BEENE:基于深度学习的非线性嵌入改进了批量效应估计
Md Ashiqur Rahman1,2, Abdullah Aman Tutul1,3, Mahfuza Sharmin4
1Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh.
Bioinformatics (Oxford, England)
|August 10, 2023
概括
使用非线性嵌入 (BEENE) 的批量效应估计解决了单细胞RNA测序数据的挑战,提供了一种强大的方法来检测和量化批量效应. 这种深度学习方法改善了数据集成和解释,超出了PCA等传统方法.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 分析大规模单细胞RNA测序 (scRNA-seq) 数据集受到批量效应的阻碍,即来自不同实验条件或技术的系统变异.
- 准确检测和纠正批量效应对于整合数据集和从scRNA-seq数据中得出可靠的生物学结论至关重要.
- 现有的方法,如主要组件分析 (PCA),经常与复杂的,非线性批量效应作斗争,限制了它们的有效性.
研究的目的:
- 开发一种基于深度学习的新方法,用于在scRNA-seq数据中进行可靠的批量效应估计.
- 创建一个低维嵌入,有效地捕捉和区分生物变异和技术批量效应.
- 提供一种比线性方法更灵敏,更精确的工具,用于检测和量化批量效应,适应线性和非线性模式.
主要方法:
- 使用非线性嵌入 (BEENE) 实现批量效应估计,这是一个深度非线性自动编码器网络.
- 在scRNA-seq数据中同时学习批量和生物变量.
- 为批量效应检测和量化而优化的替代低维嵌入的生成.
主要成果:
- 与PCA相比,BEENE产生的嵌入式对于检测和量化批量效应来说更强大,更敏感.
- 该方法在模拟数据集和各种生物数据集上成功验证,包括小鼠胚胎细胞,外周血液单核细胞和胰腺小岛细胞.
- BEENE有效地处理线性和非线性批量效应,提供改进的数据集成功能.
结论:
- 在scRNA-seq数据分析中,BEENE提供了一种强大的新方法来解决批量效应.
- 深度非线性嵌入策略提高了数据集成和生物解释的准确性和可靠性.
- BEENE代表了单细胞基因组学计算工具的重大进步.
相关概念视频
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
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When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
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When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...