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

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…