Explain the workflow and analytical challenges in single-cell RNA sequencing.

scRNA-seq workflow involves cell isolation (FACS, microfluidics like 10x Genomics), lysis, reverse transcription with unique molecular identifiers (UMIs), amplification, library preparation, and sequencing. Computational analysis includes quality control (filtering low-quality cells, doublets), normalization (accounting for library size, dropout events), dimensionality reduction (PCA, t-SNE, UMAP), clustering (Louvain, Leiden algorithms), differential expression, trajectory inference, and cell-type annotation. Challenges include dropout events (technical zeros), batch effects, and computational scalability. Integration methods (Seurat, Harmony) combine datasets. Interpretation requires biological context and validation of novel cell populations.