Data & ReproScientific VisualizationFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-data-visualization-interactive-visualization

Maintainer FreedomIntelligence · Last updated April 1, 2026

Interactive omics visualizations with Plotly, Bokeh, or shiny.

OpenClawNanoClawAnalysisWritingbio-data-visualization-interactive-visualization🧠 bioos extended suitebioos extended bioinformatics suiteinteractive

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-data-visualization-interactive-visualization

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

fig = px.scatter(df, x='PC1', y='PC2', color='condition', hover_data=['sample'], title='PCA Plot') fig.write_html('pca_interactive.html') fig.show().
p <- ggplot(df, aes(PC1, PC2, color = condition, text = sample)) + geom_point() ggplotly(p).

Source Doc

Excerpt From SKILL.md

plotly (Python)

import plotly.express as px
import plotly.graph_objects as go
import pandas as pd

## Native plotly

plot_ly(df, x = ~PC1, y = ~PC2, color = ~condition, text = ~sample,
        type = 'scatter', mode = 'markers') %>%
    layout(title = 'PCA Plot')

plotly

fig.write_html('plot.html') fig.write_json('plot.json')

Use cases

Use bio-data-visualization-interactive-visualization to prepare publication-quality figures.
Apply bio-data-visualization-interactive-visualization when results need clear visual communication.