OmicVerse visualization for bulk, color systems, and single-cell data

Volcano plots visualize differentially expressed genes by plotting log-fold change against p-value significance. OmicVerse provides standardized volcano plot functions that apply professional styling and color schemes automatically, letting you generate publication-ready plots directly from your DEG results with minimal customization.

Yes, OmicVerse supports UMAP and MDE embeddings with advanced color control, including predefined palettes like the Forbidden City scheme and custom gradients. You can color by metadata columns like cell type or cluster, add density overlays and convex hulls, and apply consistent styling across all figures.

OmicVerse enforces consistent styling by initializing default themes and styles at the start of your workflow. This approach ensures all plots—Venn diagrams, volcano plots, embeddings, and boxplots—share professional aesthetics without manual reconfiguration for each figure.

Yes, OmicVerse requires loading and preprocessing data, validating required columns and embeddings, and importing necessary libraries like pandas and scanpy. The Skill enforces these prerequisites to ensure your data structure matches expected formats before generating plots.

Reproduce tutorials by importing omicverse, initializing styles, loading your bulk or single-cell data, validating column names and embeddings, then applying the same functions (ov.pl, ov.embedding, color utilities) shown in the tutorial to your specific dataset.

OmicVerse generates Venn diagrams for DEG overlaps, volcano plots for differential expression, boxplots, UMAP/MDE embeddings with density overlays, and convex hulls. All plots support custom color management and apply standardized styling for publication-ready figures.