Single-Cell Visualization Plotting functions for single-cell RNA-seq data including embeddings, marker expression, trajectory, and QC visualizations. Contents File Purpose visualization.py UMAP, t-SNE, PCA plots, marker dotplots, trajectory, and QC figures Key Functions Function Description plot_umap() UMAP embedding colored by cluster, gene, or metadata plot_tsne() t-SNE embedding visualization plot_pca() PCA scatter plot for single-cell data plot_trajectory() Trajectory with pseudotime coloring plot_marker_expression() Dotplot, heatmap, or violin for marker genes plot_qc_metrics() QC violin plots for gene counts, UMIs, mito fraction plot_cluster_comparison() Side-by-side cluster composition comparison Usage from metainformant.singlecell.visualization.visualization import plot_umap, plot_marker_expression plot_umap(data, color="cluster", output_path="output/umap.png") plot_marker_expression(data, marker_genes=["CD4", "CD8A"], method="dotplot")