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Quantifying Intermembrane Distances with Serial Image Dilations
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Correcting nuisance variation using Wasserstein distance.

Gil Tabak1, Minjie Fan1, Samuel Yang1

  • 1Google, Mountain View, CA, USA.

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|March 13, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to remove unwanted batch effects from cell imaging data, improving the accuracy of drug discovery analysis. The adjusted embeddings better capture biological signals while discarding domain-specific variations.

Keywords:
Batch effectCellular phenotypingDomain adaptationEmbeddingMinimaxOptimal transportWasserstein distance

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Image Analysis

Background:

  • Cellular phenotype profiling from microscopic imaging offers biological insights, crucial for applications like drug development.
  • Quantifying drug compound similarities relies on morphological cell features from images, but separating biological signals from nuisance variations (e.g., batch effects) is challenging.
  • Existing methods often embed image data into a lower-dimensional space, but domain-specific information can obscure relevant biological signals.

Purpose of the Study:

  • To develop a general framework for adjusting image embeddings to remove domain-specific information while preserving relevant biological signals.
  • To enhance the utility of cell imaging data for applications such as drug discovery by improving signal-to-noise ratio.
  • To create image embeddings that accurately reflect biological effects, independent of experimental batch variations.

Main Methods:

  • A novel framework was developed to adjust image embeddings, aiming to 'forget' domain-specific information.
  • The adjustment process involved minimizing a loss function based on distances between marginal distributions (e.g., Wasserstein distance) of embeddings across different experimental domains.
  • The method was applied to cell imaging data, focusing on replicated treatments, including a negative control.

Main Results:

  • Transformed embeddings demonstrated preserved underlying geometric structure.
  • The adjusted embeddings exhibited an improved biological signal, more accurately reflecting treatment effects.
  • A significant reduction in domain-specific information was observed in the transformed embeddings.

Conclusions:

  • The developed framework effectively removes nuisance variations, such as batch effects, from cell imaging data.
  • The adjusted embeddings enhance the biological interpretability of image data, particularly for drug discovery.
  • This approach offers a robust method for extracting meaningful biological insights from high-throughput microscopy.
  • Improved embeddings facilitate more accurate quantification of drug compound similarities and effects.