High speed data processing for imaging MS-based molecular histology using graphical processing units.
Imaging MS enables the distributions of hundreds of biomolecular ions to be determined directly from tissue samples. The application of multivariate methods, to identify pixels possessing correlated MS profiles, is referred to as molecular histology as tissues can be annotated on the basis of the MS profiles. The application of imaging MS-based molecular histology to larger tissue series, for clinical applications, requires significantly increased computational capacity in order to efficiently analyze the very large, highly dimensional datasets. Such datasets are highly suited to processing using graphical processor units, a very cost-effective solution for high speed processing. Here we demonstrate up to 13× speed improvements for imaging MS-based molecular histology using off-the-shelf components, and demonstrate equivalence with CPU based calculations. It is then discussed how imaging MS investigations may be designed to fully exploit the high speed of graphical processor units.
Biomolecular Mass Spectrometry Unit, Department of Parasitology, Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
Emrys A
EA
Jones
René J M
RJ
van Zeijl
Per E
PE
Andrén
André M
AM
Deelder
Lex
L
Wolters
Liam A
LA
McDonnell
D000818:Animals; D001923:Brain Chemistry; D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D006651:Histocytochemistry / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D013058:Mass Spectrometry / Q000379:methods*; D051379:Mice; D057054:Molecular Imaging / Q000379:methods*; D025341:Principal Component Analysis