Spatial Autocorrelation in Mass Spectrometry Imaging

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.