This work applies deep variational autoencoder learning architecture to study multi-cellular
growth characteristics of human mammary epithelial cells in response to diverse microenvironment
perturbations. Our approach introduces a novel method of visualizing learned feature
spaces of trained variational autoencoding models that enables visualization of principal
features in two dimensions. We find that unsupervised learned features more closely
associate with expert annotation of cell colony organization than biologically-inspired
hand-crafted features, demonstrating the utility of deep learning systems to meaningfully
characterize features of multi-cellular growth characteristics in a fully unsupervised
and data-driven manner.
