Local Binary Patterns as a Feature Descriptor in Alignment-free Visualisation of Metagenomic Data

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)

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
• 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