DeepReGraph co-clusters temporal gene expression and cis-regulatory elements through heterogeneous graph representation learning
DeepReGraph co-clusters temporal gene expression and cis-regulatory elements through heterogeneous graph representation learning
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DOI：
10.12688/f1000research.114698.1
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发表时间：
2022-05
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影响因子：
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通讯作者：
Jesús Fernando Cevallos Moreno;P. Zarrineh;Aminael Sánchez-Rodríguez;Massimo Mecella
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文献类型：
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作者：
Jesús Fernando Cevallos Moreno;P. Zarrineh;Aminael Sánchez-Rodríguez;Massimo Mecella
这项工作提出了DeepReGraph，这是一种将基因和顺式调控元件（克雷斯）共聚类到候选调控网络中的新方法。基因表达数据以及来自公开可用的小鼠胎儿心脏组织数据集的三个CRE活性标记物的数据用于DeepReGraph概念验证。在这项研究中，我们使用来自ATAC-seq实验的开放染色质可及性，以及H3 K27 ac和H3 K27 me 3组蛋白标记作为克雷斯活性标记。然而，该方法可以用其他标记集来执行。我们将所有数据源建模为异构图，并采用最先进的表示学习算法来生成低维且易于聚类的基因和克雷斯嵌入。深度图自动编码器和自适应稀疏生成模型是DeepReGraph的算法核心。我们的工作的主要贡献是设计适当的组合规则的异质性基因表达和CRE活性数据和计算编码的知名基因表达调控机制到一个合适的目标函数的图形嵌入。我们发现，最终包埋中的基因和克雷斯的共簇揭示了小鼠胎儿心脏组织中的发育调控机制。这样的聚类不能通过仅使用基因表达数据来实现。功能富集分析证明，共簇中的基因参与不同的生物过程。克雷斯中富集的转录因子结合位点优先考虑驱动基因表达时间变化的候选转录因子。因此，我们得出结论，DeepReGraph可以从高通量表达和表观基因组数据中促进假设驱动的组织发育研究。完整的源代码和数据可以在DeepReGraph GitHub项目上获得。
This work presents DeepReGraph, a novel method for co-clustering genes and cis-regulatory elements (CREs) into candidate regulatory networks. Gene expression data, as well as data from three CRE activity markers from a publicly available dataset of mouse fetal heart tissue, were used for DeepReGraph concept proofing. In this study we used open chromatin accessibility from ATAC-seq experiments, as well as H3K27ac and H3K27me3 histone marks as CREs activity markers. However, this method can be executed with other sets of markers. We modelled all data sources as a heterogeneous graph and adapted a state-of-the-art representation learning algorithm to produce a low-dimensional and easy-to-cluster embedding of genes and CREs. Deep graph auto-encoders and an adaptive-sparsity generative model are the algorithmic core of DeepReGraph. The main contribution of our work is the design of proper combination rules for the heterogeneous gene expression and CRE activity data and the computational encoding of well-known gene expression regulatory mechanisms into a suitable objective function for graph embedding. We showed that the co-clusters of genes and CREs in the final embedding shed light on developmental regulatory mechanisms in mouse fetal-heart tissue. Such clustering could not be achieved by using only gene expression data. Function enrichment analysis proves that the genes in the co-clusters are involved in distinct biological processes. The enriched transcription factor binding sites in CREs prioritize the candidate transcript factors which drive the temporal changes in gene expression. Consequently, we conclude that DeepReGraph could foster hypothesis-driven tissue development research from high-throughput expression and epigenomic data. Full source code and data are available on the DeepReGraph GitHub project.