Metadata-Version: 2.2
Name: vima-spatial
Version: 0.1.1.4
Summary: variationa inference-based microniche analysis
Home-page: https://github.com/yakirr/vima
Author: Yakir Reshef
Author-email: yreshef@broadinstitute.org
Project-URL: Bug Tracker, https://github.com/yakirr/vima/issues
Project-URL: Tutorial, https://github.com/yakirr/vima/blob/main/README.md
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.12.3
Description-Content-Type: text/markdown
Requires-Dist: torch>=2.3.0
Requires-Dist: torchvision>=0.18.0
Requires-Dist: anndata>=0.10.7
Requires-Dist: matplotlib
Requires-Dist: multianndata
Requires-Dist: numpy>=1.26.4
Requires-Dist: scanpy
Requires-Dist: opencv-python
Requires-Dist: xarray
Requires-Dist: seaborn
Requires-Dist: pandas>=2.2.3
Requires-Dist: scipy
Requires-Dist: cna>=0.1.7
Requires-Dist: tqdm
Requires-Dist: scikit-image
Requires-Dist: IPython
Requires-Dist: h5py
Requires-Dist: netCDF4

# vima
Variational inference-based microniche analysis is a method for conducting case-control analysis on multi-sample spatial molecular datasets. `vima` can be applied to any spatially resolved molecular technology, is well powered even at the modest sample sizes typical of research cohorts, and avoids traditional, parameter-intensive preprocessing steps such as cell segmentation or clustering of cells into discrete cell types. It works by treating each spatial sample as an image and using a variational autoencoder to extract numerical "fingerprints" from small tissue patches that capture their biological content. It uses these fingerprints to define a large number of "microniches'' – small, potentially overlapping groups of tissue patches with highly similar biology that span multiple samples. It then uses rigorous statistics to identify microniches whose abundance correlates with case-control status.

## installation
To use `vima`, you can either install it directly from the [Python Package Index](https://pypi.org/) by running, e.g.,

`pip install vima-spatial`

or if you'd like to manipulate the source code you can clone this repository and add it to your `PYTHONPATH`.

Note that the package requires a working installation of `pytorch`, and it may be beneficial to first install `pytorch`, verify it works properly, and then install `vima`. For data preprocessing the current version of the package also requires a working `R` environment with the [`harmony` package](https://github.com/immunogenomics/harmony) installed.

## demo
Coming soon!

## citation
Coming soon!
