Multiple Signed Graph Learning for Gene Regulatory Network Inference
Multiple Signed Graph Learning for Gene Regulatory Network Inference
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DOI：
10.1109/icassp49357.2023.10096490
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Abdullah Karaaslanli;Satabdi Saha;T. Maiti;Selin Aviyente
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文献类型：
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作者：
Abdullah Karaaslanli;Satabdi Saha;T. Maiti;Selin Aviyente
许多真实世界的数据是通过数据样本之间的关系来表示的，即图结构。尽管许多数据集都带有预先存在的图，但仍然有大量的应用程序不能很容易地获得图结构。这种情况下的一个基本任务是图学习(GL)，它从一组图信号中推断出图的结构。现有的GL技术主要集中于学习单一的图结构；然而，样本通常以多种不同的方式连接。此外，现有的工作只能处理无符号图，而当代的任务需要有符号图的推理，而有符号图更能表示样本的相似性和差异性。本文提出了多个相关符号图的联合估计框架(MvSGL)。MvSGL优化了图形信号相对于图形的总变化，同时通过一致性图形确保图形彼此相似。MvSGL用于从包含多种细胞类型的单细胞数据集中推断多个基因调控网络(GRN)。通过模拟数据集和真实数据集的性能评估，验证了mvSGL在多个相关GRN的推理中的有效性。
Many real-world data are represented through the relations between data samples, i.e., a graph structure. Although many datasets come with a pre-existing graph, there is still a large number of applications where the graph structure is not readily available. An essential task for such cases is graph learning (GL), which infers the graph structure from a set of graph signals. Existing GL techniques mostly focus on learning a single graph structure; however, samples are usually connected in multiple different ways. Furthermore, existing works can only handle unsigned graphs, while contemporary tasks require inference of signed graphs, which are better at representing similarity and dissimilarity of samples. In this paper, we propose a framework (mvSGL) for joint estimation of multiple related signed graphs. mvSGL optimizes the total variation of graph signals with respect to graphs while ensuring that the graphs are similar to each other through a consensus graph. mvSGL is employed in the inference of multiple gene regulatory networks (GRN) from single cell datasets that include multiple cell types. Performance evaluation using simulated and real datasets demonstrates the effectiveness of mvSGL in the inference of multiple related GRNs.