Hamed Haselimashhadi, Kolawole Babalola, Robert Wilson, Tudor Groza, Violeta Muñoz-Fuentes
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引用次数: 0
Abstract
Experiments in which data are collected by multiple independent resources, including multicentre data, different laboratories within the same centre or with different operators, are challenging in design, data collection and interpretation. Indeed, inconsistent results across the resources are possible. In this paper, we propose a statistical solution for the problem of multi-resource consensus inferences when statistical results from different resources show variation in magnitude, directionality, and significance. Our proposed method allows combining the corrected p-values, effect sizes and the total number of centres into a global consensus score. We apply this method to obtain a consensus score for data collected by the International Mouse Phenotyping Consortium (IMPC) across 11 centres. We show the application of this method to detect sexual dimorphism in haematological data and discuss the suitability of the methodology.
由多个独立资源（包括多中心数据、同一中心的不同实验室或不同操作人员）收集数据的实验，在设计、数据收集和解释方面都具有挑战性。事实上，不同资源的结果可能不一致。在本文中，我们提出了一种统计解决方案，用于解决来自不同资源的统计结果在幅度、方向性和显著性方面存在差异时的多资源共识推断问题。我们提出的方法可以将校正后的 p 值、效应大小和中心总数合并成一个全球共识得分。我们应用这种方法为国际小鼠表型联盟（IMPC）在 11 个中心收集的数据获得了共识得分。我们展示了这种方法在血液学数据中检测性双态性的应用，并讨论了该方法的适用性。
期刊介绍：
Mammalian Genome focuses on the experimental, theoretical and technical aspects of genetics, genomics, epigenetics and systems biology in mouse, human and other mammalian species, with an emphasis on the relationship between genotype and phenotype, elucidation of biological and disease pathways as well as experimental aspects of interventions, therapeutics, and precision medicine. The journal aims to publish high quality original papers that present novel findings in all areas of mammalian genetic research as well as review articles on areas of topical interest. The journal will also feature commentaries and editorials to inform readers of breakthrough discoveries as well as issues of research standards, policies and ethics.
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