ArticleBriefings in bioinformatics2023
A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.Biochemistry and biophysics reports · 2026Article
- High-Dimensional Feature Selection Using Improved Hybrid Breeding Optimization Algorithm with Feature Grouping.Biomimetics (Basel, Switzerland) · 2026Article
- Rewiring tumor immunity via zinc finger proteins: a new frontier in cancer immunotherapy.Journal of translational medicine · 2025Review
- SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis.Genome biology · 2025Article
- LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data.Genomics, proteomics & bioinformatics · 2025Article
- A Hybrid Ensemble Equilibrium Optimizer Gene Selection Algorithm for Microarray Data.Biomimetics (Basel, Switzerland) · 2025Article
- STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration.Nature communications · 2025Article
- A semi-supervised weighted SPCA- and convolution KAN-based model for drug response prediction.Frontiers in genetics · 2025Article
- Hypergraph-based analysis of weighted gene co-expression hypernetwork.Frontiers in genetics · 2025Article
- stGuide advances label transfer in spatial transcriptomics through attention-based supervised graph representation learning.Frontiers in genetics · 2025Article
- Reference-free deconvolution of complex samples based on cross-cell-type differential analysis: Systematic evaluations with various feature selection options.Frontiers in genetics · 2025Article
- MOLUNGN: a multi-omics graph neural network for biomarker discovery and accurate lung cancer classification.Frontiers in genetics · 2025Article
- WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.Frontiers in genetics · 2025Article
- Uncovering biomarkers and molecular pathways linking NAFLD and AIS: Insights from bioinformatic analysis and experiment.PloS one · 2025Article
- KSRV: a Kernel PCA-Based framework for inferring spatial RNA velocity at single-cell resolution.Frontiers in genetics · 2025Article
- GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.Frontiers in genetics · 2025Article
- Detecting anomalous anatomic regions in spatial transcriptomics with STANDS.Nature communications · 2024Article
- CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2024Article
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7 authors.
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Abstract
Feature gene selection has significant impact on the performance of cell clustering in single-cell RNA sequencing (scRNA-seq) analysis. A well-rounded feature selection (FS) method should consider relevance, redundancy and complementarity of the features. Yet most existing FS methods focus on gene relevance to the cell types but neglect redundancy and complementarity, which undermines the cell clustering performance. We develop a novel computational method GeneClust to select feature genes for scRNA-seq cell clustering. GeneClust groups genes based on their expression profiles, then selects genes with the aim of maximizing relevance, minimizing redundancy and preserving complementarity. It can work as a plug-in tool for FS with any existing cell clustering method. Extensive benchmark results demonstrate that GeneClust significantly improve the clustering performance. Moreover, GeneClust can group cofunctional genes in biological process and pathway into clusters, thus providing a means of investigating gene interactions and identifying potential genes relevant to biological characteristics of the dataset. GeneClust is freely available at https://github.com/ToryDeng/scGeneClust.
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