What are the key considerations in single-cell RNA-Seq data analysis?

Single-cell RNA-Seq (scRNA-Seq) analysis requires handling unique challenges: 1) Quality control - filter cells based on gene count, UMI count, and mitochondrial percentage to remove empty droplets and doublets. 2) Normalization - account for technical variation and library size differences using scran or SCTransform. 3) Dimensionality reduction - reduce noise and visualization using PCA followed by UMAP/t-SNE. 4) Batch correction - remove technical batch effects using Harmony, Seurat integration, or MNN. 5) Clustering - identify cell populations using graph-based methods (Louvain, Leiden). 6) Cell type annotation - use marker genes or reference datasets (SingleR, CellTypist). 7) Trajectory analysis - infer developmental trajectories using Monocle, Velocyto. Tools: Seurat, Scanpy.