Meehan Stephen, Walther Guenther, Moore Wayne, Orlova Darya, Meehan Connor, Parks David, Ghosn Eliver, Philips Megan, Mitsunaga Erin, Waters Jeffrey, Kantor Aaron, Okamura Ross, Owumi Solomon, Yang Yang, Herzenberg Leonard A, Herzenberg Leonore A
Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Immunol Res. 2014 May;58(2-3):218-23. doi: 10.1007/s12026-014-8519-y.
Nowadays, one can hardly imagine biology and medicine without flow cytometry to measure CD4 T cell counts in HIV, follow bone marrow transplant patients, characterize leukemias, etc. Similarly, without flow cytometry, there would be a bleak future for stem cell deployment, HIV drug development and full characterization of the cells and cell interactions in the immune system. But while flow instruments have improved markedly, the development of automated tools for processing and analyzing flow data has lagged sorely behind. To address this deficit, we have developed automated flow analysis software technology, provisionally named AutoComp and AutoGate. AutoComp acquires sample and reagent labels from users or flow data files, and uses this information to complete the flow data compensation task. AutoGate replaces the manual subsetting capabilities provided by current analysis packages with newly defined statistical algorithms that automatically and accurately detect, display and delineate subsets in well-labeled and well-recognized formats (histograms, contour and dot plots). Users guide analyses by successively specifying axes (flow parameters) for data subset displays and selecting statistically defined subsets to be used for the next analysis round. Ultimately, this process generates analysis "trees" that can be applied to automatically guide analyses for similar samples. The first AutoComp/AutoGate version is currently in the hands of a small group of users at Stanford, Emory and NIH. When this "early adopter" phase is complete, the authors expect to distribute the software free of charge to .edu, .org and .gov users.
如今，人们很难想象没有流式细胞术的生物学和医学。流式细胞术可用于测量HIV患者的CD4 T细胞计数、跟踪骨髓移植患者、鉴别白血病等。同样，如果没有流式细胞术，干细胞应用、HIV药物研发以及免疫系统中细胞和细胞相互作用的全面表征都将面临黯淡的前景。尽管流式细胞仪有了显著改进，但用于处理和分析流式数据的自动化工具的开发却严重滞后。为了弥补这一不足，我们开发了自动化流式分析软件技术，暂命名为AutoComp和AutoGate。AutoComp从用户或流式数据文件中获取样本和试剂标签，并利用这些信息完成流式数据补偿任务。AutoGate用新定义的统计算法取代了当前分析软件包提供的手动子集划分功能，这些算法能以清晰标记和易于识别的格式（直方图、等高线图和点图）自动、准确地检测、显示和描绘子集。用户通过依次指定用于数据子集显示的轴（流式参数）并选择用于下一轮分析的统计定义子集来指导分析。最终，这个过程会生成分析“树”，可用于自动指导对类似样本的分析。AutoComp/AutoGate的首个版本目前掌握在斯坦福大学、埃默里大学和美国国立卫生研究院的一小部分用户手中。当这个“早期采用者”阶段结束后，作者预计将向.edu、.org和.gov用户免费分发该软件。
