Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study.
Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study.
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DOI：
10.1038/s41598-017-09094-4
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发表时间：
2017-08-17
影响因子：
4.6
通讯作者：
Feltus FA
中科院分区：
文献类型：
--
作者：
Ficklin SP;Dunwoodie LJ;Poehlman WL;Watson C;Roche KE;Feltus FA
基因共表达网络（GCN）描述了基因之间的关联，并指出了生物化学途径的遗传协调。然而，GCN中的遗传相关性只有在采样条件下才能检测到。随着公共知识库中基因表达样本数量的增加，从各种生物学上有趣的条件中发现遗传相关性的潜力更大。然而，即使存在基因相关性，它们的发现也会被噪音掩盖。噪声来自自然变化（内在和外在）、系统变化（由样品测量协议和仪器引起）以及由数据处理工具选择产生的算法和统计变化。各种已发表的研究、途径和方法试图解决变化的这些贡献中的每一个以减少噪声。在这里，我们描述了一种方法，使用高斯混合模型（GARCH），以解决自然外在（特定条件）的变化，在网络建设从混合输入条件。为了证明实用性，我们从癌症基因组图谱中获得的五种肿瘤亚型的2，016个混合基因表达数据集的概要中构建并分析了条件注释的GCN。我们的研究结果表明，甘精胰岛素有助于发现肿瘤亚型特异性基因共表达模式（模块），这些模式显著丰富了临床属性。
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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DOI：
10.1016/j.future.2014.10.008
发表时间：
2015-05-01
影响因子：
7.5
作者：
Deelman, Ewa;Vahi, Karan;Wenger, Kent
通讯作者：
Wenger, Kent
影响因子：
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作者：
Feltus FA;Ficklin SP;Gibson SM;Smith MC
通讯作者：
Smith MC
影响因子：
1.8
作者：
Biernacki, Christophe;Celeux, Gilles;Langrognet, Florent
通讯作者：
Langrognet, Florent
影响因子：
4.7
作者：
Clarke, Colin;Madden, Stephen F.;Clynes, Martin
通讯作者：
Clynes, Martin
DOI：
10.1038/nrg3575
发表时间：
2014-01
期刊：
Nature reviews. Genetics
影响因子：
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作者：
通讯作者：
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