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
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.
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.
Measures requiring no learning are available in the jupyter notebook ion_intensity_coloc_measures.ipynb here.
Measures based on deep learning are available here.
We are planning to integrate the best methods into https://metaspace2020.eu.
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.