AI and the World's Hard Problems · Life sciences · Nature (machine learning) · 2026-10-06
Accurate and well-powered case–control analysis of spatial molecular data
Existing methods for spatial molecular data often use restrictive assumptions that can miss important disease signals. VIMA, a deep-learning method, uses an ensemble of variational autoencoders to summarize small tissue patches and identify microniches whose abundance correlates with case or control status. In simulations it was well calibrated and powerful, and applied to datasets spanning three diseases it recovered known biology and identified new spatial features of disease.