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)

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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
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 > Institute of Infection Immunity and Inflammation
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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