NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles
NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles
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DOI：
10.1534/g3.120.401067
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发表时间：
2020-09-01
影响因子：
2.6
通讯作者：
Feltus, F. Alex
中科院分区：
文献类型：
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作者：
Husain, Benafsh;Hickman, Allison R.;Feltus, F. Alex
双基因表达关系通常基于诸如Pearson或斯皮尔曼相关性的度量来定义，其通常不能检测潜在的非线性依赖性或要求关系是单调的。此外，内在和外在噪声的组合以及样本子群体之间的嵌入关系降低了在构建基因共表达网络（GCN）期间提取生物相关边缘的概率。在本报告中，我们通过NetExtractor算法解决这些问题。NetExtractor首先使用高斯混合模型（GARCH）检查所有成对基因表达谱，以识别样本亚群，然后进行互信息（MI）分析，该分析能够检测非线性差异双基因表达关系。我们将NetExtractor应用于基因型-组织表达（GTEx）项目的脑组织RNA图谱，以获得以小脑和小脑半球富集边缘为中心的脑组织特异性基因表达关系网络。我们利用PsychENCODE前额叶皮层（PFC）基因调控网络（GRN）构建了一个与小脑组织中转录活性区域相关的小脑皮层（小脑）GRN。因此，我们证明了我们的NetExtractor方法的实用性，以检测生物相关的和新的非线性二进制基因关系。
Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.