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A conditional generative model to disentangle morphological variation from batch effects in model organism imaging

Ricardo M Valdarrago1, Hongru Hu1,2,3, Ruoxin Li1,2,4

  • 1Department of Molecular and Cellular Biology, University of California, Davis, CA, USA.

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This study introduces a new conditional latent diffusion model (cLDM) to separate biological data from technical artifacts in zebrafish imaging. The model accurately classifies genotypes by correcting for batch effects in high-throughput phenotyping.

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Area of Science:

  • Genetics and Genomics
  • Computational Biology
  • Bioimaging

Background:

  • Genotype-to-phenotype studies are crucial for understanding genetic variation.
  • High-throughput imaging of model organisms like zebrafish is vital for phenotyping.
  • Technical batch effects from group housing (e.g., clutches) confound image analysis, obscuring true genetic differences.

Purpose of the Study:

  • To develop a novel computational approach for disentangling technical batch effects from biological variation in high-throughput imaging data.
  • To improve the accuracy of genotype classification in model organisms despite confounding environmental factors.
  • To demonstrate the utility of generative models in addressing domain-specific challenges in bioinformatics.

Main Methods:

  • Proposed a conditional latent diffusion model (cLDM) that explicitly conditions on batch-specific variables during image generation.
  • Utilized cLDM to disentangle technical batch effects from morphological features in zebrafish images.
  • Applied the model to classify genotypes from morphological images of individual zebrafish.

Main Results:

  • The cLDM successfully separated technical artifacts from biologically relevant morphological data.
  • Accurate classification of zebrafish genotypes was achieved using the model.
  • Demonstrated efficient batch effect correction and the model's versatility in addressing domain-specific problems.

Conclusions:

  • Conditional latent diffusion models offer a powerful solution for overcoming batch effects in biological imaging.
  • This approach enables more accurate genotype-phenotype correlation by isolating true biological signals.
  • The cLDM framework has broad potential for extracting meaningful biological insights from complex, high-throughput datasets.