Abstract
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.
Date of this Version
12-14-2012
DOI
10.1186/1471-2105-13-S18-A8
Recommended Citation
Bemis, Kyle D.; Eberlin, Livia; Ferreira, Christina R.; Cooks, Robert Graham; and Vitek, Olga, "Spatial Segmentation and Feature Selection for Desi Imaging Mass Spectrometry Data with Spatially-Aware Sparse Clustering." (2012). Department of Chemistry Faculty Publications. Paper 3.
http://dx.doi.org/10.1186/1471-2105-13-S18-A8
Comments
This is the publisher pdf of Kyle D Bemis, Livia Eberlin, Christina Ferreira, R Cooks, Olga Vitek. Spatial Segmentation and Feature Selection for Desi Imaging Mass Spectrometry Data with Spatially-Aware Sparse Clustering. BMC Bioinformatics 2012, 13(Suppl 18):A8 (14 December 2012) and is available at: 10.1186/1471-2105-13-S18-A8.