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Updated: Oct 2, 2025

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods.

Haidong Yan¹, Jiyoung Lee^1,2, Qi Song^1,2

¹School of Plant and Environmental Sciences (SPES), Virginia Tech, Blacksburg, VA, 24060, USA.

The New Phytologist

|February 25, 2022

Summary

This summary is machine-generated.

We developed a new machine learning pipeline, single-cell predictive marker (SPmarker), to discover novel cell-type marker genes in plant single-cell RNA sequencing data. This method identifies hundreds of new markers, enhancing cross-species cell type mapping.

Keywords:

cell marker genes gene expression machine learning root development single-cell genomics single-cell sequencing

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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

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Published on: January 21, 2020

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Published on: May 1, 2021

Area of Science:

Plant biology
Genomics
Bioinformatics

Background:

Accurate cell type classification is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data.
Identifying reliable marker genes is essential for cell type annotation in scRNA-seq studies.
Current methods for marker gene discovery may not capture the full spectrum of cell-type specific genes.

Purpose of the Study:

To develop and validate a novel machine learning pipeline, single-cell predictive marker (SPmarker), for identifying cell-type marker genes in Arabidopsis root scRNA-seq data.
To leverage interpretable machine learning models for robust marker gene selection.
To discover new marker genes and enable cross-species scRNA-seq data mapping in plants.

Main Methods:

Development of the SPmarker machine learning pipeline utilizing interpretable models.
Application of SPmarker to Arabidopsis root scRNA-seq datasets.
Validation of SPmarker by assigning cell types based on published labels, trajectory analysis, and GFP markers.
Comparative analysis of newly identified marker genes with known markers and their orthologs in rice.

Main Results:

SPmarker successfully assigned cell types across different datasets and experimental conditions.
Hundreds of novel cell-type marker genes were identified in Arabidopsis root.
New marker genes showed higher ortholog identification in rice scRNA-seq clusters compared to known markers.
172 new root hair marker genes with cross-species orthologs were discovered, expanding root hair marker gene identification by 35-154%.

Conclusions:

SPmarker provides a powerful and interpretable approach for discovering novel cell-type marker genes from plant scRNA-seq data.
The identified novel marker genes facilitate more accurate cell type annotation and enhance cross-species comparative analyses.
This work advances the field of plant single-cell genomics by providing new tools and insights for cell type identification and mapping.