ArticleGenome biology2023
Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 28 citations in OpenAlex.
- Machine learning-driven decoding of maternal immune signatures in repeated pregnancy loss.PLoS computational biology · 2026Article
- Interpretable deep generative ensemble learning for single-cell omics with Hydra.Molecular systems biology · 2026Article
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Secretary bird optimization algorithm incorporating independent thinking mechanism and sine-square step length for feature selection.Scientific reports · 2026Article
- Review
- Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics.World journal of gastrointestinal oncology · 2025Review
- Feature selection methods affect the performance of scRNA-seq data integration and querying.Nature methods · 2025Article
- Research Trends and Dynamics in Single-cell RNA Sequencing for Musculoskeletal Diseases: A Scientometric and Visualization Study.International journal of medical sciences · 2025Review
- Integrating feature importance techniques and causal inference to enhance early detection of heart disease.PloS one · 2025Article
- Enhanced RNA-targeting CRISPR-Cas technology in zebrafish.bioRxiv : the preprint server for biology · 2024Article
- A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.Functional & integrative genomics · 2024Review
- Interpretable deep learning in single-cell omics.Bioinformatics (Oxford, England) · 2024Review
- Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis.Genome biology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFeature selection is an essential task in single-cell RNA-seq (scRNA-seq) data analysis and can be critical for gene dimension reduction and downstream analyses, such as gene marker identification and cell type classification. Most popular methods for feature selection from scRNA-seq data are based on the concept of differential distribution wherein a statistical model is used to detect changes in gene expression among cell types. Recent development of deep learning-based feature selection methods provides an alternative approach compared to traditional differential distribution-based methods in that the importance of a gene is determined by neural networks.
resultsIn this work, we explore the utility of various deep learning-based feature selection methods for scRNA-seq data analysis. We sample from Tabula Muris and Tabula Sapiens atlases to create scRNA-seq datasets with a range of data properties and evaluate the performance of traditional and deep learning-based feature selection methods for cell type classification, feature selection reproducibility and diversity, and computational time.
conclusionsOur study provides a reference for future development and application of deep learning-based feature selection methods for single-cell omics data analyses.
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