AtML: An Arabidopsis thaliana root cell identity recognition tool for medicinal ingredient accumulation

文献类型： 外文期刊

第一作者： Yu, Shicong

作者： Yu, Shicong;Zheng, Shuqin;Ning, Jing;Luo, Ruxian;Deng, Xiaoshu;Liu, Lijia;Wang, Hao;Yan, Shen;Fu, Xiangzheng;Deng, Xiaoshu

作者机构：

关键词： Machine learning; Marker genes; scRNA-seq; Arabidopsis root tips; Cell subpopulations; Medicinal compound

期刊名称：METHODS （ 影响因子：4.3； 五年影响因子：4.2 ）

ISSN： 1046-2023

年卷期： 2024 年 231 卷

页码：

收录情况： SCI

摘要： Arabidopsis thaliana synthesizes various medicinal compounds, and serves as a model plant for medicinal plant research. Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots, facilitating the analysis of synthesis and accumulation patterns of medicinal compounds in different cell subpopulations. Although methods for interpreting single-cell transcriptomics data are rapidly advancing in Arabidopsis, challenges remain in precisely annotating cell identity due to the lack of marker genes for certain cell types. In this work, we trained a machine learning system, AtML, using sequencing datasets from six cell subpopulations, comprising a total of 6000 cells, to predict Arabidopsis root cell stages and identify biomarkers through complete model interpretability. Performance testing using an external dataset revealed that AtML achieved 96.50% accuracy and 96.51% recall. Through the interpretability provided by AtML, our model identified 160 important marker genes, contributing to the understanding of cell type annotations. In conclusion, we trained AtML to efficiently identify Arabidopsis root cell stages, providing a new tool for elucidating the mechanisms of medicinal compound accumulation in Arabidopsis roots.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序