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ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images

Katja Ovchinnikova, View ORCID ProfileAlexander Rakhlin, Lachlan Stuart, View ORCID ProfileSergey Nikolenko, View ORCID ProfileTheodore Alexandrov
doi: https://doi.org/10.1101/758425
Katja Ovchinnikova
1Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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Alexander Rakhlin
2Neuromation OU, Tallinn, Estonia
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Lachlan Stuart
1Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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Sergey Nikolenko
3National Research Institute Higher School of Economics, St. Petersburg, Russia
4Steklov Institute of Mathematics at St.Petersburg, St. Petersburg, Russia
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Theodore Alexandrov
1Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
5Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
6Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
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  • For correspondence: theodore.alexandrov@embl.de
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Abstract

Motivation Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development.

Results We present ColocAI, an artificial intelligence approach addressing this gap. With the help of 42 imaging MS experts from 9 labs, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using tf-idf and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings respectively). We illustrate these measures by inferring co-localization properties of 10273 molecules from 3685 public METASPACE datasets.

Availability and Implementation https://github.com/metaspace2020/coloc

Contact theodore.alexandrov{at}embl.de

Footnotes

  • https://github.com/metaspace2020/coloc

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 September 08, 2019.
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ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images
Katja Ovchinnikova, Alexander Rakhlin, Lachlan Stuart, Sergey Nikolenko, Theodore Alexandrov
bioRxiv 758425; doi: https://doi.org/10.1101/758425
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ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images
Katja Ovchinnikova, Alexander Rakhlin, Lachlan Stuart, Sergey Nikolenko, Theodore Alexandrov
bioRxiv 758425; doi: https://doi.org/10.1101/758425

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