Self-supervised clustering of mass spectrometry imaging data using contrastive learning.

ABSTRACT: Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. One of key challenges in molecular colocalization is that complex MSI data are too large for manual annotation but too small for training deep neural networks. Herein, we introduce a self-supervised clustering approach based on contrastive learning, which shows an excellent performance in clustering of MSI data. We train a deep convolutional neural network (CNN) using MSI data from a single experiment without manual annotations to effectively learn high-level spatial features from ion images and classify them based on molecular colocalizations. We demonstrate that contrast

SUBMITTER: Hu H 

PROVIDER: S-EPMC8694357 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature