VoDEx is an open-source Python library that streamlines the management and analysis of volumetric functional imaging data. It offers a suite of tools for creating, organizing, and storing information pertaining to image acquisition and time annotation. Additionally, it allows for the retrieval of imaging data based on specific experimental conditions, enabling researchers to access and analyze the data easily. VoDEx is available as both a standalone Python package and a napari plugin, providing a user-friendly solution for processing volumetric functional imaging data, even for researchers without extensive programming experience.
You can install vodex via pip:
pip install vodex
or via conda ( although this is not recommended as the conda package might be not up to date ):
conda install vodex -c conda-forge
To get started with vodex, please refer to the Documentation. The documentation is continuously updated through the use of mkdocs and mkdocstrings packages, as well as GitHub actions, ensuring that any changes to the API are promptly reflected in the documentation.
VoDEx is designed to address the challenges in functional imaging studies where accurate synchronization of the time course of experimental manipulations and stimulus presentations with resulting imaging data is crucial for analysis. It integrates the information about individual image frames, volumes, and experimental conditions and allows the retrieval of sub-portions of the 3D-time series datasets based on any of these identifiers. It logs all information related to the experiment into an SQLite database, enabling later data verification and sharing in accordance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles.
VoDEx is implemented as a napari plugin for interactive use with a GUI and as an open-source Python package, making it a useful tool for image analysis and allowing for integration into a wide range of analysis pipelines.
VoDEx has been successfully applied in the study of numerosity estimation in zebrafish larvae, where it played a key role in the processing of whole-brain functional imaging data acquired using light-sheet fluorescence microscopy. Some use case scenarious such as using vodex for batch processing, creating and using time annotations is described in the supplemental note as a part of the vodex paper. Full analysis pipelines for different numerosity stimuli combinations are available as sets of Jupyter notebooks at github.com/LemonJust/numan under notebooks/individual datasets.
Contributions are very welcome. Tests can be run with [tox], please ensure the coverage at least stays the same before you submit a pull request.
Distributed under the terms of the [BSD-3] license,
vodex is free and open source software
If you use VoDEx in your research, please cite our paper:
Anna Nadtochiy, Peter Luu, Scott E Fraser, Thai V Truong, VoDEx: a Python library for time annotation and management of volumetric functional imaging data, Bioinformatics, Volume 39, Issue 9, September 2023, btad568, https://doi.org/10.1093/bioinformatics/btad568