Co-localization features for classification of tumors using mass spectrometry imaging

Inglese, P.; dos Santos Correia, G.; Pruski, P.; Glen, R. C.; Takats, Z.

doi:10.1101/440057

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bioRxiv · 10.1101/440057

Co-localization features for classification of tumors using mass spectrometry imaging

Inglese, P.·dos Santos Correia, G.·Pruski, P.·Glen, R. C.·Takats, Z.

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Abstract

Statistical modeling of mass spectrometry imaging (MSI) data is a crucial component for the understanding of the molecular characteristics of cancerous tissues. Quantification of the abundances of metabolites or batch effect between multiple spectral acquisitions represents only a few of the challenges associated with this type of data analysis. Here we introduce a method based on ion co-localization features that allows the classification of whole tissue specimens using MSI data, which overcomes the possible batch effect issues and generates data-driven hypotheses on the underlying mechanisms associated with the different classes of analyzed samples.

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Inglese, P., dos Santos Correia, G., Pruski, P., Glen, R. C., Takats, Z.. 2018-10-11. Co-localization features for classification of tumors using mass spectrometry imaging. https://doi.org/10.1101/440057

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