Title: "Efficient Processing and Spatial Segmentation with Automated Feature Selection for DESI Imaging Mass Spectrometry"
Speaker: Kyle Bemis, Department of Statistics, Purdue University

Place: LILY G126
Date: October 30, 2012; Tuesday


Recent advances in matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) have demonstrated the usefulness of these technologies in molecular imaging of biological samples. However, development of computational methods for the statistical interpretation and analysis of imaging mass spectrometry (IMS) data remains a challenge. We propose statistically-minded computational methods for analyzing DESI imaging experiments. Specifically, we present techniques for signal processing and unsupervised multivariate image segmentation, which are also applicable to other 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. 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.

To segment the sample into sub-regions of homogenous chemical composition in MALDI images, Alexandrov & Kobarg (2011) proposed two spatially-aware clustering techniques. We demonstrate these approaches are also useful for DESI. Moreover, we extend one of these clustering methods using statistical regularization, enabling simultaneous feature selection of structurally-important peaks and facilitating interpretation.

We evaluate the performance of the proposed methods in both a biological and non-biological example, and show that statistical regularization improves accuracy and interpretation of spatial segmentation over existing approaches.

** In collaboration with Livia Eberlin (2), Christina Ferreira (2), R. Graham Cooks(2), and Olga Vitek (1,3)
(1) Department of Statistics, Purdue University, West Lafayette, IN, USA;(2) Department of Chemistry, Purdue University, West Lafayette, IN, USA; (3) Department of Computer Science, Purdue University, West Lafayette, IN, USA

Associated Reading:
Alexandrov, T., and Kobarg, J. H. 2011. Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering. Bioinformatics, 27(13), i230i238. doi:10.1093/bioinformatics/btr246

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