dvp-io 0.3.0

Read and write funtionalities from and to spatialdata for deep vizual proteomics

dvp-io

Read and write funtionalities from and to spatialdata for deep visual proteomics

Getting started

Please refer to the documentation, in particular, the API documentation, tutorials, and the FAQs.

Installation

You need to have Python 3.10 or newer installed on your system.

Users

Install the latest release of dvp-io from [PyPI][https://pypi.org/project/dvp-io]:

# Optional: Create a suitable conda envionemnt
conda create -n dvpio python=3.11 -y  && conda activate dvpio

pip install dvp-io

C++ dependencies

Some critical dependencies of dvpio require C++ bindings, so a suitable C++ compiler must be installed.

For Unix Users (Linux, macOS)

Ensure cmake and libssh2 are installed by running:

# Unix
conda install -n dvpio conda-forge::cmake conda-forge::libssh2

Windows users

Windows users require the Microsoft Visual C++ (MSVC) compiler. Before creating the dvpio environment, follow these steps:

1. Download and install Visual Studio.
2. In the installer, select Desktop Development with C++ as a workload.
3. Complete the installation and restart your system if necessary.

After installation, proceed with the dvp-io installation steps above.

Developers

Install the latest development version

In your shell, go to your favorite directory and clone the repository. Then, make an editable install

# Optional create environment
# conda install -n dvpio-dev python=3.11 && conda activate dvpio-dev

# Clone
git clone https://github.com/lucas-diedrich/dvp-io.git

# Go into the directory
cd dvp-io

# Make editable, local installation, including development dependencies
pip install -e ".[dev,doc]"

Release notes

Refer to the Releases page for information on releases and the changelog.

References

SPARCS, a platform for genome-scale CRISPR screening for spatial cellular phenotypes Niklas Arndt Schmacke, Sophia Clara Maedler, Georg Wallmann, Andreas Metousis, Marleen Berouti, Hartmann Harz, Heinrich Leonhardt, Matthias Mann, Veit Hornung bioRxiv 2023.06.01.542416; doi: https://doi.org/10.1101/2023.06.01.542416

Marconato, L. et al. SpatialData: an open and universal data framework for spatial omics. Nat Methods 1–5 (2024) doi:10.1038/s41592-024-02212-x.

Zeng, W.-F. et al. AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics. Nat Commun 13, 7238 (2022).