CellMate─一个深度学习辅助的单细胞数据处理平台
Felix Friedrich1, Cátia Marques1, Ingela Lanekoff1,2
1Department of Chemistry for Life Sciences, Uppsala University, Uppsala 75 123, Sweden.
Analytical chemistry
|February 13, 2026
概括
单细胞代谢 (SCM) 揭示了细胞差异. 一个新的MATLAB工具CellMate简化了SCM数据分析,可以更深入地了解代谢物异质性.
科学领域:
- 生物化学 生物化学
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 单细胞代谢学 (SCM) 对于理解细胞异质性至关重要.
- 现有的SCM数据分析工具是有限的,通常与传统方法不兼容.
- 高分辨率质谱仪能够在单细胞中灵敏地检测代谢物.
研究的目的:
- 介绍CellMate,这是一个基于MATLAB的平台,用于处理单细胞代谢数据.
- 提供一个用户友好的界面,用于代谢物识别和峰值对齐.
- 支持定量,有针对性和无针对性的代谢工作流.
主要方法:
- 开发CellMate,一个使用直接输液技术的MATLAB平台.
- 实现一个图形用户界面,用于直观的数据处理.
- 集成一个深度学习算法来区分非目标工作流中的代谢物.
主要成果:
- 细胞Mate 便于代谢物识别和SCM数据的峰值对齐.
- 该平台支持可定制的定量,有针对性和非有针对性的代谢分析.
- 深度学习模型有效地将内源代谢物与背景噪声区分开来.
结论:
- 细胞Mate增强了单细胞代谢数据的分析,克服了当前的局限性.
- 该工具可以从单个细胞中全面提取代谢物信息.
- 细胞Mate推进了单细胞代谢学研究工具箱的功能.
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