OpenDVP
Overview
OpenDVP is an open-source framework designed to support Deep Visual Proteomics (DVP) across multiple modalities using community-supported tools. OpenDVP empowers researchers to perform Deep Visual Proteomics using open-source software. It integrates with community data standards such as AnnData and SpatialData to ensure interoperability with popular analysis tools like Scanpy, Squidpy, and Scimap.
Getting started
Please refer to the documentation, particularly the API documentation.
Installation
openDVP needs Python 3.12 or 3.13. If you are new to creating Python environments, we suggest uv or pixi.
First create and activate an environment — with conda, for example:
conda create --name opendvp -y python=3.12
conda activate opendvp
Then install openDVP from PyPI:
pip install opendvp
The interactive napari viewer is an optional extra, because it pulls in PyQt6, which is
GPL-3.0-only:
pip install "opendvp[napari]"
To install the latest version:
pip install git+https://github.com/CosciaLab/openDVP.git@main
Tutorials
To understand what are the applications of openDVP, please check our
Tutorials.
Briefly, they introduce users to (1) Image analysis, (2) downstream proteomic analysis, and (3) Integration of imaging with proteomic data. Please download our Demo Dataset to best follow the tutorials :)
Community & Discussions
We are excited to hear from you and together we can improve spatial protemics.
We welcome questions, feedback, and community contributions!
Join the conversation in the GitHub Discussions tab.
Citation
Please cite the BioArxiv:
Nimo, J., Fritzsche, S., Valdes, D. S., Trinh, M., Pentimalli, T., Schallenberg, S., Klauschen, F., Herse, F., Florian, S., Rajewsky, N., & Coscia, F. (2025). OpenDVP: An experimental and computational framework for community-empowered deep visual proteomics [Preprint]. bioRxiv. https://doi.org/10.1101/2025.07.13.662099
Motivation
Deep Visual Proteomics (DVP) combines high-dimensional imaging, spatial analysis, and machine learning to extract complex biological insights from tissue samples. However, many current DVP tools are locked into proprietary formats, restricted software ecosystems, or closed-source pipelines that limit reproducibility, accessibility, and community collaboration.
- Work transparently across modalities and analysis environments
- Contribute improvements back to a growing ecosystem
- Avoid vendor lock-in for critical workflows
Qupath-to-LMD
Qupath to lmd is a tool we use to make it as easy as possible to go from QuPath annotations to LMD contours Check our Qupath-to-LMD Webapp, or watch our Youtube tutorial:
1 maintainer
Metadata
1 maintainer
Release files for opendvp 0.8.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| opendvp-0.8.0.tar.gz | 66.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| opendvp-0.8.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 163.3 kB
Release files / opendvp-0.8.0.tar.gz
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| Size | 66.7 kB |
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twine/7.0.0 CPython/3.13.14
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Release files / opendvp-0.8.0-py3-none-any.whl
| Download URL | opendvp-0.8.0-py3-none-any.whl |
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| Size | 96.5 kB |
| Tags | Python 3 |
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No |
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twine/7.0.0 CPython/3.13.14
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