vima-spatial 0.1.0

variationa 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, please clone this repository and add it to your PYTHONPATH. You will first need to install `pytorch'.

demo

Coming soon!