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|a 10.1109/TVCG.2017.2744479
|2 doi
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|a eng
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1 |
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|a Rubel, Oliver
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a BASTet
|b Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI
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|c 2018
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|a Text
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|a Date Completed 15.04.2019
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| 500 |
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|a Date Revised 15.04.2019
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation. However, the lack of means to share, apply, and reproduce analyses hinders the broad application, validation, and use of novel MSI analysis methods. To address this central challenge, we introduce the Berkeley Analysis and Storage Toolkit (BASTet), a novel framework for shareable and reproducible data analysis that supports standardized data and analysis interfaces, integrated data storage, data provenance, workflow management, and a broad set of integrated tools. Based on BASTet, we describe the extension of the OpenMSI mass spectrometry imaging science gateway to enable web-based sharing, reuse, analysis, and visualization of data analyses and derived data products. We demonstrate the application of BASTet and OpenMSI in practice to identify and compare characteristic substructures in the mouse brain based on their chemical composition measured via MSI
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Bowen, Benjamin P
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|t IEEE transactions on visualization and computer graphics
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|u http://dx.doi.org/10.1109/TVCG.2017.2744479
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