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Integrating feature selection with unsupervised deep embedding for clustering single-cell RNA-seq data
Cheng Zhong1, Siqi Jiang1, Zhi Wei1
1Department of Computer Science, New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, United States.
Briefings in Bioinformatics
|March 2, 2026
Summary
This study introduces FSSC, a novel framework for joint feature selection and clustering in single-cell RNA sequencing (scRNA-seq) analysis. FSSC improves cell population identification by simultaneously selecting informative genes and clustering data.
Area of Science:
- Genomics
- Computational Biology
- Bioinformatics
Background:
- Single-cell RNA sequencing (scRNA-seq) provides high-resolution gene expression data.
- Clustering is essential for identifying distinct cell populations in scRNA-seq data.
- Current methods often perform gene selection separately, potentially missing crucial clustering information.
Purpose of the Study:
- To develop a unified framework for joint feature selection and clustering in scRNA-seq analysis.
- To address limitations of separate preprocessing steps in scRNA-seq data analysis.
- To improve the accuracy and biological relevance of cell clustering.
Main Methods:
- Proposed FSSC (Feature Selection for scRNA-seq Clustering) framework.
- Integrated a zero-inflated negative binomial (ZINB) autoencoder.
- Employed a group Lasso penalty and a dedicated clustering loss for joint optimization.
Main Results:
- FSSC simultaneously learns low-dimensional representations and selects cluster-discriminatory genes.
- The framework preserves statistical characteristics and cluster structure of scRNA-seq data.
- Consistently outperformed state-of-the-art methods on simulated and real datasets.
Conclusions:
- FSSC offers a unified approach for enhanced scRNA-seq clustering.
- The method effectively identifies biologically meaningful marker genes.
- Achieved superior clustering accuracy compared to existing methods.
