Summary of study ST001857

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR001171. The data can be accessed directly via it's Project DOI: 10.21228/M8BM4Q This work is supported by NIH grant, U2C- DK119886.


This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST001857
Study TitlePeak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks (Prostate tissue)
Study SummaryThe human prostate tissue sample was analyzed by 2D MALDI FT ICR MSI. For detailed information we refer to the msiPL manuscript by Abdelmoula et al.
Brigham and Women’s Hospital
Last NameAbdelmoula
First NameWalid
Address60 Fenwood RD, Boston, MA
Submit Date2021-06-20
Raw Data AvailableYes
Raw Data File Type(s)h5
Analysis Type DetailMALDI
Release Date2021-07-18
Release Version1
Walid Abdelmoula Walid Abdelmoula application/zip

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Project ID:PR001171
Project DOI:doi: 10.21228/M8BM4Q
Project Title:msiPL: Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks
Project Type:Deep Learning for MSI data analysis
Project Summary:The attached MSI datasets of GBM and prostate cancer tissues were analyzed in the manuscript by Abdelmoula et al. (bioRxiv 2020.08.13.250142). The below is taken from the abstract: Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving clinical diagnosis, biomarker discovery, metabolomics research and pharmaceutical applications. The large data size and high dimensional nature of MSI pose computational and memory complexities that hinder accurate identification of biologically-relevant molecular patterns. We propose msiPL, a robust and generic probabilistic generative model based on a fully-connected variational autoencoder for unsupervised analysis and peak learning of MSI data. The method can efficiently learn and visualize the underlying non-linear spectral manifold, reveal biologically-relevant clusters of tumor heterogeneity and identify underlying informative m/z peaks. The method provides a probabilistic parametric mapping to allow a trained model to rapidly analyze a new unseen MSI dataset in a few seconds. The computational model features a memory-efficient implementation using a minibatch processing strategy to enable the analyses of big MSI data (encompassing more than 1 million high-dimensional datapoints) with significantly less memory. We demonstrate the robustness and generic applicability of the application on MSI data of large size from different biological systems and acquired using different mass spectrometers at different centers, namely: 2D Matrix-Assisted Laser Desorption Ionization (MALDI) Fourier Transform Ion Cyclotron Resonance (FT ICR) MSI data of human prostate cancer, 3D MALDI Time-of-Flight (TOF) MSI data of human oral squamous cell carcinoma, 3D Desorption Electrospray Ionization (DESI) Orbitrap MSI data of human colorectal adenocarcinoma, 3D MALDI TOF MSI data of mouse kidney, and 3D MALDI FT ICR MSI data of a patient-derived xenograft (PDX) mouse brain model of glioblastoma.
Institute:Brigham and Women's Hospital
Laboratory:Surgical Molecular Imaging Laboratory
Last Name:Abdelmoula
First Name:Walid
Address:60 Fenwood RD, Boston, Massachusetts, 02115, USA