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Deep generative modeling of sample-level heterogeneity in single-cell genomics.

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Multi-resolution variational inference (MrVI) enables deeper insights from single-cell genomic data. This deep generative model identifies sample stratifications and molecular differences without predefined cell states, revealing novel biological discoveries.

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Single-cell genomic studies generate complex data with potential for linking phenotypes to cellular composition.
  • Current analyses often simplify data by averaging across cells, limiting discovery.
  • There is a need for advanced analytical methods to fully leverage single-cell cohort data.

Purpose of the Study:

  • To introduce multi-resolution variational inference (MrVI), a deep generative model for single-cell genomic data analysis.
  • To address the challenges of sample stratification and evaluation of cellular/molecular differences without predefined cell states.
  • To enable novel discoveries from large-scale single-cell studies.

Main Methods:

  • Development of multi-resolution variational inference (MrVI), a deep generative model.
  • Application of MrVI to analyze complex single-cell genomic datasets from cohort studies.
  • Utilizing a single-cell perspective to avoid data averaging and capture finer biological signals.

Main Results:

  • MrVI successfully stratified cohorts with COVID-19 and inflammatory bowel disease based on subtle cellular subset differences.
  • The model identified clinically relevant stratifications that would be missed by traditional methods.
  • MrVI demonstrated the ability to de novo identify groups of small molecules and assess their impact on cellular composition and gene expression.

Conclusions:

  • MrVI unlocks the potential of large-scale single-cell genomic studies by providing a powerful tool for sample stratification and differential analysis.
  • The model facilitates the discovery of previously overlooked biological insights, particularly in complex diseases.
  • MrVI is an open-source tool available for broader scientific application.