Dataset Information

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

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 .

SUBMITTER: Wang H

PROVIDER: S-EPMC10625708 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

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

Wang Hao H Lin Yu-Nan YN Yan Shen S Hong Jing-Peng JP Tan Jia-Rui JR Chen Yan-Qing YQ Cao Yong-Sheng YS Fang Wei W

Plant methods 20231104 1

<h4>Background</h4>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.<h4>Results</h4>To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretabili ...[more]

PMID: 37925413

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Dataset Information

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

Background

Results

Conclusion

Publications

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

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