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Observing changes in cellular phenotypes under experimental interventions is a powerful approach for studying biology and has many applications, including treatment design. Unfortunately, not all interventions can be tested experimentally, which limits our ability to study complex phenomena such as combinatorial treatments or continuous time or dose responses. In this work, we explore unbiased, image-based generative models to analyze phenotypic changes in cell morphology and tissue organization. The proposed approach is based on generative adversarial networks (GAN) conditioned on feature representations obtained with self-supervised learning. Our goal is to ensure that image-based phenotypes are accurately encoded in a latent space that can be later manipulated and used for generating images of novel phenotypic variations. We present an evaluation of our approach for phenotype analysis in a drug screen and a cancer tissue dataset.