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

• 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.

• 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.

• 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) .

• 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.

• 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