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

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. To a...

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Bibliographic Details
Published in:Plant methods 2023-11, Vol.19 (1), p.1-119, Article 119
Main Authors: Wang, Hao, Lin, Yu-Nan, Yan, Shen, Hong, Jing-Peng, Tan, Jia-Rui, Chen, Yan-Qing, Cao, Yong-Sheng, Fang, Wei
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Biomarkers
Biosynthesis
Cell subpopulations
Datasets
Ensemble learning
Epidermis
Feature selection
Flooding
Gene expression
Gene sequencing
Genes
Genetic aspects
Growth
Heterogeneity
Machine learning
Marker genes
Methods
Molecular modelling
Physiological aspects
Ribonucleic acid
Rice
Rice root tips
RNA
RNA sequencing
scRNA-seq
Stresses
Subpopulations
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Summary: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. 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. 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.
ISSN:1746-4811
1746-4811
DOI:10.1186/s13007-023-01092-0