Department of medical biochemistry and microbiology, Uppsala University, Uppsala, Sweden.
Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, Sweden.
BMC Genomics. 2018 Dec 27;19(1):964. doi: 10.1186/s12864-018-5300-y.
Studies that aim at explaining phenotypes or disease susceptibility by genetic or epigenetic variants often rely on clustering methods to stratify individuals or samples. While statistical associations may point at increased risk for certain parts of the population, the ultimate goal is to make precise predictions for each individual. This necessitates tools that allow for the rapid inspection of each data point, in particular to find explanations for outliers.
ACES is an integrative cluster- and phenotype-browser, which implements standard clustering methods, as well as multiple visualization methods in which all sample information can be displayed quickly. In addition, ACES can automatically mine a list of phenotypes for cluster enrichment, whereby the number of clusters and their boundaries are estimated by a novel method. For visual data browsing, ACES provides a 2D or 3D PCA or Heat Map view. ACES is implemented in Java, with a focus on a user-friendly, interactive, graphical interface.
ACES has been proven an invaluable tool for analyzing large, pre-filtered DNA methylation data sets and RNA-Sequencing data, due to its ease to link molecular markers to complex phenotypes. The source code is available from https://github.com/GrabherrGroup/ACES .
旨在通过遗传或表观遗传变异来解释表型或疾病易感性的研究，通常依赖于聚类方法来对个体或样本进行分层。虽然统计学关联可能指向某些人群的风险增加，但最终目标是对每个个体进行精确预测。这需要能够快速检查每个数据点的工具，特别是要找到异常值的解释。
ACES 是一个集成的聚类和表型浏览器，它实现了标准的聚类方法，以及多种可视化方法，其中可以快速显示所有样本信息。此外，ACES 可以自动挖掘出一组表型进行聚类富集，其中通过一种新方法估计聚类的数量和边界。对于可视化数据浏览，ACES 提供了 2D 或 3D PCA 或热图视图。ACES 是用 Java 实现的，重点是用户友好、交互性强、图形化的界面。
由于其易于将分子标记与复杂表型联系起来，ACES 已被证明是分析大型预过滤 DNA 甲基化数据集和 RNA-测序数据的宝贵工具。其源代码可从 https://github.com/GrabherrGroup/ACES 获得。
