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.
The 'omicverse-visualization-for-bulk-color-systems-and-single-cell-d' skill helps users to recreate and adapt visualizations from the OmicVerse plotting tutorials, which cover bulk RNA-seq data, color systems, and single-cell data. By leveraging OmicVerse's plotting utilities, users can easily generate advanced plots such as venn and volcano charts, manage color palettes, and work with single-cell embedding layouts. This skill simplifies the plotting process by providing predefined functions and styles, enabling the generation of informative and visually appealing data representations in bioinformatics workflows.
The skill provides multiple features, including bulk RNA-seq visualizations like volcano plots and box plots, color palette management from the Forbidden City color system, and seamless integration with single-cell analysis workflows. Users can visualize and compare gene expression, identify key clusters, and create intuitive visual embeddings using predefined or custom color palettes. Data preprocessing, color management, and validation are key components of this skill, offering users a robust environment for generating high-quality visual data representations. Ideal for bioinformaticians and data scientists working with gene expression and single-cell RNA-seq data, this skill allows for flexible and efficient data visualization and analysis.
To set up the environment, import the necessary libraries such as 'omicverse' and 'matplotlib', then apply the default OmicVerse styling by calling 'ov.plot_set()'. You can load data using 'ov.read()' or preprocess it with 'ov.pp.preprocess()'.
This skill allows you to create various plots such as venn diagrams, volcano plots, box plots for bulk RNA-seq, as well as single-cell visualizations like UMAP or PCA embeddings. You can also manage color palettes and gradients for visualization.
Yes, the skill requires input data in formats such as AnnData or CSV files. For single-cell data, ensure the AnnData object is preprocessed with required information (e.g., embeddings and categorical labels) before plotting.
Yes, you can customize the color palettes by using predefined color systems like the Forbidden City palette or by defining your own color mappings for different cell types or groups.
Yes, the venn diagram function supports no more than 4 groups for displaying overlaps in DEG lists.
Quick Setup:
.claude/skills/Repository
Starlitnightly/omicverse