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Measuring co-localization of ion images

This repository is devoted to a project on measuring co-localization of mass spectrometry images. The project is carried out by the Alexandrov team at EMBL Heidelberg. We created a webapp for ranking pairs of ion images, engaged external experts to rank images from their public data from METASPACE, consolidated the results into a gold standard set of ranked pairs of ion images, and, finally, developed and evaluated various measures of co-localization.

For more information, please see our recent paper Ovchinnikova et al. (2020) Bioinformatics.

Team:

  • Katja Ovchinnikova: pixel-based co-localization method development, gold standard preparation
  • Alexander Rakhlin: deep learning based co-localization method development
  • Lachlan Stuart: development and implementation of the RankColoc web app
  • Sergey Nikolenko: PI for the deep learning work
  • Theodore Alexandrov: supervision, gold standard preparation

Creating gold standard ion images

Using public METASPACE datasets

We used public datasets from METASPACE, a community-populated knowledge base of metabolite images. Please see the section Acknowledgements acknowledging contributors of the used data.

RankColoc was rapidly prototyped using the METASPACE codebase as a foundation, allowing its back-end, image display and ranking to be reused. The RankColoc-specific changes can be found in this commit range.

Data

Gold standard

The gold standard is available here. The ion images are available under gs_imgs1 and gs_imgs2 file names. To join both files into one arhive run cat gs_imgs* > gs_imgs.tar.gz

The initial expert rankings can be found in rankings.csv, the filtered gold standard with average rankings is in coloc_gs.csv.

Colocalization measures

Measures requiring no learning

Measures requiring no learning are available in the jupyter notebook ion_intensity_coloc_measures.ipynb here.

Measures based on deep learning

Measures based on deep learning are available here.

Future steps

We are planning to integrate the best methods into https://metaspace2020.eu.

License

Unless specified otherwise in file headers or LICENSE files present in subdirectories, all files in this repository are licensed under the Apache 2.0 license.