plotomics (Python)

High-performance bioinformatics visualization widgets backed by a shared JavaScript core, exposed to Python through anywidget.

Works in Jupyter, JupyterLab, marimo, Google Colab, VS Code, Shiny for Python and Streamlit. Large numeric columns are shipped to the browser as a single binary buffer (not JSON), so millions of points stay interactive.

import numpy as np, pandas as pd
from plotomics import Volcano

n = 200_000
df = pd.DataFrame({
    "x": np.random.randn(n),                 # log2 fold change
    "y": np.abs(np.random.randn(n)) * 3,     # -log10 p-value
    "label": [f"GENE{i}" for i in range(n)],
})
Volcano(df, fc_threshold=1.0, p_threshold=0.05)

Install

pip install plotomics

Shiny for Python

The widgets are anywidgets, so they render in Shiny for Python through shinywidgets: output_widget in the UI, @render_widget on the server:

from shiny import App, ui
from shinywidgets import output_widget, render_widget
from plotomics import Volcano
import numpy as np, pandas as pd

app_ui = ui.page_fluid(output_widget("plot"))

def server(input, output, session):
    @render_widget
    def plot():
        n = 100_000
        df = pd.DataFrame({"x": np.random.randn(n), "y": np.abs(np.random.randn(n)) * 3})
        return Volcano(df)

app = App(app_ui, server)

Development

The widget JS is built from the monorepo root and copied into src/plotomics/static/:

pnpm dist          # build JS + sync bundles into this package
pip install -e ".[dev]"
pytest