PROLONG:对结果指导的纵向奥米克分析进行处罚回归,使用网络和组约束
Steven Broll1, Sumanta Basu1, Myung Hee Lee2
1Department of Statistics and Data Science, Cornell University, Ithaca, NY 14850, United States.
Bioinformatics (Oxford, England)
|March 7, 2025
概括
我们开发了PROLONG,一种用于分析纵向奥米克数据以寻找生物标志物的新方法. 它识别了随着疾病进展而随时间变化的代谢物,改进了结核病的传统模型.
科学领域:
- 生物统计学 生物统计学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 与临床结果相结合的纵向omics数据对生物标志物发现越来越感兴趣.
- 传统的方法,如线性混合效应模型,在有限的纵向数据下往往缺乏统计能力.
- 识别随着时间的推移与临床结果共同变化的omics变量对于了解疾病进展至关重要.
研究的目的:
- 开发一种新的统计方法来识别纵向的奥米克生物标志物.
- 为了提高从稀疏的纵向奥米克数据集中选择生物标志物的功率和准确性.
- 为了确定与结核病 (TB) 菌株负载共变的尿代谢物,以获得疾病进展的见解.
主要方法:
- 提出了惩罚性回归方法,使用纵向omics和临床数据的第一个差异.
- 将拉索+拉普拉斯方法扩展到一个纵向组拉索+拉普拉斯方法,命名为PROLONG.
- 对于可变的依赖结构,PROLONG使用拉普拉斯惩罚,而对于稀疏性和术语分组,则使用群拉索.
主要成果:
- 在自动化超参数选择的各种场景中,PROLONG在选择目标代谢物中表现出高的特异性和灵敏性.
- 该方法成功地从现实世界结核病数据集中确定了一组相关的尿路代谢物.
- 选择的代谢物包括之前通过探索性数据分析 (EDA) 识别的代谢物.
结论:
- PROLONG提供了一种强大而有效的方法来分析纵向omics数据以识别疾病生物标志物.
- 该方法通过利用数据结构和处罚技术来增强生物标记物的发现.
- 开发的R包"prolong"有助于在研究环境中应用这种方法.
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