A quick and effective multivariate statistical strategy for imaging mass spectrometry

A new multivariate statistical strategy for analyzing large datasets that are produced by imaging mass spectrometry (IMS) techniques is reported. The strategy divides the whole datacube of the sample into several subsets and analyses them one by one to obtain the results. Instead of analyzing the whole datacube at one time, the strategy makes the analysis easier and decreases the computation time greatly. In this report, the IMS data are produced by the air flow-assisted ionization IMS (AFAI-IMS). The strategy can be used in combination with most multivariate statistical analysis methods. In this paper, the strategy was combined with the principal component analysis (PCA) and partial least square analysis (PLS). It was proven to be effective by analyzing the handwriting sample. By using the strategy, the m/z corresponding to the specific lipids in rat brain tissue were distinguished successfully. Moreover the analysis time grew linearly instead of exponentially as the size of sample increased. The strategy developed in this study has enormous potential for searching for the m/z of potential biomarkers quickly and effectively.

It was the original data to be analyzed by the multivariate statistical strategy. The (X, Y) was the length and the width of the sample. A subset of the datacube was a dataset which had same X or Y.Figure optionsDownload as PowerPoint slide