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Nested effects models for high-dimensional phenotyping screens.

Florian Markowetz1, Dennis Kostka, Olga G Troyanskaya

  • 1Lewis-Sigler Institute for Integrative Genomics and Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA.

Bioinformatics (Oxford, England)
|July 25, 2007
PubMed
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We developed probabilistic methods to infer genetic hierarchies from high-dimensional phenotyping screens. These methods reveal gene clusters and their relationships, aiding in understanding cellular signaling and regulatory networks.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • High-dimensional phenotyping screens generate vast cellular data after gene perturbations (knockouts/RNA interference).
  • Analyzing these perturbation effects is key to gene function attribution.
  • Existing statistical methods are not fully adapted for large-scale, high-dimensional phenotyping data.

Purpose of the Study:

  • To introduce and compare probabilistic methods for inferring genetic hierarchies from high-dimensional phenotyping data.
  • To reveal gene clusters with similar phenotypic profiles.
  • To order genes based on subset relationships between their observed phenotypes.

Main Methods:

  • Probabilistic modeling to infer genetic hierarchies.
  • Analysis of nested structures in perturbation effects.

Related Experiment Videos

  • Clustering algorithms to group genes with similar phenotypic profiles.
  • Ordering algorithms to establish subset relationships between phenotypes.

    • Main Results:

    • The developed methods efficiently infer genetic hierarchies, elucidating signaling pathways and regulatory networks.
    • Identified clusters of genes with highly similar phenotypic profiles.
    • Successfully ordered gene clusters based on subset relationships of their phenotypes.
    • Validated methods through simulations and two experimental datasets (Drosophila melanogaster, Saccharomyces cerevisiae).

    Conclusions:

    • The probabilistic methods effectively identify biologically justified genetic hierarchies from perturbation effects.
    • These hierarchies provide insights into complex genetic interactions and cellular organization.
    • The freely available R package 'nem' facilitates the application of these methods.