Kouchaki, S., Tirunagari, S., Tapinos, A. and Robertson, D. L. (2017) Local Binary Patterns as a Feature Descriptor in Alignment-free Visualisation of Metagenomic Data. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece, 06-09 Dec 2016, ISBN 9781509042401 (doi: 10.1109/SSCI.2016.7849955)
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Abstract
Shotgun sequencing has facilitated the analysis of complex microbial communities. However, clustering and visualising these communities without prior taxonomic information is a major challenge. Feature descriptor methods can be utilised to extract these taxonomic relations from the data. Here, we present a novel approach consisting of local binary patterns (LBP) coupled with randomised singular value decomposition (RSVD) and Barnes-Hut t-stochastic neighbor embedding (BH-tSNE) to highlight the underlying taxonomic structure of the metagenomic data. The effectiveness of our approach is demonstrated using several simulated and a real metagenomic datasets.
Item Type: | Conference Proceedings |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Robertson, Professor David |
Authors: | Kouchaki, S., Tirunagari, S., Tapinos, A., and Robertson, D. L. |
College/School: | College of Medical Veterinary and Life Sciences > School of Infection & Immunity College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research |
ISBN: | 9781509042401 |
Published Online: | 13 February 2017 |
Copyright Holders: | Copyright © 2016 IEEE |
First Published: | First published in 2016 IEEE Symposium Series on Computational Intelligence (SSCI) |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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