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 coul

SUBMITTER: Wang H 

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

REPOSITORIES: biostudies-literature