UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

BMC Bioinformatics

Methodology Article

Basel Abu-Jamous¹  Rui Fa¹  Asoke K. Nandi²  David J. Roberts³ 

[1] Department of Electronic and Computer Engineering, Brunel University London, UB8 3PH, Uxbridge, Middlesex, UK;Department of Electronic and Computer Engineering, Brunel University London, UB8 3PH, Uxbridge, Middlesex, UK;Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland;National Health Service Blood and Transplant, OX3 9BQ, Oxford, UK;Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, OX3 9DU, Oxford, UK;

关键词: Genome-wide analysis;    Consistent co-expression;    Bi-CoPaM;    UNCLES;    Multiple datasets analysis;   

DOI  :  10.1186/s12859-015-0614-0

 received in 2015-03-16, accepted in 2015-05-16,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundCollective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets.ResultsHere, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn.ConclusionsThe UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

【 授权许可】

Unknown   
© Abu-Jamous et al.; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.