单细胞RNA-seq数据的表示学习
Constantin Ahlmann-Eltze1, Florian Barkmann2, Jan Lause3
1University College London.
概括
单细胞RNA测序 (scRNA-seq) 数据的表示学习方法解决了诸如高维度和噪声等挑战. 本综述对关键方法进行了分类,有助于未来对单细胞转录组学分析的研究.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 数据科学数据科学数据科学
背景情况:
- 单细胞RNA测序 (scRNA-seq) 产生高维度,稀疏和杂的基因表达数据.
- 超过1亿个单细胞转录组是公开的,需要先进的分析方法.
- 代表性学习旨在创建scRNA-seq数据的有效的低维表示.
研究的目的:
- 对scRNA-seq数据的主要表示学习范式进行审查和分类.
- 阐述这些方法的概念基础,假设和区别.
- 确定当前的挑战和该领域的未来研究方向.
主要方法:
- 这些是因子模型.
- 自动编码器 自动编码器
- 相反的学习方法对比的学习方法.
- 基于变压器的基础模型
主要成果:
- 这些方法学习了对下游分析 (如集群和可视化) 的低维表示.
- 新兴的方法可以从跨实验的聚合scRNA-seq数据中学习潜在的表示.
- 提供了一个分类学,澄清了scRNA-seq.q.的表示学习格局.
结论:
- 代表性学习对于从复杂的scRNA-seq数据集中提取有意义的生物学见解至关重要.
- 了解不同的范式是选择特定研究问题的适当方法的关键.
- 未来的工作应该集中在解决现有的基准和开放挑战,以推进该领域.
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