Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve

Abstract

There is interest in measuring the influence of spatial cellular organization on pathophysiology, which is being accomplished through spatial transcriptomics. There the authors present UniCell Deconvolve, a pre-trained deep learning model that predicts cell identity and deconvolves cell type fractions using a 28 M cell database.