论文标题：Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data

期刊：

作者：Nicolas F. Fernandez, Gregory W. Gundersen, Adeeb Rahman, Mark L. Grimes, Klarisa Rikova, Peter Hornbeck, Avi Ma’ayan

发表时间：2017/10/10

数字识别码：10.1038/sdata.2017.151

原文链接：

生物明升手机版研究领域高通量实验方法的多样性正在迅速增长。尽管数据采集的速度不断加快，我们从这些数据中得出有效结论的能力却有所滞后。数据可视化是生物数据初步分析的主要步骤，一些常用的降维方法，例如主分量分析（PCA）和t分布领域嵌入算法（t-SNE），常常被用于将高维数据映射至二维或三维空间中，以实现可视化。然而，由高维空间向低维空间转换的代价很大，经常导致信息的丢失。与之相对，聚类图（clustergram）或者热图技术可直接将数据进行可视化而无需降维。聚类图易于解释，广泛应用于生物数据可视化的印刷出版物中。

大多数分层聚类热图可视化工具产生的都是静态图像。发表在《app数据》的一篇研究Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data中，来自美国依坎明升手机版院的AviMa’ayan及同事向我们详细介绍了Clustergrammer。Clustergrammer是一个基于网络的可视化工具，拥有许多交互特征，如缩放、平移、过滤、重排、分享、执行富集分析、提供动态基因注释等。Clustergrammer可通过向网站上传数据表或嵌入至Jupyter Notebooks，产生可分享的交互式可视化结果。开发者甚至可以将Clustergrammer的核心库作为一个工具箱，嵌入自己的应用程序来实现可视化。

作者利用不同的数据演示了Clustergrammer的功能：癌细胞系百科全书（CCLE）的基因表达数据，利用质谱分析法从肺癌细胞系采集的原始翻译后修饰数据，以及质谱流式细胞仪测得血液中单细胞蛋白质组的原始数据。结果证明，Clustergrammer可分析多种生物数据，并产生基于网络的交互式可视化图表。

图1. 交互式热图工具特征对比

摘要：Most tools developed to visualize hierarchically clustered heatmaps generate static images. Clustergrammer is a web-based visualization tool with interactive features such as: zooming, panning, filtering, reordering, sharing, performing enrichment analysis, and providing dynamic gene annotations. Clustergrammer can be used to generate shareable interactive visualizations by uploading a data table to a web-site, or by embedding Clustergrammer in Jupyter Notebooks. The Clustergrammer core libraries can also be used as a toolkit by developers to generate visualizations within their own applications. Clustergrammer is demonstrated using gene expression data from the cancer cell line encyclopedia (CCLE), original post-translational modification data collected from lung cancer cells lines by a mass spectrometry approach, and original cytometry by time of flight (CyTOF) single-cell proteomics data from blood. Clustergrammer enables producing interactive web based visualizations for the analysis of diverse biological data.

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期刊介绍： () is a peer-reviewed, open-access journal for descriptions of scientifically valuable datasets, and research that advances the sharing and reuse of scientific data. Scientific Data welcomes submissions from a broad range of research disciplines, including descriptions of big or small datasets, from major consortiums to single research groups. Scientific Data primarily publishes Data Descriptors, a new type of publication that focuses on helping others reuse data, and crediting those who share.

The 2017 journal metrics for Scientific Data are as follows:

•2-year impact factor: 5.305

•5-year impact factor: 5.862

•Immediacy index: 0.843

•Eigenfactor® score: 0.00855

•Article Influence Score: 2.597

•2-year Median: 2

（来源：明升手机版(明升中国)）