Clusterwise Peak Detection and Filtering Based on Spatial Distribution To Efficiently Mine Mass Spectrometry Imaging Data

Mass
spectrometry imaging (MSI) has the potential to reveal the
localization of thousands of biomolecules such as metabolites and
lipids in tissue sections. The increase in both mass and spatial resolution
of today’s instruments brings on considerable challenges in
terms of data processing; accurately extracting meaningful signals
from the large data sets generated by MSI without losing information
that could be clinically relevant is one of the most fundamental tasks
of analysis software. Ion images of the biomolecules are generated
by visualizing their intensities in 2-D space using mass spectra collected
across the tissue section. The intensities are often calculated by
summing each compound’s signal between predefined sets of borders
(bins) in the m/z dimension. This
approach, however, can result in mixed signals from different compounds
in the same bin or splitting the signal from one compound between
two adjacent bins, leading to low quality ion images. To remedy this
problem, we propose a novel data processing approach. Our approach
consists of a sensitive peak detection method able to discover both
faint and localized signals by utilizing clusterwise kernel density
estimates (KDEs) of peak distributions. We show that our method can
recall more ground-truth molecules, molecule fragments, and isotopes
than existing methods based on binning. Furthermore, it automatically
detects previously reported molecular ions of lipids, including those
close in m/z, in an experimental
data set.