基准测试变异性 癌症转录组学数据上的自动编码器
Mostafa Eltager1, Tamim Abdelaal1,2, Mohammed Charrout1
1Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.
PloS one
|October 5, 2023
概括
超参数调整对于计算生物学中的变异自编码器 (VAE) 等深度生成模型至关重要. 我们的研究为VAE超参数选择提供了强有力的建议,确保了癌症研究数据集的概括性.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 深度生成模型,特别是变异自编码器 (VAE),在计算生物学中越来越多地被使用.
- 这些模型擅长捕获复杂的数据结构,用于诸如癌症亚型化等任务.
- 然而,由于许多超参数需要仔细调整,VAE存在培训挑战.
研究的目的:
- 调查各种超参数对下游癌症相关任务中VAE性能的影响.
- 提供数据驱动的建议,以在VAE中进行最佳的超参数选择.
- 评估VAE学习的潜空间所捕获的生物相关性.
主要方法:
- 在TCGA转录组学数据上训练了六种不同的VAE模型,评估了癌症亚型集群和生存分析的性能.
- 系统地改变了超参数,包括潜在空间维度,学习速率,优化器,初始化和激活函数.
- 在GTEx数据集上验证了超参数效应,以确保可概括性和隐性空间表示与生物数据特征之间的测量相关性.
主要成果:
- β-TCVAE和DIP-VAE的平均性能良好,但对超参数选择敏感.
- 在TCGA和GTEx数据集中对聚类的超参数效应之间观察到显著的相关性 (ρ = 0.7),证实了强度.
- 学习的潜伏因素通常与特定的生物特征 (如性别,年龄或突变特征) 没有独特的相关性.
结论:
- 在转录学数据分析中为VAE超参数选择制定了强有力的,可通用的建议.
- 突出了某些VAE模型对超参数设置的敏感性.
- 表示当前的VAE隐藏空间可能无法完全捕获可分离或唯一相关的生物信息.
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