Omicverse Visualization For Bulk Color Systems And Single Cell D

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:

     # 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, use ov.pl.optim_palette(adata, basis='X_umap', colors='clusters') to auto-generate color schemes when categories clash.
   • Reproduce stacked proportions with ov.pl.cellproportion(adata, groupby='clusters', celltype_clusters='celltype', ax=ax) and transform into stacked area charts by setting kind='area'.
   • Showcase compound embedding utilities:
     • ov.pl.embedding_celltype to place counts/proportions alongside UMAPs.
     • ov.pl.ConvexHull or ov.pl.contour for highlighting regions of interest.
     • ov.pl.embedding_adjust to reposition legends automatically.
     • ov.pl.embedding_density for density overlays, controlling smoothness with adjust.
   • For spatial gene density, describe the workflow: ov.pl.calculate_gene_density(adata, genes=[...], basis='spatial'), then overlay with ov.pl.embedding(..., layer='gene_density', cmap='...').
   • Cover additional charts like ov.pl.single_group_boxplot, ov.pl.bardotplot, ov.pl.dotplot, and ov.pl.marker_heatmap, emphasizing input formats (long-form DataFrame vs. AnnData with .obs annotations) and optional helpers such as ov.pl.add_palue for manual p-value annotations.
5. Finishing touches and exports
   • Encourage adding titles, axis labels, and fig.tight_layout() to prevent clipping.
   • Suggest saving figures with fig.savefig('plot.png', dpi=300, bbox_inches='tight') and documenting color mappings for reproducibility.
   • Troubleshoot common issues:
     • Missing AnnData keys: Always validate adata.obs columns and adata.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