基于内核的层次结构组件模型用于对生存表型的途径分析
Suhyun Hwangbo1,2, Sungyoung Lee2, Md Mozaffar Hosain3
1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-747, Korea.
Genes & genomics
|September 26, 2024
概括
HisCoM-KernelS使用RNA测序数据识别生存途径,考虑复杂的基因效应和途径相关性. 这种方法改进了胰腺癌存活率分析的传统方法.
科学领域:
- 基因组学和生物信息学
- 癌症研究 癌症研究
- 计算生物学 计算生物学
背景情况:
- 高通量测序,包括RNA测序 (RNA-seq),已经彻底改变了基因表达分析.
- 传统的途径分析方法往往忽视了途径之间的相关性和重叠的生物标志物.
- 现有的方法通常假定对表型产生线性基因效应,从而限制了它们的范围.
研究的目的:
- 开发HisCoM-KernelS模型,用于识别与现型相关的生存途径.
- 为了适应基因和生存结果之间的复杂,非线性关系.
- 在基于途径的生存分析中考虑途径间的相关性.
主要方法:
- 将HisCoM-KernelS模型应用于TCGA胰腺管道腺癌 (PDAC) RNA-seq数据集.
- 利用内核机器回归来模拟通路对生存的影响,并结合了基因通路结构.
- 通过交替最小正方形估计模型参数,并通过排列测试评估路径显著性.
主要成果:
- HisCoM-KernelS确定了与胰腺癌存活率相关的重要途径.
- 与HisCoM-PAGE,全球测试,GSEA和CoxKM相比,该模型显示了检测率和显著途径的优越平衡.
- 在HisCoM-KernelS中Gaussian内核集成提高了性能.
结论:
- HisCoM-KernelS通过捕捉非线性基因效应和相互路径相关性,有效地将路径分析扩展到生存结果.
- 该模型对TCGA PDAC数据的应用凸显了其在识别生物相关途径方面的实用性.
- HisCoM-KernelS提供了一种强大的工具,用于使用高通量测序数据进行生存表型研究.
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