Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.

Area of Science:

• Computational biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) offers high-resolution insights into cellular heterogeneity for biological processes.
• Clinical translation of scRNA-seq is hindered by data sparsity, batch effects, and lack of standardized benchmarks.
• Existing pipelines like Seurat and Scanpy require robust computational strategies for reliable clinical application.

Purpose of the Study:

• To review emerging computational strategies addressing limitations in scRNA-seq for clinical translation.
• To assess transformer-based annotation tools and multimodal integration with spatial transcriptomics.
• To propose a roadmap for clinical adoption, including benchmarked workflows and privacy-aware data sharing.

Main Methods:

• SCTransform for zero-inflation correction and Harmony for batch integration.
• Transformer-based annotation tools (scGPT, CellTypist) for immune profiling.
• Multimodal integration with spatial transcriptomics (10x Visium, cell2location v2) and scANVI for epigenetic analysis.

Main Results:

• Harmony achieves 30% faster alignment than BBKNN for large cohorts.
• Transformer-based tools reach >95% accuracy in immune profiling.
• Spatial methods delineate microenvironmental niches and tumor-immune crosstalk at subcellular resolution.

Conclusions:

• Computational strategies like robust preprocessing, advanced annotation, and multimodal integration are crucial for clinical scRNA-seq.
• Addressing ethical risks and establishing benchmarked workflows are essential for widespread adoption.
• Causal AI and federated learning can enhance data analysis and privacy in scRNA-seq applications.