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Unsupervised learning for exploring MALDI imaging mass spectrometry 'omics' data

Wijetunge, C.D., Saeed, I., Halgamuge, S.K., Boughton, B.ORCID: 0000-0001-6342-9814 and Roessner, U. (2014) Unsupervised learning for exploring MALDI imaging mass spectrometry 'omics' data. In: 7th International Conference on Information and Automation for Sustainability, 22 - 24 December 2014, Colombo, Sri Lanka

Link to Published Version: https://doi.org/10.1109/ICIAFS.2014.7069634
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Abstract

Matrix Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) is an emerging data acquisition technology in biological research. It has gained its popularity in `omics' sciences because of its ability to explore the spatial distributions of various bio-molecules in detail. The sheer volume of data generated through this technology and the often limited a priori knowledge about the molecular compositions of biological samples, call for efficient data analysis methods. In this paper, first we review the available computational methods for analyzing the high-dimensional imaging datasets highlighting their advantages and limitations. Then, we propose a more recent unsupervised method as a means of exploring MALDI-IMS data and demonstrate its competency by extracting hidden significant spatial distribution patterns of a rat brain imaging dataset. Finally, we explain the potential future advances of `omics' research associated with MALDI-IMS and the foreseeable challenges in analyzing the resultant data.

Item Type: Conference Paper
URI: http://researchrepository.murdoch.edu.au/id/eprint/56516
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