Critical assessment of automated flow cytometry data analysis techniques.
Critical assessment of automated flow cytometry data analysis techniques.
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DOI：
10.1038/nmeth.2365
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发表时间：
2013-03
期刊：
影响因子：
48
通讯作者：
Scheuermann RH
中科院分区：
文献类型：
--
作者：
Aghaeepour N;Finak G;FlowCAP Consortium;DREAM Consortium;Hoos H;Mosmann TR;Brinkman R;Gottardo R;Scheuermann RH
在这项分析中，作者直接将流式细胞术数据处理算法的性能与手动设门方法进行了比较。研究结果提供了有关这些算法应用于不同数据集和挑战时性能的实用信息。
本文的网络版（doi:10.1038/nmeth.2365）包含补充材料，授权用户可获取。
流式细胞术（FCM）数据处理的传统方法依赖于主观的手动设门。最近，几个研究小组开发了用于识别多维FCM数据中细胞群体的计算方法。设立了“流式细胞术：细胞群体识别方法的关键评估（FlowCAP）”挑战，以比较这些方法在两项任务中的性能：（i）哺乳动物细胞群体识别，以确定自动化算法是否能重现专家手动设门；（ii）样本分类，以确定分析流程是否能识别与外部变量（如临床结果）相关的特征。本分析展示了首次FlowCAP挑战的结果。使用统计性能指标衡量，与手动设门或外部变量相比，有几种方法表现良好，这表明自动化方法在FCM数据分析中的可靠使用已达到足够的成熟度和准确性。
本文的网络版（doi:10.1038/nmeth.2365）包含补充材料，授权用户可获取。
In this analysis, the authors directly compared the performance of flow cytometry data processing algorithms to manual gating approaches. The results offer information of practical utility about the performance of the algorithms as applied to different data sets and challenges. The online version of this article (doi:10.1038/nmeth.2365) contains supplementary material, which is available to authorized users. Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis. The online version of this article (doi:10.1038/nmeth.2365) contains supplementary material, which is available to authorized users.
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影响因子：
3.7
作者：
Aghaeepour, Nima;Jalali, Adrin;Brinkman, Ryan R.
通讯作者：
Brinkman, Ryan R.
DOI：
10.1126/science.1198704
发表时间：
2011-05-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bendall SC;Simonds EF;Qiu P;Amir el-AD;Krutzik PO;Finck R;Bruggner RV;Melamed R;Trejo A;Ornatsky OI;Balderas RS;Plevritis SK;Sachs K;Pe'er D;Tanner SD;Nolan GP
通讯作者：
Nolan GP
影响因子：
--
作者：
Finak G;Bashashati A;Brinkman R;Gottardo R
通讯作者：
Gottardo R
影响因子：
3.7
作者：
Aghaeepour, Nima;Nikolic, Radina;Hoos, Holger H.;Brinkman, Ryan R.
通讯作者：
Brinkman, Ryan R.
影响因子：
16.8
作者：
Bendall SC;Nolan GP;Roederer M;Chattopadhyay PK
通讯作者：
Chattopadhyay PK