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2 changes: 2 additions & 0 deletions .github/workflows/docs.yml
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Expand Up @@ -4,6 +4,8 @@ on:
push:
tags:
- 'v*'
branches:
- docs

jobs:
build-docs:
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11 changes: 7 additions & 4 deletions docs/Workflows/index.md
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Expand Up @@ -35,10 +35,13 @@ Ready to explore the proteomics of more complex tissues? or you are planning a l

Requirements and workflow vary so much between projects that it is not worth generalizing. But there are four main components:

1. Image processing
2. Image analysis
3. Sample collection and LCMS
4. Downstream proteomic analysis
1. Image acquisition
2. Image processing
3. Image analysis
4. Sampling strategy
5. Sample collection
6. Sample prepatation and LCMS
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Copilot AI Jul 7, 2025

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Typo: 'prepatation' should be 'preparation'.

Suggested change
6. Sample prepatation and LCMS
6. Sample preparation and LCMS

Copilot uses AI. Check for mistakes.
7. Downstream proteomic analysis

openDVP highly recommends utilizing open-source image processing pipelines: MCMICRO and SOPA are wonderful examples, each with their tradeoffs.

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6 changes: 0 additions & 6 deletions docs/api/generated/opendvp.io.adata_to_perseus.rst

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6 changes: 6 additions & 0 deletions docs/api/generated/opendvp.io.adata_to_voronoi.rst
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opendvp.io.adata\_to\_voronoi
=============================

.. currentmodule:: opendvp.io

.. autofunction:: adata_to_voronoi
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6 changes: 0 additions & 6 deletions docs/api/generated/opendvp.preprocessing.rescale.rst

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6 changes: 0 additions & 6 deletions docs/api/generated/opendvp.tl.impute_single.rst

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1 change: 1 addition & 0 deletions docs/api/io.md
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Expand Up @@ -9,6 +9,7 @@
:toctree: generated

io.adata_to_qupath
io.adata_to_voronoi
io.import_thresholds
io.quant_to_adata
io.segmask_to_qupath
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16 changes: 7 additions & 9 deletions docs/index.md
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[![PyPI version](https://img.shields.io/pypi/v/openDVP.svg)](https://pypi.org/project/openDVP/)
[![License](https://img.shields.io/github/license/CosciaLab/openDVP.svg)](https://github.com/CosciaLab/opendvp/blob/main/LICENSE)

`opendvp` is a python package containing different tools enabling users to perform deep visual proteomics. To perform quality control and image analysis of multiplex immunofluorescence. Also to integrate imaging datasets with proteomic datasets with [Spatialdata](https://github.com/scverse/spatialdata). Lastly, it contains a powerful toolkit for label-free downstream proteomic analysis.
`opendvp` is a python package offering a comprehensive set of tools for deep visual proteomics. It supports quality control and image analysis of multiplex immunofluorescence data. openDVP facilitates the integration of imaging datasets with proteomic datasets with [Spatialdata](https://github.com/scverse/spatialdata). Lastly, it contains a powerful toolkit for label-free downstream proteomic analysis.

It is a package that leverages the [scverse](https://www.scverse.org/) ecosystem, designed for easy interoperability with `anndata`, `scanpy`, `decoupler`, `scimap`, and other related packages.

Expand Down Expand Up @@ -61,12 +61,11 @@ pip install 'openDVP[spatialdata]'
pip install git+https://github.com/CosciaLab/openDVP.git@main
```


## Tutorials

- [Tutorial 1: Image analysis](tutorials/T1_ImageAnalysis)
- [Tutorial 2: Integration of imaging with proteomics](tutorials/T2_ProteomicsIntegration)
- [Tutorial 3: Downstream proteomics analysis](tutorials/T3_DownstreamProteomics)
- [Tutorial 1: Image analysis](Tutorials/T1_ImageAnalysis)
- [Tutorial 2: Integration of imaging with proteomics](Tutorials/T2_ProteomicsIntegration)
- [Tutorial 3: Downstream proteomics analysis](Tutorials/T3_DownstreamProteomics)


## Contact
Expand All @@ -77,12 +76,11 @@ For questions about openDVP and the DVP workflow you are very welcome to post a

Not yet available.


```{toctree}
:maxdepth: 2
:hidden:

api/index
tutorials/index
workflows/index
```
Tutorials/index
Workflows/index
```
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