NIAID, National Institutes of Health, Laboratory of Immune System Biology, Bethesda, MD 20892, USA; University of Cambridge, Department of Veterinary Medicine, Cambridge, UK.
NIAID, National Institutes of Health, Laboratory of Immune System Biology, Bethesda, MD 20892, USA.
Cell Syst. 2021 Apr 21;12(4):338-352.e5. doi: 10.1016/j.cels.2021.03.001. Epub 2021 Mar 24.
Hit selection from high-throughput assays remains a critical bottleneck in realizing the potential of omic-scale studies in biology. Widely used methods such as setting of cutoffs, prioritizing pathway enrichments, or incorporating predicted network interactions offer divergent solutions yet are associated with critical analytical trade-offs. The specific limitations of these individual approaches and the lack of a systematic way by which to integrate their rankings have contributed to limited overlap in the reported results from comparable genome-wide studies and costly inefficiencies in secondary validation efforts. Using comparative analysis of parallel independent studies as a benchmark, we characterize the specific complementary contributions of each approach and demonstrate an optimal framework to integrate these methods. We describe selection by iterative pathway group and network analysis looping (SIGNAL), an integrated, iterative approach that uses both pathway and network methods to optimize gene prioritization. SIGNAL is accessible as a rapid user-friendly web-based application (https://signal.niaid.nih.gov). A record of this paper's transparent peer review is included in the Supplemental information.
高通量检测中的命中选择仍然是生物学中实现组学研究潜力的关键瓶颈。广泛使用的方法，如设定截止值、优先考虑途径富集，或纳入预测的网络相互作用，提供了不同的解决方案，但也存在关键的分析权衡。这些单一方法的具体局限性，以及缺乏系统的方法来整合它们的排名，导致了可比全基因组研究报告结果的有限重叠，以及二次验证工作的昂贵效率低下。我们使用平行独立研究的比较分析作为基准，描述了每种方法的具体互补贡献，并展示了一个整合这些方法的最佳框架。我们描述了通过迭代途径组和网络分析循环（SIGNAL）进行选择，这是一种集成的、迭代的方法，它同时使用途径和网络方法来优化基因优先级。SIGNAL 可以作为一个快速易用的基于网络的应用程序使用（https://signal.niaid.nih.gov）。本文的透明同行评审记录包含在补充信息中。
