Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images are created through a grid-wise interrogation of individual spots by mass spectrometry across a surface. Classical statistical tests for within-sample comparisons fail as close-by measurement spots violate the assumption of independence of these tests, which can lead to an increased false-discovery rate. For spatial data, this effect is referred to as spatial autocorrelation. In this study, we investigated spatial autocorrelation in three different matrix-assisted laser desorption/ionization MSI data sets. These data sets cover different molecular classes (metabolites/drugs, lipids, and proteins) and different spatial resolutions ranging from 20 to 100 μm. Significant spatial autocorrelation was detected in all three data sets and found to increase with decreasing pixel size. To enable statistical testing for differences in mass signal intensities between regions of interest within MSI data sets, we propose the use of Conditional Autoregressive (CAR) models. We show that, by accounting for spatial autocorrelation, discovery rates (i.e., the ratio between the features identified and the total number of features) could be reduced between 21% and 69%. The reliability of this approach was validated by control mass signals based on prior knowledge. In light of the advent of larger MSI data sets based on either an increased spatial resolution or 3D data sets, accounting for effects due to spatial autocorrelation becomes even more indispensable. Here, we propose a generic and easily applicable workflow to enable within-sample statistical comparisons.
- CORTICAL SPREADING DEPRESSION
- MOUSE MODEL