Holec Matěj, Kuželka Ondřej, Železný Filip
Faculty of Electrical Engineering, Czech Technical University, Technická 2, Prague, 166 27, Czech Republic.
School of Computer Science and Informatics, Cardiff University, Queen's Buildings, 5 The Parade, Roath, Cardiff, CF24 3AA, UK.
BMC Bioinformatics. 2015 Oct 28;16:348. doi: 10.1186/s12859-015-0786-7.
Set-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.
We 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.
The 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.
Novel 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.
基因表达数据的集水平分类近来受到了广泛关注。在这种情况下，与基因相对应的高维特征向量被转换为与具有生物学可解释性的基因集相对应的低维特征向量。降维有望降低过拟合风险，从而可能提高所学习分类器的准确性。然而，最近的实证研究并未证实这一预期。在此，我们假设在集水平框架中报告的不利分类结果是由于采用了通常基于基因本体和代谢网络的KEGG数据库定义的不合适基因集。我们探索了一种基于调控相互作用来定义基因集的替代方法，我们期望这种方法能收集到表达更相关的基因。我们假设这样更相关的基因集将能够学习到更准确的分类器。
我们利用调控相互作用信息定义了两个基因集家族，并使用公开的原核生物基因表达数据集对它们进行表型分类任务评估。从这两个基因集家族中，我们首先选择表现最佳的亚型。然后，在独立（测试）数据集上，将这两个选定的亚型与最先进的基因集以及传统的基因水平方法进行评估。
新的基因集确实比传统基因集更具相关性，并能带来显著更准确的分类器。新的基因集确实比传统基因集更具相关性，并能带来显著更准确的分类器。
基于调控相互作用定义的新基因集改善了基因表达数据的集水平分类。可通过http://ida.felk.cvut.cz/novelgenesets.tar.gz获取重现实验所需的实验脚本和其他材料。
