Flow cytometry bioinformatics.
Flow cytometry bioinformatics.
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DOI：
10.1371/journal.pcbi.1003365
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发表时间：
2013
影响因子：
4.3
通讯作者：
Brinkman R
中科院分区：
文献类型：
--
作者：
O'Neill K;Aghaeepour N;Spidlen J;Brinkman R
流式细胞术生物信息学是将生物信息学应用于流式细胞术数据，其涉及使用广泛的计算资源和工具存储、检索、组织和分析流式细胞术数据。流式细胞术生物信息学需要广泛使用计算统计学和机器学习技术，并有助于这些技术的发展。流式细胞术和相关方法允许对大量单细胞上的多个独立生物标志物进行定量。流式细胞术数据的多维性和通量的快速增长，特别是在2000年代，导致了各种计算分析方法，数据标准和公共数据库的创建，以共享结果。计算方法的存在有助于流式细胞术数据的预处理，识别其中的细胞群，将这些细胞群与样品进行匹配，并使用先前步骤的结果进行诊断和发现。对于预处理，这包括补偿光谱重叠，将数据转换到有利于可视化和分析的尺度上，评估数据的质量，以及对样本和实验数据进行归一化。对于群体识别，工具可用于辅助二维散点图（门控）中的群体的传统手动识别，使用降维来辅助门控，以及以各种方式在高维空间中自动找到群体。也可以以更全面的方式来表征数据，例如密度引导的二进制空间划分技术（称为概率分箱）或组合门控。最后，使用流式细胞术数据的诊断可以通过监督学习技术来辅助，并且通过高通量统计方法来发现具有生物重要性的新细胞类型，作为结合所有上述方法的管道的一部分。 开放标准、数据和软件也是流式细胞术生物信息学的关键部分。数据标准包括广泛采用的流式细胞术标准（FCS），定义了如何存储来自细胞仪的数据，以及国际细胞计数促进协会（ISAC）正在开发的几个新标准，以帮助存储有关实验设计和分析步骤的更详细信息。随着CytoBank数据库和FlowRepository数据库分别于2010年和2012年开放，开放数据正在缓慢增长，这两个数据库都允许用户自由分发其数据，而FlowRepository数据库已被ISAC推荐为符合MIFlowCyt标准的数据的首选存储库。开放式软件以一套Bioconductor软件包的形式最广泛地使用，但也可用于GenePattern平台上的Web执行。
Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.
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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.
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.
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：
48
作者：
Aghaeepour N;Finak G;FlowCAP Consortium;DREAM Consortium;Hoos H;Mosmann TR;Brinkman R;Gottardo R;Scheuermann RH
通讯作者：
Scheuermann RH
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