Peak learning of mass spectrometry imaging data using artificial neural networks
Name
s41467-021-25744-8.pdf
Description
Published version
Size
5.04 MB
Format
Adobe PDF
Checksum (MD5)
a70deb8e822faa821414cfe1f6e650ee
Author(s)
Abdelmoula, Walid M
Lopez, Begona Gimenez-Cassina
Randall, Elizabeth C
Kapur, Tina
Sarkaria, Jann N
White, Forest M
Agar, Jeffrey N
Wells, William M
Agar, Nathalie YR
Date Issued
2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Abdelmoula, Walid M, Lopez, Begona Gimenez-Cassina, Randall, Elizabeth C, Kapur, Tina, Sarkaria, Jann N et al. 2021. "Peak learning of mass spectrometry imaging data using artificial neural networks." Nature Communications, 12 (1).
Version
Final published version
Abstract
AbstractMass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41467-021-25744-8
