A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis

Abstract

The Multi-Encoder Variational AutoEncoder (ME-VAE) is a computational model that can control for multiple transformational features in single-cell imaging data, enabling researchers to extract meaningful single-cell information and better separate heterogeneous cell types.