Abstract
Hierarchical cluster analysis (HCA) is extensively used for the analysis of hyperspectral data. In this work, hyperspectral data sets obtained from Raman maps were analyzed using an alternative mode of cluster analysis, clustering “images” instead of spectra, under the assumption that images showing similar spatial distributions are related to the same chemical species. Such an approach was tested with two Raman maps: one simple “test map” of micro-crystals of four different compounds for a proof of principle and a map of a biological tissue (i.e., cartilage) as an example of chemically complex sample. In both cases, the “image-clustering” approach gave similar results as the traditional HCA, but at lower computational effort. The alternative approach proved to be particularly helpful in cases, as for the cartilage tissue, where concentration gradients of chemical composition are present. Moreover, with this approach, yielded information about correlation between bands in the average spectrum makes band assignment and spectral interpretation easier.
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References
Sasic S, Ozaki Y (2011) Raman, infrared, and near-infrared chemical imaging. Wiley, Hoboken
Salzer R, Siesler HW (2014) Infrared and Raman spectroscopic imaging. Wiley
Ghomi M (2012) Applications of Raman spectroscopy to biology: from basic studies to disease diagnosis. IOS Press
Diem M, Griffiths P, Chalmers JM (2008) Vibrational spectroscopy for medical diagnosis. Wiley
Lasch P, Kneipp J (2008) Biomedical vibrational spectroscopy. Wiley
Srinivasan G (2010) Vibrational spectroscopic imaging for biomedical applications. McGraw Hill Professional
Beleites C, Bonifacio A, Codrich D et al (2013) Raman spectroscopy and imaging: promising optical diagnostic tools in pediatrics. Curr Med Chem 20:2176–2187
Diem M, Mazur A, Lenau K et al (2013) Molecular pathology via IR and Raman spectral imaging. J Biophoton 6:855–886. doi:10.1002/jbio.201300131
Carter EA, Tam KK, Armstrong RS, Lay PA (2009) Vibrational spectroscopic mapping and imaging of tissues and cells. Biophys Rev 1:95–103
Krafft C, Steiner G, Beleites C, Salzer R (2009) Disease recognition by infrared and Raman spectroscopy. J Biophotonics 2:13–28
Tran TN, Wehrens R, Buydens LMC (2005) Clustering multispectral images: a tutorial. Chemom Intell Lab Syst 77:3–17
Alexandrov T, Chernyavsky I, Becker M et al (2013) Analysis and interpretation of imaging mass spectrometry data by clustering mass-to-charge images according to their spatial similarity. Anal Chem 85:11189–11195
Malinowski ER (2002) Factor analysis in chemistry. Wiley
Legendre P, Legendre L (2012) Numerical ecology. Elsevier
Gower JC (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53:325–338
Nesselroade JR, Cattell RB (1988) Handbook of multivariate experimental psychology. Plenum Press, New York
Cattell RB (1973) Factor analysis: an introduction and manual for the psychologist and social scientist. Greenwood Press, London
Alexandrov T, Lasch P (2013) Segmentation of confocal Raman microspectroscopic imaging data using edge-preserving denoising and clustering. Anal Chem 85:5676–5683
Kelm BM, Kaster FO, Henning A et al (2012) Using spatial prior knowledge in the spectral fitting of MRS images. NMR Biomed 25:1–13
Bonifacio A, Beleites C, Vittur F et al (2010) Chemical imaging of articular cartilage sections with Raman mapping, employing uni- and multi-variate methods for data analysis. Analyst (Cambridge, U K) 135:3193–3204
R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Beleites C, Sergo V hyperSpec: a package to handle hyperspectral data sets in R.
Sarkar D (2008) Lattice: multivariate data visualization with R. Springer
Liland KH, Mevik B-H baseline: Baseline correction of spectra. R Package version 1.1-2. http://CRAN.R-project.org/package=baseline
Gibb S, Strimmer K (2012) MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28:2270–2271. doi:10.1093/bioinformatics/bts447
Müllner D (2011) Modern hierarchical, agglomerative clustering algorithms. ArXiv11092378 Cs Stat
Müllner D (2013) Fast hierarchical, agglomerative clustering routines for R and python. J Stat Softw 53:1–18
Acknowledgements
AB and VS acknowledge partial support from IRCCS Burlo Garofolo, Trieste (Italy) and from FRA 2012 grant from University of Trieste. CB is funded by the German Ministry for Education and Research (BMBF) via the project RamanCTC (13N12685).
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Bonifacio, A., Beleites, C. & Sergo, V. Application of R-mode analysis to Raman maps: a different way of looking at vibrational hyperspectral data. Anal Bioanal Chem 407, 1089–1095 (2015). https://doi.org/10.1007/s00216-014-8321-7
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DOI: https://doi.org/10.1007/s00216-014-8321-7