Department of Chemistry, Tsinghua University, Beijing 100084, China.
Division of Chemical Metrology and Analytical Science, National Institute of Metrology China, Beijing 100029, China.
Bioinformatics. 2024 Sep 2;40(9). doi: 10.1093/bioinformatics/btae545.
To address the challenges in single-cell metabolomics (SCM) research, we have developed an open-source Python-based modular library, named SCMeTA, for SCM data processing. We designed standardized pipeline and inter-container communication format and have developed modular components to adapt to the diverse needs of SCM studies. The validation was carried out on multiple SCM experiment data. The results demonstrated significant improvements in batch effects, accuracy of results, metabolic extraction rate, cell matching rate, as well as processing speed. This library is of great significance in advancing the practical application of SCM analysis and makes a foundation for wide-scale adoption in biological studies.
SCMeTA is freely available on https://github.com/SCMeTA/SCMeTA and https://doi.org/10.5281/zenodo.13569643.
为了解决单细胞代谢组学（SCM）研究中的挑战，我们开发了一个基于 Python 的开源模块化库，名为 SCMeTA，用于 SCM 数据处理。我们设计了标准化的流水线和容器间通信格式，并开发了模块化组件，以适应 SCM 研究的各种需求。在多个 SCM 实验数据上进行了验证。结果表明，在批处理效应、结果准确性、代谢物提取率、细胞匹配率以及处理速度方面都有显著提高。该库在推进 SCM 分析的实际应用方面具有重要意义，为生物研究中的广泛采用奠定了基础。
SCMeTA 可在 https://github.com/SCMeTA/SCMeTA 和 https://doi.org/10.5281/zenodo.13569643 上免费获取。
