vima-spatial 0.1.5.2

variational inference-based microniche analysis

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 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 and arrow package installed. You can create such an environment easily with conda by running

conda create -n Renv -c conda-forge r-base r-harmony r-arrow

To use vima you'll need the path to the Rscript executable in this R environment, which you can get by running which Rscript with the R environment active.

demo

Take a look at our demo to see how to get started with an example analysis. We plan to put up demos for other data modalities in the future.

citation

If you use vima, please cite:

Y. Reshef, et al. Powerful and accurate case-control analysis of spatial molecular data. bioRxiv. https://doi.org/10.1101/2025.02.07.637149v1.