OffsampleAI: artificial intelligence approach to recognize off-sample mass spectrometry images View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-04-03

AUTHORS

Katja Ovchinnikova, Vitaly Kovalev, Lachlan Stuart, Theodore Alexandrov

ABSTRACT

BackgroundImaging mass spectrometry (imaging MS) is an enabling technology for spatial metabolomics of tissue sections with rapidly growing areas of applications in biology and medicine. However, imaging MS data is polluted with off-sample ions caused by sample preparation, particularly by the MALDI (matrix-assisted laser desorption/ionization) matrix application. Off-sample ion images confound and hinder statistical analysis, metabolite identification and downstream analysis with no automated solutions available.ResultsWe developed an artificial intelligence approach to recognize off-sample ion images. First, we created a high-quality gold standard of 23,238 expert-tagged ion images from 87 public datasets from the METASPACE knowledge base. Next, we developed several machine and deep learning methods for recognizing off-sample ion images. The following methods were able to reproduce expert judgements with a high agreement: residual deep learning (F1-score 0.97), semi-automated spatio-molecular biclustering (F1-score 0.96), and molecular co-localization (F1-score 0.90). In a test-case study, we investigated off-sample images corresponding to the most common MALDI matrix (2,5-dihydroxybenzoic acid, DHB) and characterized properties of matrix clusters.ConclusionsOverall, our work illustrates how artificial intelligence approaches enabled by open-access data, web technologies, and machine and deep learning open novel avenues to address long-standing challenges in imaging MS. More... »

PAGES

129

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12859-020-3425-x

DOI

http://dx.doi.org/10.1186/s12859-020-3425-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1126125601

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32245392


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