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

Overview

Leverage this skill when a user wants help recreating or adapting plots from the OmicVerse plotting tutorials: - t_visualize_bulk.ipynb - t_visualize_colorsystem.ipynb - t_visualize_single.ipynb

It covers how to configure OmicVerse's plotting style, choose colors from the Forbidden City palette, and generate bulk as well as single-cell specific figures.

Instructions

1. Set up the plotting environment - Import omicverse as ov, matplotlib.pyplot as plt, and other libraries required by the user's request (pandas, seaborn, scanpy, etc.). - Call ov.ov_plot_set() (or ov.plot_set() depending on the installed version) to apply OmicVerse's default styling before generating figures. - Load example data via ov.read(...)/ov.pp.preprocess(...) or instruct users to supply their own AnnData/CSV files.
2. Bulk RNA-seq visuals (t_visualize_bulk) - Use ov.pl.venn(sets=..., palette=...) to display overlaps among DEG lists (no more than 4 groups). Encourage setting sets as a dictionary of set names → gene lists. - For volcano plots, load the DEG table (result = ov.read('...csv')) and call ov.pl.volcano(result, pval_name='qvalue', fc_name='log2FoldChange', ...). Explain optional keyword arguments such as sig_pvalue, sig_fc, palette, and label formatting. - To compare group distributions with box plots, gather long-form data (e.g., from seaborn.load_dataset('tips')) and invoke ov.pl.boxplot(data, x_value=..., y_value=..., hue=..., ax=ax, palette=...). Mention how to adjust figure size, legend placement, and significance annotations.
3. Color management (t_visualize_colorsystem) - Introduce the color book via fb = ov.pl.ForbiddenCity() and demonstrate fb.get_color(name='凝夜紫') for specific hues. - Show how to pull predefined palettes (ov.pl.green_color, ov.pl.red_color, etc.) and build dicts mapping cell types/groups to color hex codes. - For segmented gradients, combine colors and call ov.pl.get_cmap_seg(colors, name='custom'), then pass the colormap into Matplotlib/Scanpy plotting functions. - Highlight using these palettes in embeddings: ov.pl.embedding(adata, basis='X_umap', color='clusters', palette=color_dict, ax=ax).

4. Single-cell visualizations (t_visualize_single) - Remind users to preprocess AnnData if needed (adata = ov.pp.preprocess(adata, mode='shiftlog|pearson', n_HVGs=2000)). - IMPORTANT - Data validation: Before plotting, always verify that required data exists: ```python # Before plotting by clustering or other categorical variable color_col = 'leiden' # or 'clusters', 'celltype', etc. if color_col not in adata.obs.columns: raise ValueError(f"Column '{color_col}' not found in adata.obs. Available columns: {list(adata.obs.columns)}")

   # Before plotting embeddings basis = 'X_umap' # or 'X_pca', 'X_tsne', etc. if basis not in adata.obsm.keys(): raise ValueError(f"Embedding '{basis}' not found in adata.obsm. Available embeddings: {list(adata.obsm.keys())}") `` - For palette optimization, useov.pl.optim_palette(adata, basis='X_umap', colors='clusters')to auto-generate color schemes when categories clash. - Reproduce stacked proportions withov.pl.cellproportion(adata, groupby='clusters', celltype_clusters='celltype', ax=ax)and transform into stacked area charts by settingkind='area'. - Showcase compound embedding utilities: -ov.pl.embedding_celltypeto place counts/proportions alongside UMAPs. -ov.pl.ConvexHullorov.pl.contourfor highlighting regions of interest. -ov.pl.embedding_adjustto reposition legends automatically. -ov.pl.embedding_densityfor density overlays, controlling smoothness withadjust. - For spatial gene density, describe the workflow:ov.pl.calculate_gene_density(adata, genes=[...], basis='spatial'), then overlay withov.pl.embedding(..., layer='gene_density', cmap='...'). - Cover additional charts likeov.pl.single_group_boxplot,ov.pl.bardotplot,ov.pl.dotplot, andov.pl.marker_heatmap, emphasizing input formats (long-form DataFrame vs. AnnData with.obsannotations) and optional helpers such asov.pl.add_paluefor manual p-value annotations. 5. **Finishing touches and exports** - Encourage adding titles, axis labels, andfig.tight_layout()to prevent clipping. - Suggest saving figures withfig.savefig('plot.png', dpi=300, bbox_inches='tight')and documenting color mappings for reproducibility. - Troubleshoot common issues: - **Missing AnnData keys**: Always validateadata.obscolumns andadata.obsm` embeddings exist before plotting - Palette names not found: Verify color dictionaries match actual category values - Matplotlib font rendering: When using Chinese characters, ensure appropriate fonts are installed - "Could not find X in adata.obs": Check that clustering or annotation has been performed before trying to visualize results. Use defensive checks to compute missing prerequisites on-the-fly.

Examples

• "Plot a three-set Venn diagram of overlapping DEG lists and reuse Forbidden City colors for consistency."
• "Load the dentate gyrus AnnData, color clusters with fb.get_color selections, and render an embedding with adjusted legend placement."
• "Generate single-cell proportion bar/area plots plus gene-density overlays using OmicVerse helper functions."

References

• Bulk tutorial: t_visualize_bulk.ipynb
• Color system tutorial: t_visualize_colorsystem.ipynb
• Single-cell tutorial: t_visualize_single.ipynb
• Quick reference snippets: reference.md