LEA: Latent Eigenvalue Analysis in application to high-throughput phenotypic profiling

Wu, J.; Koelzer, V. H.

doi:10.1101/2023.02.10.528026

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bioRxiv · 10.1101/2023.02.10.528026

LEA: Latent Eigenvalue Analysis in application to high-throughput phenotypic profiling

Wu, J.·Koelzer, V. H.

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Abstract

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. Here, we propose a novel GAN Inversion-enabled Latent Eigenvalue Analysis (GILEA) framework and apply it to phenome profiling and editing. As key use cases for fluorescence and natural imaging, we demonstrate the power of GILEA using publicly available SARS-CoV-2 datasets stained with the multiplexed fluorescence cell-painting protocol as well as real-world medical images of common skin lesions captured by dermoscopy. The quantitative results of GILEA can be biologically supported by editing latent representations and simulating dynamic phenotype transitions between physiological and pathological states. 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 publicly available at https://github.com/CTPLab/GILEA.

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Wu, J., Koelzer, V. H.. 2023-02-13. LEA: Latent Eigenvalue Analysis in application to high-throughput phenotypic profiling. https://doi.org/10.1101/2023.02.10.528026

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