Data Processing for 3D Mass Spectrometry Imaging. Xiong, X., Xu, W., Eberlin, L. S., Wiseman, J. M., Fang, X., Jiang, Y., Huang, Z., Zhang, Y., Cooks, R. G., & Ouyang, Z. Journal of The American Society for Mass Spectrometry, 23(6):1147–1156, 2012. ![Data Processing for 3D Mass Spectrometry Imaging [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Data processing for three dimensional mass spectrometry (3D-MS) imaging was investigated, starting with a consideration of the challenges in its practical implementation using a series of sections of a tissue volume. The technical issues related to data reduction, 2D imaging data alignment, 3D visualization, and statistical data analysis were identified. Software solutions for these tasks were developed using functions in MATLAB. Peak detection and peak alignment were applied to reduce the data size, while retaining the mass accuracy. The main morphologic features of tissue sections were extracted using a classification method for data alignment. Data insertion was performed to construct a 3D data set with spectral information that can be used for generating 3D views and for data analysis. The imaging data previously obtained for a mouse brain using desorption electrospray ionization mass spectrometry (DESI-MS) imaging have been used to test and demonstrate the new methodology.
@Article{xiong12data,
author = {Xiong, Xingchuang and Xu, Wei and Eberlin, Livia S. and Wiseman, Justin M. and Fang, Xiang and Jiang, You and Huang, Zejian and Zhang, Yukui and Cooks, R. Graham and Ouyang, Zheng},
title = {Data Processing for 3D Mass Spectrometry Imaging},
journal = {Journal of The American Society for Mass Spectrometry},
year = {2012},
volume = {23},
number = {6},
pages = {1147--1156},
issn = {1879-1123},
abstract = {Data processing for three dimensional mass spectrometry (3D-MS) imaging was investigated, starting with a consideration of the challenges in its practical implementation using a series of sections of a tissue volume. The technical issues related to data reduction, 2D imaging data alignment, 3D visualization, and statistical data analysis were identified. Software solutions for these tasks were developed using functions in MATLAB. Peak detection and peak alignment were applied to reduce the data size, while retaining the mass accuracy. The main morphologic features of tissue sections were extracted using a classification method for data alignment. Data insertion was performed to construct a 3D data set with spectral information that can be used for generating 3D views and for data analysis. The imaging data previously obtained for a mouse brain using desorption electrospray ionization mass spectrometry (DESI-MS) imaging have been used to test and demonstrate the new methodology.},
doi = {10.1007/s13361-012-0361-7},
owner = {Purva},
timestamp = {2016-09-13},
url = {http://dx.doi.org/10.1007/s13361-012-0361-7},
}
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