ACES: a machine learning toolbox for clustering analysis and visualization.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
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 .
['Gao|Jiangning|J|', 'Sundström|Görel|G|', 'Moghadam|Behrooz Torabi|BT|', 'Zamani|Neda|N|', 'Grabherr|Manfred G|MG|']
D016000:Cluster Analysis D019175:DNA Methylation D003922:Diabetes Mellitus, Type 1 D006801:Humans D000077230:Internet Access D025341:Principal Component Analysis D012313:RNA D014584:User-Computer Interface
Clustering Centroid detection Data visualization Discriminative power prediction