Institute for Asthma and Allergy Prevention, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany.
Department of Statistics, LMU München, Munich, Germany.
Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa290.
Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data.
Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi's wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children's rooms between samples from two study centers (Ulm and Munich).
R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi.
Tel:+49 89 3187 43258; stefanie.peschel@mail.de.
Supplementary data are available at Briefings in Bioinformatics online.
从高通量测序数据中估计微生物关联网络是一种常见的探索性数据分析方法，旨在了解微生物群落在其自然栖息地中的复杂相互作用。统计网络估计工作流程包括多个分析步骤，包括零处理、数据归一化和计算微生物关联的方法。由于微生物相互作用可能会在不同条件下发生变化，例如在健康个体和患者之间，因此识别组间网络差异通常是一个基本的二次分析步骤。然而，到目前为止，还没有一个统一的计算工具可以方便地从高通量测序数据中构建、分析和比较微生物关联网络。
在这里，我们介绍了 NetCoMi（微生物组数据的网络构建和比较），这是一个 R 包，它将每个分析步骤的现有方法集成到一个可重复的计算工作流程中。该软件包提供了构建和分析单个微生物关联网络以及量化网络差异的功能。这可以深入了解是否在组间发生了单个分类群、分类群组或整个网络结构的变化。NetCoMi 还包含构建差异网络的功能，从而可以评估两个组之间的两个分类群是否存在差异关联。此外，NetCoMi 便于构建和分析微生物样本的不相似网络，从而可以对整个微生物样本集的异质性进行高级图形总结。我们使用 GABRIELA 研究的数据来比较两个研究中心（乌尔姆和慕尼黑）的儿童房积尘样本之间的微生物关联，展示了 NetCoMi 的广泛适用性。
用于生成本文所示示例的 R 脚本作为补充数据提供。NetCoMi 包以及教程可在 https://github.com/stefpeschel/NetCoMi 上获得。
电话：+49 89 3187 43258；stefanie.peschel@mail.de。
补充数据可在Briefings in Bioinformatics 在线获取。
