Uncovering biomarker genes with enriched classification potential from Hallmark gene sets
Uncovering biomarker genes with enriched classification potential from Hallmark gene sets
复制标题
DOI：
10.1038/s41598-019-46059-1
复制
发表时间：
2019-07-05
影响因子：
4.6
通讯作者：
Feltus, F. Alex
中科院分区：
文献类型：
--
作者：
Targonski, Colin A.;Shearer, Courtney A.;Feltus, F. Alex
鉴于基因表达和表型结果之间的复杂关系，需要计算效率高的方法来筛选大型高维数据集，以识别生物相关的生物标志物。在这份报告中，我们描述了一种识别数据集中最突出的生物标志物基因的方法，我们称之为“候选基因”，通过评估基因组合对数据集中的样本进行分类的能力，我们称之为“分类潜力”。我们的算法，基因甲骨文，使用神经网络来测试用户定义的基因集的多基因分类潜力，然后使用组合的方法来进一步分解选定的基因集为候选和非候选生物标志物基因。我们在来自分子签名数据库（MSigDB）的策划基因集上测试了该算法，该基因集在从癌症基因组图谱（TCGA）和基因型-组织表达（GTEx）数据库获得的RNAseq基因表达矩阵中定量。首先，我们确定了哪些MSigDB Hallmark子集对TCGA和GTEx数据集都具有重要的分类潜力。然后，我们确定了每个Hallmark基因集中最具歧视性的候选生物标志物基因，并提供证据表明这些基因的生物标志物潜力的提高可能是由于功能复杂性的降低。
Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call "candidate genes", by evaluating the ability of gene combinations to classify samples from a dataset, which we call "classification potential". Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes. We tested this algorithm on curated gene sets from the Molecular Signatures Database (MSigDB) quantified in RNAseq gene expression matrices obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data repositories. First, we identified which MSigDB Hallmark subsets have significant classification potential for both the TCGA and GTEx datasets. Then, we identified the most discriminatory candidate biomarker genes in each Hallmark gene set and provide evidence that the improved biomarker potential of these genes may be due to reduced functional complexity.