hCoCena：转录组学数据集的水平整合与分析。
hCoCena: horizontal integration and analysis of transcriptomics datasets.
机构信息
Modular High Performance Computing and Artificial Intelligence, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany.
Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V., 53127 Bonn, Germany.
出版信息
Bioinformatics. 2022 Oct 14;38(20):4727-4734. doi: 10.1093/bioinformatics/btac589.
MOTIVATION
Transcriptome-based gene co-expression analysis has become a standard procedure for structured and contextualized understanding and comparison of different conditions and phenotypes. Since large study designs with a broad variety of conditions are costly and laborious, extensive comparisons are hindered when utilizing only a single dataset. Thus, there is an increased need for tools that allow the integration of multiple transcriptomic datasets with subsequent joint analysis, which can provide a more systematic understanding of gene co-expression and co-functionality within and across conditions. To make such an integrative analysis accessible to a wide spectrum of users with differing levels of programming expertise it is essential to provide user-friendliness and customizability as well as thorough documentation.
RESULTS
This article introduces horizontal CoCena (hCoCena: horizontal construction of co-expression networks and analysis), an R-package for network-based co-expression analysis that allows the analysis of a single transcriptomic dataset as well as the joint analysis of multiple datasets. With hCoCena, we provide a freely available, user-friendly and adaptable tool for integrative multi-study or single-study transcriptomics analyses alongside extensive comparisons to other existing tools.
AVAILABILITY AND IMPLEMENTATION
The hCoCena R-package is provided together with R Markdowns that implement an exemplary analysis workflow including extensive documentation and detailed descriptions of data structures and objects. Such efforts not only make the tool easy to use but also enable the seamless integration of user-written scripts and functions into the workflow, creating a tool that provides a clear design while remaining flexible and highly customizable. The package and additional information including an extensive Wiki are freely available on GitHub: https://github.com/MarieOestreich/hCoCena. The version at the time of writing has been added to Zenodo under the following link: https://doi.org/10.5281/zenodo.6911782.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
基于转录组的基因共表达分析已成为理解和比较不同条件和表型的结构化和上下文化的标准程序。由于具有多种条件的大型研究设计既昂贵又费力，因此仅利用单个数据集进行广泛比较会受到阻碍。因此，需要有工具可以整合多个转录组数据集，然后进行联合分析，这可以更系统地理解条件内和条件间的基因共表达和共功能。为了使具有不同编程专业知识水平的广泛用户都能够进行这种综合分析，提供用户友好性和可定制性以及全面的文档是至关重要的。
结果
本文介绍了水平 CoCena（hCoCena：共表达网络的水平构建和分析），这是一个用于网络共表达分析的 R 包，它允许分析单个转录组数据集，也可以联合分析多个数据集。通过 hCoCena，我们提供了一个免费、用户友好且可适应的工具，用于整合多研究或单研究转录组分析，并与其他现有工具进行广泛比较。
可用性和实现
hCoCena R 包与 R Markdowns 一起提供，这些 Markdowns 实现了一个示例分析工作流程，包括广泛的文档以及数据结构和对象的详细描述。这些努力不仅使工具易于使用，而且还能够将用户编写的脚本和函数无缝集成到工作流程中，从而创建一个具有清晰设计但同时保持灵活和高度可定制的工具。该软件包以及其他信息（包括一个广泛的 Wiki）可在 GitHub 上免费获得：https://github.com/MarieOestreich/hCoCena。在撰写本文时，该版本已被添加到 Zenodo 中，链接如下：https://doi.org/10.5281/zenodo.6911782。
补充信息
补充数据可在 Bioinformatics 在线获取。
Zotero 插件