GRcalculator: an online tool for calculating and mining dose-response data.
GRcalculator: an online tool for calculating and mining dose-response data.
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DOI：
10.1186/s12885-017-3689-3
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发表时间：
2017-10-24
期刊：
影响因子：
3.8
通讯作者：
Medvedovic M
中科院分区：
文献类型：
--
作者：
Clark NA;Hafner M;Kouril M;Williams EH;Muhlich JL;Pilarczyk M;Niepel M;Sorger PK;Medvedovic M
定量细胞系对药物或其他干扰物的反应是临床前药物开发和药物基因组学的基石，也是研究敏感性和耐药性因素的一种手段。在分裂细胞中，源自剂量-反应曲线的传统指标（如IC 50、AUC和E max）受到测定期间发生的细胞分裂次数的混淆，由于生物学和实验原因，细胞分裂次数变化很大。Hafner等人（Nat Meth 13：521-627，2016）最近提出了一种量化药物反应的替代方法，即标准化生长速率（GR）抑制，其对此类混杂因素具有鲁棒性。预计采用GR方法可提高剂量反应试验的重现性和药物基因组学关联的可靠性（Hafner et al. 500-502，2017）。我们在这里描述了一个交互式网站（www.grcalculator.org）的计算，分析和可视化的剂量反应数据使用GR的方法和比较GR和传统的指标。数据可以是用户提供的，也可以是从已发布的数据集派生的。Web工具以三个集成的Shiny应用程序（grcalculator、grbrowser和grtutorial）的形式实现，这些应用程序通过Shiny服务器部署。直观的图形用户界面（GUI）允许数据的交互式分析和可视化。Shiny应用程序使用了两个专门为此目的开发的R包（shinyLi和GRmetrics）。GRmetrics R软件包也可通过Bioconductor获得，可用于离线数据分析和可视化。可以在www.github.com/uc-bd2k/grcalculator和www.github.com/datarail/gr_metrics访问Shiny应用程序和相关软件包（shinyLi和GRmetrics）的源代码。 GRcalculator是一个功能强大，用户友好，免费的工具，以促进剂量反应数据的分析。它生成可供发表的数据，并为研究人员提供一个统一的平台，以分析不同细胞类型和干扰源（包括药物，生物配体，RNAi等）的剂量反应数据。GRcalculator还提供对NIH LINCS计划（http：//www.lincsproject.org/）收集的数据和其他公共领域数据集的访问。GRmetrics Bioconductor软件包为经过计算培训的用户提供了一个离线分析剂量反应数据的平台，并有助于将GR指标计算纳入现有的R分析管道。因此，这些工具非常适合学术界和工业界的用户。本文的在线版本（10.1186/s12885-017-3689-3）包含补充材料，可供授权用户使用。
Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose–response curves such as IC 50, AUC, and E max, are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521–627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose–response assays and the reliability of pharmacogenomic associations (Hafner et al. 500–502, 2017). We describe here an interactive website (www.grcalculator.org) for calculation, analysis, and visualization of dose–response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics. GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose–response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose–response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program (http://www.lincsproject.org/) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose–response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry. The online version of this article (10.1186/s12885-017-3689-3) contains supplementary material, which is available to authorized users.
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影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
48
作者：
Harris LA;Frick PL;Garbett SP;Hardeman KN;Paudel BB;Lopez CF;Quaranta V;Tyson DR
通讯作者：
Tyson DR
影响因子：
5.8
作者：
Smirnov, Petr;Safikhani, Zhaleh;Haibe-Kains, Benjamin
通讯作者：
Haibe-Kains, Benjamin
影响因子：
64.8
作者：
Haverty, Peter M.;Lin, Eva;Bourgon, Richard
通讯作者：
Bourgon, Richard
DOI：
10.1073/pnas.1018854108
发表时间：
2012-02-21
影响因子：
11.1
作者：
Heiser, Laura M.;Sadanandam, Anguraj;Spellman, Paul T.
通讯作者：
Spellman, Paul T.