Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.
Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.
J Proteome Res. 2021 Apr 2;20(4):2116-2121. doi: 10.1021/acs.jproteome.0c00963. Epub 2021 Mar 11.
A generalized goal of many high-throughput data studies is to identify functional mechanisms that underlie observed biological phenomena, whether they be disease outcomes or metabolic output. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, transcriptomic, proteomic, metabolomic) are faced with a challenge of summarizing the data to generate testable hypotheses. However, this requires a time-consuming process to evaluate numerous statistical methods across numerous data sources. Here, we introduce the leapR package, a framework to rapidly assess biological pathway activity using diverse statistical tests and data sources, allowing facile integration of multisource data. The leapR package with a user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR).
许多高通量数据研究的总体目标是确定潜在观察到的生物学现象的功能机制,无论它们是疾病结果还是代谢产物。越来越多依赖于多种高通量数据(基因组、转录组、蛋白质组、代谢组)的研究面临着总结数据以生成可测试假设的挑战。然而,这需要一个耗时的过程来评估众多数据源中的众多统计方法。在这里,我们介绍 leapR 包,这是一个使用各种统计测试和数据源快速评估生物途径活性的框架,允许轻松整合多源数据。带有用户手册和示例工作流程的 leapR 包可从 GitHub(https://github.com/biodataganache/leapR)下载。