Primity Cloud: High-Performance Cytometry Analysis Engine

Primity Cloud：高性能细胞计数分析引擎

• 批准号: 9348504
• 负责人: Peter Krutzik
• 金额: $ 72.54万
• 依托单位: PRIMITY, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-19 至 2018-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9348504
• 关键词: Applications Grants; Architecture; Benchmarking; Big Data; Bioinformatics; Cells; Charts and Tables; Clinical Research; Clinical Trials; Cloud Computing; Collaborations; Complex; Computer software; Computers; Cytometry; Data; Data Aggregation; Data Analyses; Data Display; Data Set; Development; Diagnostic; Disease; Drug Monitoring; Explosion; Financial compensation; Flow Cytometry; Future; Goals; Grant; Graph; Imagery; Immune; Immune System Diseases; Immune system; Immunologic Monitoring; Immunooncology; Individual; Industry; Internet; Intuition; Laboratories; Management Information Systems; Measures; Metadata; Modernization; Patient Monitoring; Patients; Performance; Phase; Programming Languages; Research; Research Infrastructure; Research Personnel; Running; Sample Size; Sampling; Secure; Series; Software Engineering; Technology; Testing; Time; Visualization software; Work; application programming interface; cloud based; cluster computing; data visualization; design; drug discovery; experimental study; gigabyte; insight; next generation; programs; screening; statistics; targeted cancer therapy; tool; usability; web-based tool

Project Summary Flow cytometry is an indispensable tool for interrogating the immune system in research, drug discovery and diagnostic applications. The platform has undergone tremendous expansion over the past decade and now, modern cytometers are able to analyze 10 to 40 parameters per single cell, for millions of cells per sample, and hundreds of samples per experiment. However, as the number of parameters, samples, and experiments increases, so too does the data analysis burden. Current flow cytometry software is unable to meet the needs of cutting-edge applications due to limitations in computational power and lack of advanced data visualization tools. In this proposal, we describe the further development of our cloud-based cytometry software that is built upon a proprietary distributed computing engine. Harnessing the power of elastic cloud computing, our solution can analyze data faster than all currently available applications, and can scale to handle even the most massive data sets. In this grant, we will further harden core features such as gating and compensation, implement data visualization tools that are directly connected to the raw data, and enable analysis across multiple experiments. The result of our work will be an end-to-end solution for advanced flow cytometry analysis, built upon a scalable, secure, and distributed cloud architecture that can meet the demands of current, as well as future, researchers.

项目摘要 流式细胞术是研究中询问免疫系统的不可或缺的工具， 药物发现和诊断应用。该平台经历了巨大的 在过去的十年里，现代细胞仪能够分析10到40个 每个单细胞的参数，每个样本数百万个细胞，每个样本数百个样本。 实验然而，随着参数、样本和实验数量的增加， 数据分析的负担也很重。目前的流式细胞仪软件无法满足需要 由于计算能力的限制和缺乏先进的数据， 可视化工具。在本提案中，我们描述了我们基于云的 流式细胞术软件，建立在专有的分布式计算引擎上。治理 借助弹性云计算的强大功能，我们的解决方案可以比目前所有解决方案更快地分析数据。 可用的应用程序，并可以扩展到处理即使是最庞大的数据集。在这份补助金中， 我们将进一步加强核心功能，如门控和补偿，实现数据 直接连接到原始数据的可视化工具，并支持跨 多次实验。我们的工作成果将是一个端到端的解决方案，先进的流 流式细胞术分析，建立在可扩展、安全和分布式的云架构之上， 满足当前和未来研究人员的需求。