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

EAPSI：开发统计方法，通过遗传学和因果推理中的负控制消除不需要的变异

• 批准号: 1713563
• 负责人: Kristen Hunter
• 金额: $ 0.04万
• 依托单位: Hunter Kristen B
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1713563&HistoricalAwards=false
• 关键词: EAPSI; Developing; Statistical; Methods; Removing

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.

不必要的变异是统计分析中的一个常见问题，它使研究人员难以区分信号和噪声。例如，在遗传学领域，研究人员可能试图检测癌症肿瘤和附近良性区域之间基因表达的差异。这两个区域之间的差异是感兴趣的变化-这是想要的变化。然而，实验室之间遗传技术的差异可能会在数据中产生不必要的变化，这可能导致研究人员从数据中得出错误的结论。不需要的变异可能会引起偏差，扭曲基因和疾病之间的真实关系，或者可能会降低精度，掩盖感兴趣的变异。理想情况下，研究人员希望消除不必要的变化。实现该目标的一种方法是通过阴性对照，其是与不需要的变化相关但与感兴趣的变化不相关的变量。 本项目的目标是开发使用阴性对照的统计技术，以有效地去除不需要的变异，同时保持感兴趣的变异。这些方法将在各个领域都很有用，但将重点关注遗传学和因果推理，包括实验设计。这项研究将与澳大利亚墨尔本沃尔特和伊丽莎霍尔医学研究所的阴性对照方面的领先专家特伦斯·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有关的方法。 本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。该项目将把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结合在一起。该奖项是东亚和太平洋夏季研究所计划的一部分，支持美国研究生的夏季研究，由NSF和澳大利亚科学院共同资助。