Primity Cloud: High-Performance Cytometry Analysis Engine
Primity Cloud: High-Performance Cytometry Analysis Engine
批准号:
9348504
负责人:
Peter Krutzik
金额:
$72.54万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-01-19 至 2018-12-31
关键词:
Applications GrantsArchitectureBenchmarkingBig DataBioinformaticsCellsCharts and TablesClinical ResearchClinical TrialsCloud ComputingCollaborationsComplexComputer softwareComputersCytometryDataData AggregationData AnalysesData DisplayData SetDevelopmentDiagnosticDiseaseDrug MonitoringExplosionFinancial compensationFlow CytometryFutureGoalsGrantGraphImageryImmuneImmune System DiseasesImmune systemImmunologic MonitoringImmunooncologyIndividualIndustryInternetIntuitionLaboratoriesManagement Information SystemsMeasuresMetadataModernizationPatient MonitoringPatientsPerformancePhaseProgramming LanguagesResearchResearch InfrastructureResearch PersonnelRunningSample SizeSamplingSecureSeriesSoftware EngineeringTechnologyTestingTimeVisualization softwareWorkapplication programming interfacecloud basedcluster computingdata visualizationdesigndrug discoveryexperimental studygigabyteinsightnext generationprogramsscreeningstatisticstargeted cancer therapytoolusabilityweb-based tool
中文摘要
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英文摘要
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.
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会议论文
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海外基金