Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.
Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.
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DOI：
10.1093/bioinformatics/btac780
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发表时间：
2023-01-01
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影响因子：
--
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理解网络多元关联模式对于高维分析的许多领域至关重要。不幸的是，随着数据空间维度的增长，关联对的数量以 O(n2) 的形式增加；这意味着热图等传统可视化很快就会变得过于复杂而无法有效解析。在这里，我们提出了关联子图：一种新的交互式可视化方法，可以使用网络渗透和聚类快速直观地探索高维关联数据集。目标是通过显示整个聚类动态并同时提供所有可能截止值下的子图，对关联子图进行有效的研究，每个子图包含一个变量子集，这些变量之间的关联性比子集外的其余变量更强、更频繁。特别是，我们将关联子图应用于从电子健康记录生成的全表型多病关联矩阵，并提供在线交互式演示来探索多病子图。实现 AssociationSubgraphs 的算法和可视化组件的 R 包可在 https://github.com/tbilab/associationsubgraphs 上找到。在线文档可在 https://prod.tbilab.org/associationsubgraphs_info/ 获取。使用多病关联矩阵的演示可在 https://prod.tbilab.org/associationsubgraphs-example/ 上找到。
Making sense of networked multivariate association patterns is vitally important to many areas of high-dimensional analysis. Unfortunately, as the data-space dimensions grow, the number of association pairs increases in O(n2); this means that traditional visualizations such as heatmaps quickly become too complicated to parse effectively. Here, we present associationSubgraphs: a new interactive visualization method to quickly and intuitively explore high-dimensional association datasets using network percolation and clustering. The goal is to provide an efficient investigation of association subgraphs, each containing a subset of variables with stronger and more frequent associations among themselves than the remaining variables outside the subset, by showing the entire clustering dynamics and providing subgraphs under all possible cutoff values at once. Particularly, we apply associationSubgraphs to a phenome-wide multimorbidity association matrix generated from an electronic health record and provide an online, interactive demonstration for exploring multimorbidity subgraphs. An R package implementing both the algorithm and visualization components of associationSubgraphs is available at https://github.com/tbilab/associationsubgraphs. Online documentation is available at https://prod.tbilab.org/associationsubgraphs_info/. A demo using a multimorbidity association matrix is available at https://prod.tbilab.org/associationsubgraphs-example/.
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影响因子：
2
作者：
DELEEUW, J
通讯作者：
DELEEUW, J
DOI：
10.1109/tvcg.2011.185
发表时间：
2011-12-01
影响因子：
5.2
作者：
Bostock, Michael;Ogievetsky, Vadim;Heer, Jeffrey
通讯作者：
Heer, Jeffrey
影响因子：
5.8
作者：
Strayer, Nick;Shirey-Rice, Jana K.;Xu, Yaomin
通讯作者：
Xu, Yaomin
影响因子：
14.9
作者：
Metsalu T;Vilo J
通讯作者：
Vilo J
影响因子：
9.3
作者：
Chan TE;Stumpf MPH;Babtie AC
通讯作者：
Babtie AC