dvp-io

dvp-io#

Tests Documentation Code Coverage

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:

# 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).