scACT:通过从未配对的单细胞数据中循环一致的训练来实现准确的交叉模式翻译
Siwei Xu1, Junhao Liu1, Jing Zhang1
1University of California, Irvine, Irvine, California, USA.
Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management
|December 4, 2024
概括
我们开发了scACT,这是一种用于单细胞测序数据的深度学习模型. 它使用未配对的数据在分子类型之间进行翻译,提供新的生物学见解,而不需要匹配的样本.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 一个单细胞的奥米克.
背景情况:
- 单细胞测序可实现多模式分析,这对于理解细胞调节至关重要.
- 跨模式转换方法受到稀缺,高质量的共同试验数据的需求的限制.
研究的目的:
- 引入scACT,这是一个深度生成模型,用于从未配对的单细胞数据中提取跨模式的生物学见解.
- 解决调整未配对数据的挑战,使没有先前知识的翻译成为可能,并探索监管网络.
主要方法:
- scACT利用对抗性训练进行未配对的多式联运数据对齐.
- 循环一致的培训有助于跨模式的翻译,而无需事先的知识.
- 在体中发生的干扰使得监管相互连接的可解释性探索成为可能.
主要成果:
- 在所有三个核心任务中,scACT在各种单细胞数据集中展示了卓越的性能.
- 该模型成功地对准了未配对的数据,并有效地执行了跨模式的翻译.
- 可解释的监管洞察力是通过in-silico扰动分析获得的.
结论:
- scACT通过从未配对的数据中实现跨模式翻译来推进单细胞奥米克分析.
- 开发的开源软件包促进了更广泛的研究社区采用.
- 这种方法克服了数据的局限性,为更深入的生物发现铺平了道路.
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