一个框架,用于在多个omics中的块级缺失数据
Sergi Baena-Miret1, Ferran Reverter1, Esteban Vegas1
1Departament of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona Spain.
PloS one
|July 23, 2024
概括
整合多omics数据可以改善预测模型,但面临着缺失数据等挑战. 我们的新方法有效地处理高维度和块智能的缺失omics数据,在乳腺癌和暴露组研究中显示出强大的性能.
科学领域:
- 多omics数据集成多omics数据集成
- 生物信息学是一种生物信息学.
- 统计建模 统计建模
背景情况:
- 高通量技术产生了大量的omics数据,需要整合以改进预测模型和生物标志物发现.
- 管理多主题数据带来了诸多挑战,包括异质性,噪音,高维度,特别是区块上缺失的数据模式.
研究的目的:
- 为了应对高维度和块智能的缺失数据在多omics集成的挑战.
- 开发和评估一个规范化和基于约束的方法,以进行可靠的多学科分析.
主要方法:
- 在R包"bwm"中实施规范化和基于约束的方法.
- 对二进制分类和连续响应变量任务的应用.
- 对乳腺癌和暴露组多组数据集的验证,包括大量缺失数据的场景.
主要成果:
- 拟议的模型实现了强大的性能 (86-92%的准确性,68-79%的F1分类;0.72-0.76的相关性回归),即使在所有omics中缺少数据.
- 随着丢失数据百分比的增加,性能略有下降,但当块智能丢失影响多个omics时,它超过了单个omic丢失数据场景.
- 通过不同的观测配置文件,可以观察到跨不同omics的特征选择一致性,而区块wise缺失的数据可能会通过不同的观测配置文件来增强模型的稳定性.
结论:
- 开发的R包"bwm"有效地处理高维度和在多omics集成中缺失的数据.
- 该方法表现出稳健性和强大的预测性能,为复杂的生物系统的生物标志物发现和分析提供了宝贵的工具.
- 需要进一步研究推动在多原子缺失数据场景中提高性能的机制.
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