Single-cell differential abundance detection: A new angle on dissecting tumor heterogeneity

Tumor heterogeneity is one of the central challenges in oncology, contributing to treatment resistance and disease recurrence. Bulk RNA sequencing has advanced understanding of tumor biology, yet its averaging effect conceals cell type-specific alterations. Single-cell RNA sequencing overcomes this limitation by capturing gene expression and cellular phenotypes with high-resolution, thereby illuminating tumor composition and the surrounding microenvironment. Within this framework, differential abundance (DA) detection has emerged as a powerful strategy to quantify shifts in cell population proportions across conditions. Unlike differential gene expression, DA highlights compositional changes in cellular ecosystems, offering a structural perspective on tumor dynamics. This review introduces the main categories of DA methods in single-cell RNA sequencing analysis, outlining their modeling strategies, assumptions, and representative applications in oncology. We also discuss key challenges, including reliance on clustering quality and batch correction. By linking methodological principles with biological insight, this review clarifies the role of DA detection in single-cell oncology and provides a conceptual framework for integrating compositional analysis into efforts to understand tumor evolution, treatment response, and disease stratification.