Abstract
Due to the constantly increasing cancer incidence rates and varying levels of effectiveness of the utilised therapeutic approaches, obtaining a clear understanding of the underlying phenomena is of the utmost importance. The problem is tackled by numerous research groups worldwide, utilising a number of molecular biology quantification techniques. MALDI-IMS (Matrix-Assisted Laser Desorption Ionization – Image Mass Spectrometry) is a quantification technique that brings together MALDI spectroscopy with tissue imaging by multiple applications of the laser beam to a raster of points on the surface of the analysed tissue. The application of MALDI-IMS in cancer research allows for the spatial identification of molecular profiles and their heterogeneity within the tumour, but leads to the creation of highly complicated datasets of great volume. Extraction of relevant information from such datasets relies on the design of appropriate algorithms and using them as the base to construct efficient data mining tools. Existing computational tools for MALDI-IMS exhibit numerous shortcomings and limited utility and cannot be used for fully automated discovery of heterogeneity in tumour samples. We developed a novel signal analysis pipeline including signal pre-processing, spectrum modelling and intelligent spectra clustering with region-driven feature selection to efficiently analyse that data. The idea of combining divisive iK-means algorithm with peptide abundance variance based dimension reduction performed independently for each analysed sub-region allowed for discovery of squamous cell carcinoma and keratinized stratified squamous epithelium together with stratified squamous epithelium within an exemplary head and neck tumour tissue.
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Acknowledgement
The work was partially financed by NCN grant no. DEC2013/08/M/ST6/924. The GeCONiI IT infrastructure (grant on POIG 02.03.01-24-099) “Upper Silesian Centre for Computational Science and Engineering” was used for performing calculations and numerical simulations.
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© 2016 Springer International Publishing Switzerland
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Mrukwa, G., Drazek, G., Pietrowska, M., Widlak, P., Polanska, J. (2016). A Novel Divisive iK-Means Algorithm with Region-Driven Feature Selection as a Tool for Automated Detection of Tumour Heterogeneity in MALDI IMS Experiments. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_11
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DOI: https://doi.org/10.1007/978-3-319-31744-1_11
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