to account for differential contributions of fat mass and lean mass to metabolic rates. Surprisingly, no tools or resources exist to address these shortcomings. Comparisons between experiments are rarely performed due to the different types of instruments with varying units of measurement and ad hoc statistical treatments of data. To address this critical need, we propose the creation of a free online tool, CalR, that helps scientists quickly and efficiently analyze indirect calorimetry data by providing standardized methods for reproducible research and a site to store and aggregate datasets. The preliminary version of CalR we launched is a user-friendly but sophisticated web tool that uses a graphical user interface to import data files from different instruments, quickly visualize experimental results, and perform basic statistical analyses. After several years of iterative development, in addition to constructive feedback from a user survey, it is clear that additional functionality and statistical methods need to be developed to realize the potential of this project. The broad goal of this research is the development of a framework that delivers modern tools for the analysis of the physiological data affecting body weight. These new tools, built on the existing CalR software, will continue to be freely provided to the scientific community. Specific aims will establish three aspects of this framework: 1) New analysis features intended to improve prediction of body weight change, automatic determination of metabolic flexibility, improved determination of metabolic rates, and better visualizations. 2) New statistical methods incorporating additional covariates into analysis and a module to determine statistical power for metabolic experiments. 3) The development of a repository where indirect calorimetry data can be deposited and made broadly accessible for large-scale analysis. For each aim, we will develop and thoroughly test each component, after which we will provide a functional web tool online to share with the thousands of scientists who use our free software platform. These efforts will extract more nuanced information and will improve the interpretation of each experiment for users in all fields where body weight impacts disease processes.