Shpigler Alon, Kolet Naor, Golan Shahar, Weisbart Erin, Zaritsky Assaf
Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
Department of Computer Science, Jerusalem College of Technology, 91160 Jerusalem, Israel.
bioRxiv. 2024 Jun 3:2024.06.01.595856. doi: 10.1101/2024.06.01.595856.
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 can not capture the full underlying complexity in cell organization, while recent weakly 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 use 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, 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.
基于高内涵图像的表型分析结合了自动显微镜和分析技术，以识别细胞形态的表型改变，并深入了解细胞的生理状态。表型概况的经典表示无法捕捉细胞组织中潜在的全部复杂性，而最近基于弱机器学习的表示学习方法难以从生物学角度进行解释。我们利用大量对照孔来学习对照实验的分布，并以此构建基于自监督重建异常的表示，该表示对复杂的形态特征间依赖性进行编码，同时保留表示的可解释性。在四个公开的细胞绘画数据集上，针对两种经典表示，我们评估了基于异常的表示在下游任务中的性能。基于异常的表示提高了可重复性、作用机制分类能力，并补充了经典表示。基于自动编码器的异常的无监督可解释性识别出了导致异常的特定特征间依赖性。基于异常的表示这一总体概念可应用于细胞生物学的其他应用。
