LIDER:基于细胞嵌入的深度神经网络分类器,用于监督的细胞类型识别
Yachen Tang1, Xuefeng Li1, Mingguang Shi1
1Hefei University of Technology, Hefei, China.
PeerJ
|August 21, 2023
概括
深度监督学习方法LIDER使用细胞嵌入和神经网络准确地识别单细胞RNA测序数据中的细胞类型. 这种方法提供了强大的性能,与现有方法相比或优于现有方法.
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:
- 单细胞RNA测序 (scRNA-seq) 生成大型数据集,需要有效的细胞类型识别.
- 目前的方法通常依赖于无监督的集群,然后是手动注释,这可能是耗时和主观的.
研究的目的:
- 开发一种深度监督学习方法,用于在scRNA-seq数据中自动识别细胞类型.
- 引入LIDER (基于细胞嵌入的深度神经网络分类器) 作为这项任务的新方法.
主要方法:
- LIDER使用一个堆叠的否定自动编码器来学习细胞嵌入.
- 一个深度神经网络分类器被用来预测基于这些嵌入的细胞类型.
- 一个定制的重建损失函数被整合到自动编码器中.
主要成果:
- 在八个不同的scRNA-seq数据集中,LIDER证明了精确的细胞类型识别.
- 性能与最先进的方法可比或优于.
- 该方法显示对批量效应的稳定性,这是scRNA-seq分析中的一个常见挑战.
结论:
- 在LIDER中实施的深度监督学习显示了在scRNA-seq数据中自动化细胞类型识别的巨大潜力.
- 利德为传统方法提供了准确,坚固和高效的替代方案.
- LIDER代码的可用性有助于其采用和进一步开发.
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