Novel gene sets improve set-level classification of prokaryotic gene expression data

BMC Bioinformatics

Research Article

Filip železný¹  Matěj Holec¹  Ondřej Kuželka² 

[1] Faculty of Electrical Engineering, Czech Technical University, Technická 2, 166 27, Prague, Czech Republic;Faculty of Electrical Engineering, Czech Technical University, Technická 2, 166 27, Prague, Czech Republic;School of Computer Science and Informatics, Cardiff University, Queen’s Buildings, 5 The Parade, Roath, CF24 3AA, Cardiff, UK;

关键词: Set-level;    Classification;    Gene expression;    Regulation;    Interaction;   

DOI  :  10.1186/s12859-015-0786-7

 received in 2015-01-28, accepted in 2015-10-20,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundSet-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers.MethodsWe define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach.ResultsThe novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers.ConclusionNovel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

【 授权许可】

CC BY   
© Holec et al. 2015