NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning

文献类型： 外文期刊

第一作者： Wang, Hao

作者： Wang, Hao;Lin, Yu-Nan;Yan, Shen;Hong, Jing-Peng;Tan, Jia-Rui;Chen, Yan-Qing;Cao, Yong-Sheng;Fang, Wei

作者机构：

关键词： Machine learning; Marker genes; scRNA-seq; Rice root tips; Cell subpopulations

期刊名称：PLANT METHODS （ 影响因子：5.1； 五年影响因子：6.1 ）

ISSN：

年卷期： 2023 年 19 卷 1 期

页码：

收录情况： SCI

摘要： BackgroundSingle-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying the cellular heterogeneity of rice roots. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity.ResultsTo address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using a test dataset, with 98.01% accuracy and 95.45% recall. With the power of interpretability provided by NRTPredictor, our model recognizes 110 marker genes partially involved in phenylpropanoid biosynthesis. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrated analysis of scRNA and bulk RNA-seq data revealed aberrant expression of Epidermis cell subpopulations in flooding, Pi, and salt stresses.ConclusionTaken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi, and salt stresses. Based on the proposed model, a free webserver has been established, which is available at https://www.cgris.net/nrtp.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序