Multivariate Analysis of Imaging Mass Spectrometry Data

Author

Muir, E.R. ; Ndiour, I.J. ; Le Goasduff, N.A. ; Moffitt, R.A. ; Liu, Y. ; Sullards, M.C. ; Merrill, A.H., Jr. ; Chen, Y. ; Wang, M.D.

Author_Institution

Emory Univ., Atlanta

fYear

2007

fDate

14-17 Oct. 2007

Firstpage

472

Lastpage

479

Abstract

Imaging mass spectrometry can be used to reveal spatial distributions of multiple molecular species in a 2D biological sample. Due to the large amount of data produced by this technology, it is difficult and time-consuming to manually extract meaningful results from imaging mass spectrometry experimentation. We have developed and implemented an original approach to easily and consistently process mass spectrometry imaging data with the goal of automatically identifying interesting regions of molecule expression. Based on multivariate analysis techniques such as principal component analysis, the system allows researchers to conveniently define and visualize spatial regions based on spectral similarity. Features of our system are demonstrated on mouse cerebellum data.

Keywords

biomedical optical imaging; brain; laser applications in medicine; mass spectroscopy; medical image processing; molecular biophysics; principal component analysis; imaging mass spectrometry data; molecule expression; mouse cerebellum data; multivariate analysis; principal component analysis; spectral similarity; Biochemical analysis; Biology computing; Data processing; Data visualization; Image analysis; Linear discriminant analysis; Mass spectroscopy; Mice; Principal component analysis; Quality control; imaging mass spectrometry; multivariate analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Bioengineering, 2007. BIBE 2007. Proceedings of the 7th IEEE International Conference on

Conference_Location

Boston, MA

Print_ISBN

978-1-4244-1509-0

Type

conf

DOI

10.1109/BIBE.2007.4375603

Filename

4375603