García-Campos Miguel A, Espinal-Enríquez Jesús, Hernández-Lemus Enrique
Computational Genomics, National Institute of Genomic Medicine México City, México.
Computational Genomics, National Institute of Genomic MedicineMéxico City, México; Complejidad en Biología de Sistemas, Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de MéxicoCiudad de México, México.
Front Physiol. 2015 Dec 17;6:383. doi: 10.3389/fphys.2015.00383. eCollection 2015.
Pathway analysis is a set of widely used tools for research in life sciences intended to give meaning to high-throughput biological data. The methodology of these tools settles in the gathering and usage of knowledge that comprise biomolecular functioning, coupled with statistical testing and other algorithms. Despite their wide employment, pathway analysis foundations and overall background may not be fully understood, leading to misinterpretation of analysis results. This review attempts to comprise the fundamental knowledge to take into consideration when using pathway analysis as a hypothesis generation tool. We discuss the key elements that are part of these methodologies, their capabilities and current deficiencies. We also present an overview of current and all-time popular methods, highlighting different classes across them. In doing so, we show the exploding diversity of methods that pathway analysis encompasses, point out commonly overlooked caveats, and direct attention to a potential new class of methods that attempt to zoom the analysis scope to the sample scale.
通路分析是生命科学研究中广泛使用的一组工具,旨在赋予高通量生物学数据以意义。这些工具的方法基于生物分子功能相关知识的收集和使用,再加上统计检验和其他算法。尽管它们被广泛应用,但通路分析的基础和整体背景可能并未被充分理解,从而导致对分析结果的误解。本综述试图涵盖在将通路分析用作假设生成工具时需要考虑的基础知识。我们讨论了这些方法的关键要素、它们的能力和当前的不足。我们还概述了当前和一直流行的方法,突出了它们之间的不同类别。通过这样做,我们展示了通路分析所涵盖的方法的爆炸式多样性,指出了通常被忽视的注意事项,并将注意力引向一类潜在的新方法,这类方法试图将分析范围缩小到样本规模。