Extended data analysis strategies for high resolution imaging MS: New methods to deal with extremely large image hyperspectral datasets

The large size of the hyperspectral datasets that are produced with modern mass spectrometric imaging techniques makes it difficult to analyze he results. Unsupervised statistical techniques are needed to extract relevant information from these datasets and reduce the data into a surveyable verview. Multivariate statistics are commonly used for this purpose. Computational power and computer memory limit the resolution at which the atasets can be analyzed with these techniques. We introduce the use of a data format capable of efficiently storing sparse datasets for multivariate nalysis. This format is more memory-efficient and therefore it increases the possible resolution together with a decrease of computation time. hree multivariate techniques are compared for both sparse-type data and non-sparse data acquired in two different imaging ToF-SIMS experiments nd one LDI-ToF imaging experiment. There is no significant qualitative difference in the use of different data formats for the same multivariate lgorithms. All evaluated multivariate techniques could be applied on both SIMS and the LDI imaging datasets. Principal component analysis is hown to be the fastest choice; however a small increase of computation time using a VARIMAX optimization increases the decomposition quality ignificantly. PARAFAC analysis is shown to be very effective in separating different chemical components but the calculations take a significant mount of time, limiting its use as a routine technique. An effective visualization of the results of the multivariate analysis is as important for the nalyst as the computational issues. For this reason, a new technique for visualization is presented, combining both spectral loadings and spatial cores into one three-dimensional view on the complete datacube. 2006 Elsevier B.V. All rights reserved.