  Imaging mass spectrometry (IMS) has transformed proteomics by providing an
avenue for collecting spatially distributed molecular data. Mass spectrometry
data acquired with matrix assisted laser desorption ionization (MALDI) IMS
consist of tens of thousands of spectra, measured at regular grid points across
the surface of a tissue section. Unlike the more standard liquid chromatography
mass spectrometry, MALDI-IMS preserves the spatial information inherent in the
tissue. Motivated by the need to differentiate cell populations and tissue
types in MALDI-IMS data accurately and efficiently, we propose an integrated
cluster and feature extraction approach for such data. We work with the derived
binary data representing presence/absence of ions, as this is the essential
information in the data. Our approach takes advantage of the spatial structure
of the data in a noise removal and initial dimension reduction step and applies
$k$-means clustering with the cosine distance to the high-dimensional binary
data. The combined smoothing-clustering yields spatially localized clusters
that clearly show the correspondence with cancer and various noncancerous
tissue types. Feature extraction of the high-dimensional binary data is
accomplished with our difference in proportions of occurrence (DIPPS) approach
which ranks the variables and selects a set of variables in a data-driven
manner. We summarize the best variables in a single image that has a natural
interpretation. Application of our method to data from patients with ovarian
cancer shows good separation of tissue types and close agreement of our results
with tissue types identified by pathologists.
