Xu Zhenjiang Zech, Amir Amnon, Sanders Jon, Zhu Qiyun, Morton James T, Bletz Molly C, Tripathi Anupriya, Huang Shi, McDonald Daniel, Jiang Lingjing, Knight Rob
State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, China.
Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
mSystems. 2019 Jan 29;4(1). doi: 10.1128/mSystems.00269-18. eCollection 2019 Jan-Feb.
Microbiome analyses can be challenging because microbial strains are numerous, and often, confounding factors in the data set are also numerous. Many tools reduce, summarize, and visualize these high-dimensional data to provide insight at the community level. However, they lose the detailed information about each taxon and can be misleading (for example, the well-known horseshoe effect in ordination plots). Thus, multiple methods at different levels of resolution are required to capture the full range of microbial patterns. Here we present Calour, a user-friendly data exploration tool for microbiome analyses. Calour provides a study-centric data model to store and manipulate sample-by-feature tables (with features typically being operational taxonomic units) and their associated metadata. It generates an interactive heatmap, allowing visualization of microbial patterns and exploration using microbial knowledge databases. We demonstrate the use of Calour by exploring publicly available data sets, including the gut and skin microbiota of habitat-switched fire salamander larvae, gut microbiota of Trichuris muris-infected mice, skin microbiota of different human body sites, gut microbiota of various ant species, and a metabolome study of mice exposed to intermittent hypoxia and hypercapnia. In these cases, Calour reveals novel patterns and potential contaminants of subgroups of microbes that are otherwise hard to find. Calour is open source under the Berkeley Software Distribution (BSD) license and available from https://github.com/biocore/calour. Calour allows us to identify interesting microbial patterns and generate novel biological hypotheses by interactively inspecting microbiome studies and incorporating annotation databases and convenient statistical tools. Calour can be used as a first-step tool for microbiome data exploration.
微生物组分析可能具有挑战性，因为微生物菌株数量众多，而且数据集中的混杂因素通常也很多。许多工具会对这些高维数据进行简化、汇总和可视化，以便在群落层面提供见解。然而，它们会丢失有关每个分类单元的详细信息，并且可能产生误导（例如，排序图中著名的马蹄效应）。因此，需要多种不同分辨率水平的方法来捕捉微生物模式的全貌。在这里，我们介绍Calour，这是一种用于微生物组分析的用户友好型数据探索工具。Calour提供了一个以研究为中心的数据模型，用于存储和处理样本-特征表（特征通常为操作分类单元）及其相关元数据。它会生成一个交互式热图，允许可视化微生物模式并使用微生物知识数据库进行探索。我们通过探索公开可用的数据集来演示Calour的使用，这些数据集包括栖息地转换的火蝾螈幼虫的肠道和皮肤微生物群、感染毛首鞭形线虫的小鼠的肠道微生物群、不同人体部位的皮肤微生物群、各种蚂蚁物种的肠道微生物群，以及暴露于间歇性缺氧和高碳酸血症的小鼠的代谢组研究。在这些案例中，Calour揭示了微生物亚群的新模式和潜在污染物，而这些在其他情况下很难发现。Calour在伯克利软件发行版（BSD）许可下开源，可从https://github.com/biocore/calour获取。Calour使我们能够通过交互式检查微生物组研究并纳入注释数据库和便捷的统计工具来识别有趣的微生物模式并生成新的生物学假设。Calour可以用作微生物组数据探索的第一步工具。
