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.