基于 GAN 反转的计算表型谱分析和编辑。
GILEA: In silico phenome profiling and editing using GAN Inversion.
机构信息
Department of Pathology and Molecular Pathology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland.
Department of Pathology and Molecular Pathology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland; Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.
出版信息
Comput Biol Med. 2024 Sep;179:108825. doi: 10.1016/j.compbiomed.2024.108825. Epub 2024 Jul 12.
BACKGROUND
Modeling heterogeneous disease states by data-driven methods has great potential to advance biomedical research. However, a comprehensive analysis of phenotypic heterogeneity is often challenged by the complex nature of biomedical datasets and emerging imaging methodologies.
METHODS
Here, we propose a novel GAN Inversion-enabled Latent Eigenvalue Analysis (GILEA) framework and apply it to in silico phenome profiling and editing.
RESULTS
We show the performance of GILEA using cellular imaging datasets stained with the multiplexed fluorescence Cell Painting protocol. The quantitative results of GILEA can be biologically supported by editing of the latent representations and simulation of dynamic phenotype transitions between physiological and pathological states.
CONCLUSION
In conclusion, GILEA represents a new and broadly applicable approach to the quantitative and interpretable analysis of biomedical image data. The GILEA code and video demos are available at https://github.com/CTPLab/GILEA.
背景
通过数据驱动的方法对异质疾病状态进行建模具有推进生物医学研究的巨大潜力。然而，生物医学数据集的复杂性质和新兴的成像方法学常常给全面分析表型异质性带来挑战。
方法
在这里，我们提出了一种新颖的基于 GAN 反演的潜在特征值分析（GILEA）框架，并将其应用于虚拟表型组学分析和编辑。
结果
我们使用经过多色荧光 Cell Painting 方案染色的细胞成像数据集展示了 GILEA 的性能。通过对潜在表示的编辑和生理状态与病理状态之间的动态表型转变的模拟，可以为 GILEA 的定量结果提供生物学支持。
结论
总之，GILEA 代表了一种新的、广泛适用的定量和可解释的生物医学图像数据分析方法。GILEA 代码和视频演示可在 https://github.com/CTPLab/GILEA 上获得。
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