通过转移学习,STEM使单细胞和空间转录组学数据的映射成为可能
Minsheng Hao1, Erpai Luo1, Yixin Chen1
1MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
Communications biology
|January 6, 2024
概括
我们开发了STEM,这是一种深度学习方法,用于整合空间转录组学 (ST) 和单细胞RNA测序 (SC) 数据. STEM将SC数据映射到ST,以单细胞分辨率显示细胞景观.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 空间转录组学 (ST) 缺乏单细胞分辨率,而单细胞RNA测序 (SC) 缺乏空间信息.
- 整合ST和SC数据对于了解细粒度层面的组织生理学和病理学至关重要.
研究的目的:
- 开发一种用于整合ST和SC数据的计算方法.
- 为了使单细胞分辨率空间转录组景观的创建.
- 发现组织内细胞的空间组织和异质性.
主要方法:
- 开发了STEM (空间意识的EMBedding),一种深度转移学习方法.
- 将ST和SC数据编码成一个统一的,空间意识的嵌入空间.
- 推断SC-ST映射和预测SC数据的伪空间邻近性.
主要成果:
- STEM有效地整合了ST和SC数据,保留了空间信息.
- 该方法消除了这两种数据类型之间的技术偏差.
- 应用于人类癌症和肝脏数据集,STEM识别了罕见细胞局部和空间基因表达变异.
结论:
- 通过整合ST和SC数据,STEM可以构建单细胞级空间转录组图.
- 这种方法提供了对组织微环境和空间异质性的机制性见解.
- STEM是推动空间生物学研究的强大工具.
相关概念视频
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...