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Decoupling global biases and local interactions between cell biological variables

View ORCID ProfileAssaf Zaritsky, Uri Obolski, Zhuo Gan, Carlos R. Reis, View ORCID ProfileZuzana Kadlecova, Yi Du, View ORCID ProfileSandra L. Schmid, View ORCID ProfileGaudenz Danuser
doi: https://doi.org/10.1101/038059
Assaf Zaritsky
1Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
2Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA.
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  • ORCID record for Assaf Zaritsky
Uri Obolski
3Department of Zoology, University of Oxford, Oxford, United Kingdom.
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Zhuo Gan
1Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
2Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA.
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Carlos R. Reis
1Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
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Zuzana Kadlecova
1Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
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Yi Du
2Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA.
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Sandra L. Schmid
1Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
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Gaudenz Danuser
1Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
2Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA.
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  • For correspondence: gaudenz.danuser@utsouthwestern.edu
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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.

Impact statement: DeBias, a generic method to decompose and quantify the confounding, global factors and direct interactions of pairwise interacting variables.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 14, 2017.
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Decoupling global biases and local interactions between cell biological variables
Assaf Zaritsky, Uri Obolski, Zhuo Gan, Carlos R. Reis, Zuzana Kadlecova, Yi Du, Sandra L. Schmid, Gaudenz Danuser
bioRxiv 038059; doi: https://doi.org/10.1101/038059
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Decoupling global biases and local interactions between cell biological variables
Assaf Zaritsky, Uri Obolski, Zhuo Gan, Carlos R. Reis, Zuzana Kadlecova, Yi Du, Sandra L. Schmid, Gaudenz Danuser
bioRxiv 038059; doi: https://doi.org/10.1101/038059

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