TY - JOUR T1 - Co-localization features for classification of tumors using mass spectrometry imaging JF - bioRxiv DO - 10.1101/440057 SP - 440057 AU - Paolo Inglese AU - Gonçalo Correia AU - Pamela Pruski AU - Robert C Glen AU - Zoltan Takats Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/10/11/440057.abstract N2 - 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. ER -