Van den Berge Koen, Soneson Charlotte, Robinson Mark D, Clement Lieven
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, 9000, Belgium.
Bioinformatics Institute Ghent, Ghent University, Ghent, 9000, Belgium.
Genome Biol. 2017 Aug 7;18(1):151. doi: 10.1186/s13059-017-1277-0.
RNA sequencing studies with complex designs and transcript-resolution analyses involve multiple hypotheses per gene; however, conventional approaches fail to control the false discovery rate (FDR) at gene level. We propose stageR, a two-stage testing paradigm that leverages the increased power of aggregated gene-level tests and allows post hoc assessment for significant genes. This method provides gene-level FDR control and boosts power for testing interaction effects. In transcript-level analysis, it provides a framework that performs powerful gene-level tests while maintaining biological interpretation at transcript-level resolution. The procedure is applicable whenever individual hypotheses can be aggregated, providing a unified framework for complex high-throughput experiments.
具有复杂设计和转录本分辨率分析的RNA测序研究涉及每个基因的多个假设；然而，传统方法无法在基因水平上控制错误发现率（FDR）。我们提出了stageR，这是一种两阶段测试范式，它利用了聚合基因水平测试增加的功效，并允许对显著基因进行事后评估。该方法提供了基因水平的FDR控制，并提高了测试交互作用的功效。在转录本水平分析中，它提供了一个框架，在保持转录本水平分辨率的生物学解释的同时，进行强大的基因水平测试。只要可以聚合单个假设，该程序就适用，为复杂的高通量实验提供了一个统一的框架。
