Spatially aware clustering of ion images in mass spectrometry imaging data using deep learning.

ABSTRACT: Computational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction. The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coined neural ion images, is used to cluster ion images based on spatial expressions. We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering,

SUBMITTER: Zhang W 

PROVIDER: S-EPMC8007517 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature