ArticleThe New phytologist2022
Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods.
Article in The New phytologist, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 33 citations in OpenAlex.
- scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation.Journal of advanced research · 2026Article
- Integrating single-cell omic techniques to resolve the spatio-temporal complexity of arbuscular mycorrhizal symbiosis.Journal of experimental botany · 2026Review
- Heads Up: Transcriptomics Reveal Functional Roles of Cannabis Glandular Trichome Stalks.Plants (Basel, Switzerland) · 2026Article
- A mixture of attention experts-embedded flow-based generative model to create synthetic cells in single-cell RNA-Seq datasets.PLoS computational biology · 2025Article
- Arabidopsis as a model for translational research.The Plant cell · 2025Review
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- Can a plant biologist fix a thermostat?The New phytologist · 2025Review
- Orthologous marker groups reveal broad cell identity conservation across plant single-cell transcriptomes.Nature communications · 2025Article
- CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes.Scientific reports · 2024Article
- Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.International journal of molecular sciences · 2024Article
- cellMarkerPipe: Cell Marker Identification and Evaluation Pipeline in Single Cell Transcriptomes.Research square · 2024Article
- Reference genes for quantitative Arabidopsis single molecule RNA fluorescence in situ hybridization.Journal of experimental botany · 2023Article
- Gene Regulatory Network Modeling Using Single-Cell Multi-Omics in Plants.Methods in molecular biology (Clifton, N.J.) · 2023Article
- A Novel Algorithm for Feature Selection Using Penalized Regression with Applications to Single-Cell RNA Sequencing Data.Biology · 2022Article
- Single-Cell RNA Sequencing for Plant Research: Insights and Possible Benefits.International journal of molecular sciences · 2022Review
- Single-cell genomics revolutionizes plant development studies across scales.Development (Cambridge, England) · 2022Article
- Single-cell genomics in plants: current state, future directions, and hurdles to overcome.Plant physiology · 2022Article
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Authors and funding
7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
An essential step in the analysis of single-cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single-cell predictive marker (SPmarker) to identify novel cell-type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single-cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non-Arabidopsis species, which expands the number of marker genes for this cell type by 35-154%. Our results represent a new approach to identifying cell-type marker genes from scRNA-seq data and pave the way for cross-species mapping of scRNA-seq data in plants.
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