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对依赖区域的贝叶斯函数数据分析及其用于识别差异甲基化区域的应用

Suvo Chatterjee¹, Shrabanti Chowdhury², Duchwan Ryu³

¹Department of Epidemiology and Biostatistics, Indiana University, School of Public Health, Bloomington, Indiana, USA.

|July 21, 2023

概括

本研究引入了贝叶斯方法来分析长顺序数据,使用光滑线和过渡模型来处理数据窗口之间的依赖关系. 该方法有效地识别了群体差异,如肺癌数据分析所示.

科学领域:

统计统计统计统计
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

在生物数据中分析极长的序列存在挑战,因为相邻部分之间的潜在依赖关系.
现有的方法可能难以捕捉这些细分领域内和这些细分领域内的功能模式的细微差别.

研究的目的:

开发一个贝叶斯函数数据分析框架,用于长时间的顺序观测.
在一个序列中解决非独立窗口之间的依赖结构.
在特定的窗口中识别个人群之间的功能差异.

主要方法:

利用贝叶斯平滑线来估计每个窗口内的个别功能模式.
开发了参数的过渡模型,以捕捉相邻窗口之间的依赖结构.
使用从马尔科夫链蒙特卡洛 (MCMC) 样本获得的贝叶斯因子进行组比较.

主要成果:

拟议的方法在模拟研究中证明了其有效性.
成功应用于识别TCGA肺腺癌数据中的差异甲基化遗传区域.
使用统计证据,量化每个窗口的组之间的功能差异.

结论:

建议的贝叶斯函数数据分析方法适用于具有窗口间依赖性的长顺序数据.
该方法为识别特定组的功能差异提供了一个强大的框架,特别是在基因组应用中.
这种方法增强了复杂的生物序列的分析,例如癌症基因组学中发现的.

关键词:

贝叶斯平滑线条是贝叶斯的平滑线条.在TCGA肺部腺癌瘤.不同的甲基化区域.动态加权颗粒过器动态加权颗粒过器功能性数据分析数据分析.后面的贝叶斯因子

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