A) Quality control (QC) metrics visualized as plots showing cell counts, gene counts, and mitochondrial percentages. B) Bar plot represents the cell count based on condition. C) Principal component analysis (PCA) plot for dimensionality reduction. D) UMAP/t-SNE visualizations of clusters representing cellular heterogeneity. E) Bar plot represents the cell counts for each cluster. F) UMAP/t-SNE plots after doublet detection and removal. G) Heatmap showing marker genes identified for each cluster. H) UMAP visualization with cell type annotations based on ScType, SingleR, GPTCelltype, or own annotation. I-K) Violin plot, feature plot, and ridge plot illustrating gene expression patterns across clusters. L-N) Condition-based differential expression analysis visualized using violin, ridge and volcano plots.