A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.
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机译：基因共表达网络（GCN）描述了基因之间的关联以及指向生物化学途径遗传协调的点。但是，只有在采样条件下存在时，GCN中的遗传相关性才可以检测到。随着公共存储库中可用基因表达样本数量的增加，从各种生物学有趣的条件中发现遗传相关性的潜力更大。但是，即使存在基因相关性，其发现也可能被噪声掩盖。噪声是由自然变化（内部和外部），系统变化（由样品测量协议和仪器引起）以及通过选择数据处理工具创建的算法和统计变化引起的。各种已发表的研究，方法和方法试图解决这些变化的每一个贡献以减少噪声。在这里，我们描述了一种使用高斯混合模型（GMM）来解决混合输入条件下网络构建过程中自然外部（特定条件）变化的方法。为了证明其实用性，我们从The Cancer Genome Atlas获得的5种肿瘤亚型的2,016个混合基因表达数据集中，构建并分析了带有条件注释的GCN。我们的结果表明，GMM有助于发现肿瘤亚型特异的基因共表达模式（模块），这些共表达模式可显着丰富临床属性。
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