Software for the Integration of Multiomics Experiments in Bioconductor
Software for the Integration of Multiomics Experiments in Bioconductor
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DOI：
10.1158/0008-5472.can-17-0344
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发表时间：
2017-11-01
期刊：
影响因子：
11.2
通讯作者：
Waldron, Levi
中科院分区：
文献类型：
--
作者：
Ramos, Marcel;Schiffer, Lucas;Waldron, Levi
多组学实验在生物医学研究中越来越普遍，并增加了实验设计、数据集成和分析的复杂性。 R和Bioconductor提供了统计分析和可视化的通用框架，以及各种高通量数据类型的专门数据类，但缺乏多组学实验的综合分析方法。 MultiAssayExperiment 软件包在 R 中实现并利用 Bioconductor 软件和设计原理，提供多种不同基因组数据的协调表示、存储和操作。我们为癌症基因组图谱中的每个癌症组织提供不受限制的多组学数据作为准备分析的 MultiAssayExperiment 对象，并在这些和其他数据集中演示该软件如何简化数据表示、统计分析和可视化。 MultiAssayExperiment Bioconductor 软件包减少了对多组学数据进行高效、可扩展和可重复统计分析的主要障碍，并增强了多组学数据集的数据科学应用。
Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets.