The rapid evolution of single-cell technologies has generated vast, multimodal datasets encompassing genomic, transcriptomic, proteomic, and spatial information. However, high dimensionality, noise, and computational costs pose significant challenges, often introducing bias through traditional feature selection methods, such as highly variable gene selection. Unsupervised machine learning (ML) provides a solution by identifying informative features without predefined labels, thereby minimizing bias and capturing complex patterns. This paper reviews a diverse array of unsupervised ML techniques tailored for single-cell data. These approaches could enhance downstream analyses, such as clustering, dimensionality reduction, visualization, and data denoising, and reveal biologically relevant gene modules. Despite their advantages, challenges such as data sparsity, parameter tuning, and scalability persist. Future directions include integrating multiomic data, incorporating domain-specific knowledge, and developing scalable and interpretable algorithms. By addressing these challenges, unsupervised ML-based feature selection promises to revolutionize single-cell data analysis, driving unbiased insights into cellular heterogeneity and advancing biological discovery.
Chatterjee et al. (Thu,) studied this question.