论文标题
AutoClassWeb：贝叶斯聚类的简单Web界面
AutoClassWeb: a simple web interface for Bayesian clustering
论文作者
论文摘要
目的：数据聚类是OMICS时代的常见探索步骤，尤其是在基因组学和蛋白质组学中，许多基因或蛋白质可以从一个或多个实验中征收的许多基因或蛋白质。贝叶斯聚类是一种强大的算法，可以对数千种基因或蛋白质进行分类。 AutoClass C（其原始实现）处理缺失的数据，会自动确定最佳数量群集数量，但不友好。回报：我们开发了一种名为AutoClassWeb的在线工具，该工具为使用AutoClass提供了易于使用的贝叶斯聚类的Web界面。输入数据以TSV文件输入。结果以格式提供，可以轻松使用电子表格程序或编程语言进行进一步分析，例如Python或R. AutoclassWeb在Python中实施，并根据3-Clauses BSD许可发布。源代码可用Athttps：//github.com/pierrepo/autoclassweb以及详细的文档。
Objective: Data clustering is a common exploration step in the omics era, notably in genomics and proteomics where many genes or proteins can bequantified from one or more experiments. Bayesian clustering is a powerful algorithm that can classify several thousands of genes or proteins. AutoClass C, its original implementation, handles missing data, automatically determines the best number of clusters but is not user-friendly.Results: We developed an online tool called AutoClassWeb, which provides an easy-to-use web interface for Bayesian clustering with AutoClass. Input data are entered as TSV files. Results are provided in formats that ease further analyses with spreadsheet programs or with programming languages, such as Python or R. AutoClassWeb is implemented in Python and is published under the 3-Clauses BSD license. The source code is available athttps://github.com/pierrepo/autoclassweb along with a detailed documentation.
