Summary of Study ST002045
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001292. The data can be accessed directly via it's Project DOI: 10.21228/M8Q70T This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
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.
Study ID | ST002045 |
Study Title | massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation |
Study Summary | The patient-derived xenograft (PDX) mouse brain tumor model of glioblastoma (GBM) samples were analyzed by 2D MALDI FT ICR MSI. |
Institute | Brigham and Women's Hospital |
Department | Department of Neurosurgery |
Laboratory | Nathalie Y.R. Agar |
Last Name | Abdelmoula |
First Name | Walid |
Address | 60 Fenwood RD, Boston, MA |
wahassan@bwh.harvard.edu | |
Phone | 8572149765 |
Submit Date | 2021-12-06 |
Raw Data Available | Yes |
Raw Data File Type(s) | h5 |
Analysis Type Detail | MALDI-MS |
Release Date | 2022-01-04 |
Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
Project ID: | PR001292 |
Project DOI: | doi: 10.21228/M8Q70T |
Project Title: | massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation |
Project Type: | integrated processing and classification of 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 2021.05.06.442938). The below is taken from the abstract: Motivation: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high dimensionality, and spectral non-linearity. Preprocessing, including peak picking, has been used to reduce raw data complexity, however peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. Results: We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity, and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model’s performance was assessed using cross-validation, and the results demonstrate higher accuracy and a 174-fold gain in speed compared to the established classical machine learning method, support vector machine. |
Institute: | Brigham and Women's Hospital |
Department: | Department of Neurosurgery |
Laboratory: | Nathalie Y.R. Agar |
Last Name: | Abdelmoula |
First Name: | Walid |
Address: | 60 Fenwood RD, Boston, MA |
Email: | wahassan@bwh.harvard.edu |
Phone: | 8572149765 |
Subject:
Subject ID: | SU002127 |
Subject Type: | Mammal |
Subject Species: | Mus musculus |
Taxonomy ID: | 10090 |
Factors:
Subject type: Mammal; Subject species: Mus musculus (Factor headings shown in green)
mb_sample_id | local_sample_id | sample_id |
---|---|---|
SA192329 | 1PDX GBM - mouse brain tumor section | Dataset1 |
SA192330 | 2PDX GBM - mouse brain tumor section | Dataset2 |
SA192331 | 3PDX GBM - mouse brain tumor section | Dataset3 |
SA192332 | 4PDX GBM - mouse brain tumor section | Dataset4 |
SA192333 | 5PDX GBM - mouse brain tumor section | Dataset5 |
SA192334 | 6PDX GBM - mouse brain tumor section | Dataset6 |
SA192335 | 7PDX GBM - mouse brain tumor section | Dataset7 |
SA192336 | 8PDX GBM - mouse brain tumor section | Dataset8 |
Showing results 1 to 8 of 8 |
Collection:
Collection ID: | CO002120 |
Collection Summary: | As stated in the massNetpaper: Briefly, 8 GBM tissue sections of 12 μm thickness were prepared and analyzed using a 9.4 Tesla SolariX mass spectrometer (Bruker Daltonics, Billerica, MA) in the positive ion mode with spatial resolution of 100 μm. The MSI data was exported from SCiLS lab 2020a (Bruker, Bremen, Germany) in the standardized format imzML (Race et al., 2012) and converted to the HDF5 format (Folk et al., 2011) for deep learning analysis. |
Sample Type: | PDX GBM - mouse brain tumor section |
Treatment:
Treatment ID: | TR002139 |
Treatment Summary: | N/A |
Sample Preparation:
Sampleprep ID: | SP002133 |
Sampleprep Summary: | As stated in the massNet paper Briefly, 8 GBM tissue sections of 12 μm thickness were prepared and analyzed using a 9.4 Tesla SolariX mass spectrometer (Bruker Daltonics, Billerica, MA) in the positive ion mode with spatial resolution of 100 μm. The MSI data was exported from SCiLS lab 2020a (Bruker, Bremen, Germany) in the standardized format imzML (Race et al., 2012) and converted to the HDF5 format (Folk et al., 2011) for deep learning analysis. |
Combined analysis:
Analysis ID | AN003329 |
---|---|
Analysis type | MS |
Chromatography type | None (Direct infusion) |
Chromatography system | none |
Column | none |
MS Type | MALDI |
MS instrument type | FT-ICR |
MS instrument name | Bruker Solarix FT-ICR-MS |
Ion Mode | POSITIVE |
Units | Da |
Chromatography:
Chromatography ID: | CH002466 |
Instrument Name: | none |
Column Name: | none |
Chromatography Type: | None (Direct infusion) |
MS:
MS ID: | MS003099 |
Analysis ID: | AN003329 |
Instrument Name: | Bruker Solarix FT-ICR-MS |
Instrument Type: | FT-ICR |
MS Type: | MALDI |
MS Comments: | Bruker software |
Ion Mode: | POSITIVE |