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OpenDVP

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Screenshot 2026-01-16 at 09 09 26

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

You will need Python 3.11 or 3.12 installed on your system. If you are new to creating Python environments, we suggest you use uv or pixi.

You can install openDVP via pip:

conda create --name opendvp -y python=3.12
pip install opendvp

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:

Tutorial

About

Facilitating the use of Deep Visual Proteomics

coscialab.github.io/openDVP/

Resources

Readme
GPL-3.0 license
Activity
Custom properties

Stars

17 stars

Watchers

2 watching

Forks

2 forks
Report repository

Releases

Packages

Used by

Contributors

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GitHub - CosciaLab/openDVP: Facilitating the use of Deep Visual Proteomics · GitHub
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OpenDVP
DocsCIPython versionsPlatformsPyPI versionLicensecodecov
Screenshot 2026-01-16 at 09 09 26
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
You will need Python 3.11 or 3.12 installed on your system.
If you are new to creating Python environments, we suggest you use uv or pixi.
You can install openDVP via pip:
conda create --name opendvp -y python=3.12
pip install opendvp
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:
Tutorial
About
Facilitating the use of Deep Visual Proteomics
coscialab.github.io/openDVP/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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GitHub - CosciaLab/openDVP: Facilitating the use of Deep Visual Proteomics · GitHub
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OpenDVP
DocsCIPython versionsPlatformsPyPI versionLicensecodecov
Screenshot 2026-01-16 at 09 09 26
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
You will need Python 3.11 or 3.12 installed on your system.
If you are new to creating Python environments, we suggest you use uv or pixi.
You can install openDVP via pip:
conda create --name opendvp -y python=3.12
pip install opendvp
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:
Tutorial
About
Facilitating the use of Deep Visual Proteomics
coscialab.github.io/openDVP/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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GitHub - CosciaLab/openDVP: Facilitating the use of Deep Visual Proteomics · GitHub
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OpenDVP
DocsCIPython versionsPlatformsPyPI versionLicensecodecov
Screenshot 2026-01-16 at 09 09 26
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
You will need Python 3.11 or 3.12 installed on your system.
If you are new to creating Python environments, we suggest you use uv or pixi.
You can install openDVP via pip:
conda create --name opendvp -y python=3.12
pip install opendvp
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:
Tutorial
About
Facilitating the use of Deep Visual Proteomics
coscialab.github.io/openDVP/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - CosciaLab/openDVP: Facilitating the use of Deep Visual Proteomics · GitHub
Skip to content
BranchesTags
Repository files navigation
OpenDVP
DocsCIPython versionsPlatformsPyPI versionLicensecodecov
Screenshot 2026-01-16 at 09 09 26
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
You will need Python 3.11 or 3.12 installed on your system.
If you are new to creating Python environments, we suggest you use uv or pixi.
You can install openDVP via pip:
conda create --name opendvp -y python=3.12
pip install opendvp
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:
Tutorial
About
Facilitating the use of Deep Visual Proteomics
coscialab.github.io/openDVP/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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BranchesTags
Repository files navigation
OpenDVP
DocsCIPython versionsPlatformsPyPI versionLicensecodecov
Screenshot 2026-01-16 at 09 09 26
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
You will need Python 3.11 or 3.12 installed on your system.
If you are new to creating Python environments, we suggest you use uv or pixi.
You can install openDVP via pip:
conda create --name opendvp -y python=3.12
pip install opendvp
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:
Tutorial
About
Facilitating the use of Deep Visual Proteomics
coscialab.github.io/openDVP/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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GitHub - CosciaLab/openDVP: Facilitating the use of Deep Visual Proteomics · GitHub
Skip to content
BranchesTags
Repository files navigation
OpenDVP
DocsCIPython versionsPlatformsPyPI versionLicensecodecov
Screenshot 2026-01-16 at 09 09 26
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
You will need Python 3.11 or 3.12 installed on your system.
If you are new to creating Python environments, we suggest you use uv or pixi.
You can install openDVP via pip:
conda create --name opendvp -y python=3.12
pip install opendvp
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:
Tutorial
About
Facilitating the use of Deep Visual Proteomics
coscialab.github.io/openDVP/
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Readme
GPL-3.0 license
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GitHub - CosciaLab/openDVP: Facilitating the use of Deep Visual Proteomics · GitHub
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OpenDVP
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Screenshot 2026-01-16 at 09 09 26
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
You will need Python 3.11 or 3.12 installed on your system.
If you are new to creating Python environments, we suggest you use uv or pixi.
You can install openDVP via pip:
conda create --name opendvp -y python=3.12
pip install opendvp
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:
Tutorial
About
Facilitating the use of Deep Visual Proteomics
coscialab.github.io/openDVP/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
Contributors
Languages