在权重基因共同表达网络分析中,对不对称的西格形函数的参数化
Muhammed Erkan Karabekmez1, Merve Yarıcı1
1Istanbul Medeniyet University, Department of Bioengineering, Istanbul, Turkey.
Computational biology and chemistry
|December 10, 2023
概括
本研究介绍了一种系统方法,用于参数化非对称的sigmoid函数,改进软值在权重基因共同表达网络分析 (WGCNA). 这有助于识别生物相关的基因表达模块.
科学领域:
- 基因组学就是基因组学.
- 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:
- 在基因组层面检查基因表达提供了比个体基因分析更准确的见解.
- 权重基因共同表达网络分析 (WGCNA) 是一种用于集群转录组数据的常见方法,利用软值的功率函数进行软值.
- 标准WGCNA功率函数可能会过度强调基因表达中的小相关性.
研究的目的:
- 介绍一个系统的程序,以参数化不对称的Sigmoid函数在WGCNA软值.
- 为WGCNA提供更强大的替代标准功率函数.
- 为了方便在基因表达网络分析中应用不对称的sigmoid函数.
主要方法:
- 开发了一种对不对称的Sigmoid函数的系统参数化程序.
- 在WGCNA框架内应用了增强软值方法.
- 通过COVID-19,酵母和大肠杆菌的转录组数据集验证了方法.
主要成果:
- 拟议的参数化方法简化了在WGCNA中使用不对称的Sigmoid函数.
- 该方法已成功应用于包括COVID-19在内的多种生物数据集.
- 结果表明,产生了生物学上可信的基因共同表达模块.
结论:
- 不对称的Sigmoid函数的系统参数化为WGCNA软值提供了一个有价值的替代方案.
- 这种方法提高了基因共同表达网络的生物解释性.
- 这种方法在各种物种和疾病背景中有效,包括COVID-19研究.
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