OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis.
OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis.
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DOI：
10.1371/journal.pcbi.1003806
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发表时间：
2014-08
影响因子：
4.3
通讯作者：
Gottardo R
中科院分区：
文献类型：
--
作者：
Finak G;Frelinger J;Jiang W;Newell EW;Ramey J;Davis MM;Kalams SA;De Rosa SC;Gottardo R
流式细胞术越来越多地应用于癌症、免疫学和疫苗的临床研究。细胞计数仪器的技术进步正在增加数据集的大小和维度，对传统的数据管理和分析提出了挑战。尽管人们普遍认为自动化分析方法对该领域未来的重要性，但其广泛采用的速度却很缓慢。在这里，我们介绍 OpenCyto，这是一种新的 BioConductor 基础设施和数据分析框架，旨在通过解决我们认为阻碍自动化方法更广泛采用的关键领域来降低自动化流数据分析算法的进入门槛。 OpenCyto 支持稳健且可重复的端到端数据分析，同时生成易于解释的结果。我们改进了现有的、广泛使用的核心 BioConductor 流式细胞术基础设施，允许分析以内存有效的方式扩展到临床试验中出现的大型流数据集，并通过细胞群之间的层次关系将特定领域的知识集成为管道的一部分。管道通过基于文本的 csv 文件定义，限制了编写特定于数据的代码的需要，并且与数据无关，以简化核心设施的重复分析。我们演示了如何分析两个大型细胞计数数据集：来自已发表的 HIV 疫苗试验的细胞内细胞因子染色 (ICS) 数据集，专注于检测稀有的抗原特异性 T 细胞群，其中我们识别出具有无法通过手动分析识别的疫苗方案特异性反应的新 CD8 T 细胞亚群，以及 CyTOF T 细胞表型数据集，其中大量 染色组和许多细胞群对传统分析来说是一个挑战。对核心 BioConductor 流式细胞仪软件包的重大改进使 OpenCyto 具有广泛采用的潜力。它可以快速利用计算细胞术的新发展，并促进统一环境中的可重复分析。
Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.
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影响因子：
--
作者：
Finak G;Bashashati A;Brinkman R;Gottardo R
通讯作者：
Gottardo R
影响因子：
3.5
作者：
Bashashati A;Johnson NA;Khodabakhshi AH;Whiteside MD;Zare H;Scott DW;Lo K;Gottardo R;Brinkman FS;Connors JM;Slack GW;Gascoyne RD;Weng AP;Brinkman RR
通讯作者：
Brinkman RR
影响因子：
2.2
作者：
Horton, Helen;Thomas, Evan P.;De Rosa, Stephen C.
通讯作者：
De Rosa, Stephen C.
影响因子：
4.3
作者：
Cron A;Gouttefangeas C;Frelinger J;Lin L;Singh SK;Britten CM;Welters MJ;van der Burg SH;West M;Chan C
通讯作者：
Chan C
影响因子：
--
作者：
Kotecha, Nikesh;Krutzik, Peter O;Irish, Jonathan M
通讯作者：
Irish, Jonathan M