vima-spatial 0.2.1

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 permutation testing to identify microniches whose abundance correlates significantly with case-control status after accounting for multiple testing.

installation

vima can be installed via pip as follows:

pip install vima-spatial

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.