- Meeting abstract
- Open Access
Spatial segmentation and feature selection for desi imaging mass spectrometry data with spatially-aware sparse clustering
© Bemis et al; licensee BioMed Central Ltd. 2012
- Published: 14 December 2012
- Imaging Mass Spectrometry
- Desorption Electrospray Ionization
- MALDI Image
- Spatial Segmentation
- Imaging Mass Spectrometry Data
Recent experimental advances in matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) have demonstrated the usefulness of these technologies in the molecular imaging of biological samples. However, development of computational methods for the statistical interpretation and analysis of the chemical differences present in the distinct regions of these samples is still a major challenge. In this poster, we propose statistically-minded methods and computational tools for analyzing DESI imaging experiments. Specifically, we present techniques for signal processing and unsupervised multivariate image segmentation, which are also applicable to other imaging mass spectrometry (IMS) methods such as MALDI.
Signal processing of DESI spectra typically involves binning to reduce dimensionality, but this inefficient for downstream analysis as it retains empty regions of the mass spectrum. In our proposed processing step, we apply a novel peak picking algorithm based on windowed smoothing splines that allows adaptive resolution based on spectral profile. With this approach, peaks are aligned using a recursive dynamic programming algorithm which accounts for the heterogenous nature of IMS data by making pairwise alignments between pixels based on their proximity. Peaks are then normalized using total ion count.
In order to segment the sample into sub-regions of homogenous chemical composition in MALDI images, Alexandrov & Kobarg  proposed two efficient spatially-aware clustering techniques. We demonstrate that these approaches are also useful for DESI. Moreover, we extend one of these clustering methods using statistical regularization techniques that enable simultaneous feature selection of structurally-important peaks and facilitate interpretation.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.