Abstract
Sequence data are widely used to get a deeper insight into biological systems. From a data analysis perspective they are given as a set of sequences of symbols with varying length. In general they are compared using nonmetric score functions. In this form the data are nonstandard, because they do not provide an immediate metric vector space and their analysis using standard methods is complicated. In this chapter we provide various strategies for how to analyze these type of data in a mathematically accurate way instead of the often seen ad hoc solutions. Our approach is based on the scoring values from protein sequence data although be applicable in a broader sense. We discuss potential recoding concepts of the scores and discuss algorithms to solve clustering, classification and embedding tasks for score data for a protein sequence application.
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Acknowledgment
I would like to thank my former colleagues Andrej Gisbrecht, Barbara Hammer and Alexander Grigor’yan, University of Bielefeld for supporting parts of this work. I am grateful to the Max-Planck-Institute for Physics of Complex Systems in Dresden and Michael Biehl, Thomas Villmann and Manfred Opper as the organizer of the Statistical Inference: Models in Physics and Learning-Workshop for providing a nice working atmosphere leading to some of the ideas outlined in this manuscript. The author was supported by a Marie Curie Intra-European Fellowship (IEF): FP7-PEOPLE-2012-IEF (FP7-327791-ProMoS). I would like to thank R. Duin and E. Pekalska for providing various information about pseudo-Euclidean spaces and access to distools and prtools.
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Schleif, FM. (2016). Protein Sequence Analysis by Proximities. In: Jung, K. (eds) Statistical Analysis in Proteomics. Methods in Molecular Biology, vol 1362. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3106-4_12
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DOI: https://doi.org/10.1007/978-1-4939-3106-4_12
Publisher Name: Humana Press, New York, NY
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