ACES : a machine learning toolbox for clustering analysis and visualization
| Autor: | Jiangning Gao, Görel Sundström, Behrooz Torabi Moghadam, Neda Zamani, Manfred G. Grabherr |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Principal Component Analysis
lcsh:QH426-470 Data visualization lcsh:Biotechnology DNA Methylation Discriminative power prediction Centroid detection Clustering lcsh:Genetics User-Computer Interface Diabetes Mellitus Type 1 lcsh:TP248.13-248.65 Genetics Cluster Analysis Humans RNA Genetik Software Internet Access |
| Zdroj: | BMC Genomics BMC Genomics, Vol 19, Iss 1, Pp 1-8 (2018) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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