Long non-coding RNAs (lncRNAs) have been shown to be involved in multiple biological processes and play critical roles in tumorigenesis. Numerous lncRNAs have been discovered in diverse species, but the functions of most lncRNAs still remain unclear. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood. With the advances of high-throughput technologies, the increasing availability of genomic data creates opportunities for deciphering the molecular mechanism and underlying pathogenesis of human diseases. Here, we develop an integrative framework called JONMF to identify lncRNA-mRNA co-expression modules based on the sample-matched lncRNA and mRNA expression profiles. We formulate the module detection task as an optimization problem with joint orthogonal non-negative matrix factorization that could effectively prevent multicollinearity and produce a good modularity interpretation. The constructed lncRNA-mRNA co-expression network and the gene-gene interaction network are used as the network-regularized constraints to improve the module accuracy, while the sparsity constraints are simultaneously utilized to achieve modular sparse solutions. We applied JONMF to human ovarian cancer dataset and the experiment results demonstrate that the proposed method can effectively discover biologically functional co-expression modules, which may provide insights into the function of lncRNAs and molecular mechanism of human diseases.

中文翻译：

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从卵巢癌的匹配表达数据中识别lncRNA和mRNA共表达模块。

长的非编码RNA（lncRNA）已被证明参与多种生物学过程，并在肿瘤发生中起关键作用。已经在不同物种中发现了许多lncRNA，但大多数lncRNA的功能仍不清楚。同时，它们的表达模式和调控机制也远未完全被理解。随着高通量技术的发展，基因组数据的可用性不断提高，为破译人类疾病的分子机制和潜在发病机理创造了机会。在这里，我们开发了一个称为JONMF的集成框架，可基于样品匹配的lncRNA和mRNA表达谱来识别lncRNA-mRNA共表达模块。我们将模块检测任务公式化为具有联合正交非负矩阵分解的优化问题，可以有效防止多重共线性并产生良好的模块化解释。构建的lncRNA-mRNA共表达网络和基因-基因相互作用网络被用作网络规范化的约束条件，以提高模块的准确性，而稀疏性约束条件则同时用于实现模块化的稀疏解决方案。我们将JONMF应用于人类卵巢癌数据集，实验结果表明，该方法可以有效地发现具有生物学功能的共表达模块，从而可以为lncRNA的功能和人类疾病的分子机制提供见解。构建的lncRNA-mRNA共表达网络和基因-基因相互作用网络被用作网络规范化的约束条件，以提高模块的准确性，而稀疏性约束条件则同时用于实现模块化的稀疏解决方案。我们将JONMF应用于人类卵巢癌数据集，实验结果表明，该方法可以有效地发现生物学功能的共表达模块，这可以为lncRNA的功能和人类疾病的分子机制提供见解。构建的lncRNA-mRNA共表达网络和基因-基因相互作用网络被用作网络规范化的约束条件，以提高模块的准确性，而稀疏性约束条件则同时用于实现模块化的稀疏解决方案。我们将JONMF应用于人类卵巢癌数据集，实验结果表明，该方法可以有效地发现生物学功能的共表达模块，这可以为lncRNA的功能和人类疾病的分子机制提供见解。