隐藏未知集群集成多omics数据 (LUCID) 的扩展,包含不完整的omics数据
Yinqi Zhao1, Qiran Jia1, Jesse Goodrich1
1Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States.
Bioinformatics advances
|September 3, 2024
概括
隐藏未知集群集成多omics数据 (LUCIDus) 是一个新的统计模型. 它处理了多omics分析中缺失的数据,并揭示了暴露如何影响健康结果,比如儿童体重指数.
科学领域:
- 多主题数据分析数据分析.
- 统计建模 统计建模
- 环境健康研究环境健康研究
背景情况:
- 多主题数据分析带来了挑战,特别是列表中的缺失.
- 了解环境暴露如何通过生物途径影响健康结果至关重要.
研究的目的:
- 引入一种新的统计模型,即隐藏未知集群集成多omics数据 (LUCIDus),用于多omics数据分析.
- 扩展模型以有效处理列表式和零星缺失数据.
- 通过使用集成的多学科数据,阐明暴露影响结果的途径.
主要方法:
- 开发了一种新的统计模型,通过潜伏集群集成omics数据,暴露和结果.
- 将模型扩展到一个集成的归算框架,以解决列表中缺失的问题.
- 将模型应用于ISGlobal/ATHLETE"暴露细胞数据挑战活动"中1301名儿童的蛋白质组学数据.
主要成果:
- 模拟研究证实,综合归算方法的结果是一致的和不太偏的估计.
- 该模型成功地确定了与更高和更低的儿童体重指数相关的两个群.
- 描述了潜在的生物学特征,将母亲的六二暴露与儿童的体重指数联系起来.
结论:
- LUCIDus模型为多omics数据分析提供了一个强大的框架,有效地处理缺失的数据.
- 该模型可以识别不同的生物特征,并阐明暴露-结果关联.
- LUCIDus R 软件包可供公众使用,以促进多组和暴露组科学领域的进一步研究.
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