AgentSkillsCN

omicverse-visualization-for-bulk-color-systems-and-single-cell-d

引导用户使用OmicVerse绘图工具，包括批量、颜色系统和单细胞可视化教程，涵盖维恩图/火山图、调色板选择和高级嵌入布局。

SKILL.md
--- frontmatter
name: omicverse-visualization-for-bulk-color-systems-and-single-cell-d
title: OmicVerse visualization for bulk, color systems, and single-cell data
description: Guide users through OmicVerse plotting utilities showcased in the bulk, color system, and single-cell visualization tutorials, including venn/volcano charts, palette selection, and advanced embedding layouts.

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:

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, 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