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High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile cannot capture the full underlying complexity in cell organization, while recent weakly supervised machine-learning-based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and used it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility and mechanism of action classification and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology. A record of this paper's transparent peer review process is included in the supplemental information.