Latch Plots are reactive Python notebooks designed for building interactive, shareable applications tailored to scientists. Developers can use Plots to write code that powers custom user interfaces, dynamic visualizations, and domain-specific analysis tools—all within a reproducible notebook environment. Key features include:
  • Python-based GUIs: Create intuitive, customizable interfaces using native Python, without needing to write JavaScript or HTML.
  • Reactive execution: Automatically re-run relevant cells when inputs change, enabling fast, interactive workflows.
  • Shareable as Apps: Turn any notebook into a deployable App with one click, making it easy for scientists to explore data without writing code.
  • Specialized biological visualizations: Built-in support for genome browsers, H5AD viewers, spatial image alignment, and more.
  • PlotsAI: Use natural language to explore datasets, generate visualizations, and summarize results

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Get Started

Plots Basics

Learn how to develop your first Plot notebook and publish an App.

Notebook Components

Create visualizations with variables & access dev mode.

Data Plotting

Use different plot types, customize your plot, & filter your table!

Data Transformations

Learn how to use Python to create data collection widgets.

Filtering with Tables

View data and filter it for downstream use.

Text Display

Write markdown descriptions

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Integrations

Biotech teams use plot dashboards to integrate the data avilable on Latch with external databases and warehouses. Here we provide quickstart documentation and boilerplate code to do this for popular systems:

Snowflake

Postgres

LLM