Peak learning of mass spectrometry imaging data using artificial neural networks
<jats:title>Abstract</jats:title><jats:p>Mass 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 d...
| Main Authors: | , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2023
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| Online Access: | https://hdl.handle.net/1721.1/147946 |
| _version_ | 1810974445758578688 |
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| author | 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 |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering 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 |
| author_sort | Abdelmoula, Walid M |
| collection | MIT |
| description | <jats:title>Abstract</jats:title><jats:p>Mass 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.</jats:p> |
| first_indexed | 2024-09-23T08:23:36Z |
| format | Article |
| id | mit-1721.1/147946 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:23:36Z |
| publishDate | 2023 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1479462023-02-08T03:48:38Z Peak learning of mass spectrometry imaging data using artificial neural networks 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 Massachusetts Institute of Technology. Department of Biological Engineering <jats:title>Abstract</jats:title><jats:p>Mass 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.</jats:p> 2023-02-07T18:56:30Z 2023-02-07T18:56:30Z 2021 2023-02-07T18:53:39Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/147946 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). en 10.1038/S41467-021-25744-8 Nature Communications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | 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 Peak learning of mass spectrometry imaging data using artificial neural networks |
| title | Peak learning of mass spectrometry imaging data using artificial neural networks |
| title_full | Peak learning of mass spectrometry imaging data using artificial neural networks |
| title_fullStr | Peak learning of mass spectrometry imaging data using artificial neural networks |
| title_full_unstemmed | Peak learning of mass spectrometry imaging data using artificial neural networks |
| title_short | Peak learning of mass spectrometry imaging data using artificial neural networks |
| title_sort | peak learning of mass spectrometry imaging data using artificial neural networks |
| url | https://hdl.handle.net/1721.1/147946 |
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