1. Cell Biology
  2. Computational and Systems Biology
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Decoupling global biases and local interactions between cell biological variables

  1. Assaf Zaritsky
  2. Uri Obolski
  3. Zhuo Gan
  4. Carlos R Reis
  5. Zuzana Kadlecova
  6. Yi Du
  7. Sandra L Schmid
  8. Gaudenz Danuser  Is a corresponding author
  1. UT Southwestern Medical Center, United States
  2. University of Oxford, United Kingdom
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Cite this article as: eLife 2017;6:e22323 doi: 10.7554/eLife.22323

Abstract

Analysis of coupled variables is a core concept of cell biological inference, with co-localization of two molecules as a proxy for protein interaction being a ubiquitous example. However, external effectors may influence the observed co-localization independently from the local interaction of two proteins. Such global bias, although biologically meaningful, is often neglected when interpreting co-localization. Here, we describe DeBias, a computational method to quantify and decouple global bias from local interactions between variables by modeling the observed co-localization as the cumulative contribution of a global and a local component. We showcase four applications of DeBias in different areas of cell biology, and demonstrate that the global bias encapsulates fundamental mechanistic insight into cellular behavior. The DeBias software package is freely accessible online via a web-server at https://debias.biohpc.swmed.edu.

Article and author information

Author details

  1. Assaf Zaritsky

    Department of Cell Biology, UT Southwestern Medical Center, Dallas, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1477-5478
  2. Uri Obolski

    Department of Zoology, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Zhuo Gan

    Department of Cell Biology, UT Southwestern Medical Center, Dallas, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Carlos R Reis

    Department of Cell Biology, UT Southwestern Medical Center, Dallas, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Zuzana Kadlecova

    Department of Cell Biology, UT Southwestern Medical Center, Dallas, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Yi Du

    Department of Bioinformatics, UT Southwestern Medical Center, Dallas, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Sandra L Schmid

    Department of Cell Biology, UT Southwestern Medical Center, Dallas, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1690-7024
  8. Gaudenz Danuser

    Department of Cell Biology, UT Southwestern Medical Center, Dallas, United States
    For correspondence
    gaudenz.Danuser@utsouthwestern.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8583-2014

Funding

Cancer Prevention and Research Institute of Texas (R1225)

  • Gaudenz Danuser

National Institutes of Health (P01 GM103723)

  • Gaudenz Danuser

National Institutes of Health (PO1 GM713165)

  • Sandra L Schmid
  • Gaudenz Danuser

EMBO (postdoctoral fellowship)

  • Uri Obolski

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Fabrice Cordelières, CNRS, France

Publication history

  1. Received: October 13, 2016
  2. Accepted: March 10, 2017
  3. Accepted Manuscript published: March 13, 2017 (version 1)
  4. Version of Record published: May 2, 2017 (version 2)

Copyright

© 2017, Zaritsky et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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