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NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning.
- Source :
- Plant Methods; 11/4/2023, Vol. 19 Issue 1, p1-12, 12p
- Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- RNA sequencing
GENE expression
BIOSYNTHESIS
PHENYLPROPANOIDS
Subjects
Details
- Language :
- English
- ISSN :
- 17464811
- Volume :
- 19
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Plant Methods
- Publication Type :
- Academic Journal
- Accession number :
- 173432038
- Full Text :
- https://doi.org/10.1186/s13007-023-01092-0