Single-cell RNA sequencing (scRNA-seq) has transformed the resolution of cellular heterogeneity, offering insights into dynamic biological processes from tumor evolution to immune regulation. However, its clinical translation is limited by challenges such as data sparsity, batch effects (differences caused by technical variation rather than biology), and the absence of standardized benchmarks for core pipelines like Seurat and Scanpy. This review outlines emerging computational strategies that address these limitations: (A) robust preprocessing, including SCTransform for zero-inflation(an excess of zero counts in gene-expression data) correction and Harmony for batch integration-achieving 30% faster alignment than BBKNN in cohorts exceeding 100,000 cells; (B) transformer-based annotation tools such as scGPT and CellTypist, which reach >95% accuracy in immune profiling using models pretrained on 33 million cells; and (C) multimodal integration with spatial transcriptomics (e.g., 10x Visium, cell2location v2), which delineate microenvironmental niches and rare CX3CR1+ T-cell subsets in disease contexts like glioblastoma and severe COVID-19. We further assess how scANVI bridges scRNA-seq and ATAC-seq to uncover epigenetic mechanisms underlying therapy resistance, and how spatial methods elucidate tumor-immune crosstalk at subcellular resolution. Despite these advances, ethical risks remain, particularly around re-identification of rare patient-derived clones such as pre-metastatic cells. To promote clinical adoption, we propose a roadmap that prioritizes benchmarked workflows (e.g., scverse ecosystem), privacy-aware data sharing via federated learning, and causal AI approaches to disentangle biological signal from technical artifact. By synthesizing computational innovations with translational case studies, this review equips researchers to navigate both the analytical and ethical complexities of scRNA-seq in pursuit of actionable diagnostics.
Nesari et al. (2026) studied this question.