1. NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning
- Author
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Hao Wang, Yu-Nan Lin, Shen Yan, Jing-Peng Hong, Jia-Rui Tan, Yan-Qing Chen, Yong-Sheng Cao, and Wei Fang
- Subjects
Machine learning ,Marker genes ,scRNA-seq ,Rice root tips ,Cell subpopulations ,Plant culture ,SB1-1110 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Single-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. Results To 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. Conclusion Taken 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 .
- Published
- 2023
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