EAPSI: Developing Statistical Methods for Removing Unwanted Variation with Negative Controls in Genetics and Causal Inference

不必要的变化是统计分析中常见的问题，这使得研究人员很难区分信号和噪声。例如，在遗传学领域，研究人员可能会试图检测癌症肿瘤和附近良性区域之间的基因表达差异。这两个地区的不同之处在于利益的差异--这是想要的差异。然而，不同实验室之间的基因技术差异可能会在数据中产生不必要的差异，这可能会导致研究人员从数据中得出错误的结论。不必要的变异可能会导致偏差，扭曲基因和疾病之间的真实关系，或者可能会降低精确度，掩盖感兴趣的变异。理想情况下，研究人员希望移除不想要的变异。实现这一目标的一种方法是通过负控制，这是一个与不想要的变化相关的变量，但与兴趣的变化无关。该项目的目标是开发使用阴性对照的统计技术，以便在保持感兴趣的变异的同时有效地去除不需要的变异。这些方法将在广泛的领域有用，但将重点放在遗传学和因果推理上，包括实验设计。这项研究将与澳大利亚墨尔本沃尔特和伊莱扎·霍尔医学研究所的首席阴性对照专家特伦斯·斯皮德教授合作进行。流行病学、因果推理和遗传学领域都有与消除不必要的变异相关的方法。该项目将综合这些领域的方法，为阴性对照建立新的统计方法。这些方法包括假设检验、倾向得分匹配和参数模型，并依赖于不同的框架和假设。这个项目将结合因果推理技术的灵活性、遗传学方法的统计效率和流行病学的严格假设。这个奖项由东亚和太平洋夏季学院计划资助一名美国研究生的夏季研究，由NSF和澳大利亚科学院联合资助。

Unwanted variation is a common problem in statistical analysis that makes it difficult for researchers to distinguish between signal and noise. For example, in the field of genetics, a researcher might to try to detect the difference in gene expression between a cancer tumor and a nearby benign region. The difference between the two regions is the variation of interest--this is wanted variation. However, differences in genetic techniques between laboratories can create unwanted variation in the data, which can lead to the researcher reaching incorrect conclusions from the data. The unwanted variation can either induce bias, distorting the true relationship between the genes and diseases, or can decrease precision, masking the variation of interest. Ideally, the researcher would like to remove the unwanted variation. One way to achieve this goal is through a negative control, which is a variable that is associated with the unwanted variation, but is not associated with the variation of interest. The goal of this project is to develop statistical techniques for using negative controls to effectively remove unwanted variation while keeping the variation of interest. These methods will be useful in a wide variety of fields, but will focus on genetics and causal inference, including experimental design. This research will be conducted in collaboration with Professor Terence Speed, a leading expert on negative controls in this context, at the Walter and Eliza Hall Medical Institute in Melbourne, Australia.The fields of epidemiology, causal inference, and genetics all have methods related to removing unwanted variation. This project will synthesize approaches from these fields to build new statistical methods for negative controls. These methods include hypothesis testing, propensity score matching, and parametric models, and rely on different frameworks and assumptions. This project will bring together the flexibility of causal inference techniques, the statistical efficiency of genetics methods, and the rigorous hypotheses of epidemiology.This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student and is jointly funded by NSF and the Australian Academy of Science.