scSGL: kernelized signed graph learning for single-cell gene regulatory network inference
scSGL: kernelized signed graph learning for single-cell gene regulatory network inference
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DOI：
10.1093/bioinformatics/btac288
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发表时间：
2022-05-06
期刊：
影响因子：
5.8
通讯作者：
Maiti, Tapabrata
中科院分区：
文献类型：
--
作者：
Karaaslanli, Abdullah;Saha, Satabdi;Maiti, Tapabrata
动机：从大的单细胞RNA测序数据集中阐明基因调控网络（GRNs）的拓扑结构，同时有效地捕获其固有的细胞周期异质性和脱落，是目前计算系统生物学中最紧迫的问题之一。最近，已经开发了基于图信号处理的图学习（GL）方法以从图上定义的信号推断图拓扑。然而，现有的GL方法不适合学习有符号图，这是GRNs的一个特征，它能够解释基因网络中的激活和抑制关系。他们也无法处理高比例的零值存在于单细胞dataset.Results：为此，我们提出了一种新的签署GL的方法，scSGL，学习GRNs的基础上的平滑和非平滑的基因表达的假设，分别在激活和抑制边缘。然后，scSGL扩展内核，以考虑共表达的非线性和有效处理高度出现的零值。所提出的方法制定为一个非凸优化问题，并使用一个有效的ADMM框架解决。使用模拟数据集进行的性能评估表明，在GRN恢复中，内核化scSGL的性能优于现有的最先进的方法。使用人类和小鼠胚胎数据集进一步研究scSGL的性能。
Motivation: Elucidating the topology of gene regulatory networks (GRNs) from large single-cell RNA sequencing datasets, while effectively capturing its inherent cell-cycle heterogeneity and dropouts, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, a characteristic feature of GRNs, which are capable of accounting for both activating and inhibitory relationships in the gene network. They are also incapable of handling high proportion of zero values present in the single cell datasets.Results: To this end, we propose a novel signed GL approach, scSGL, that learns GRNs based on the assumption of smoothness and non-smoothness of gene expressions over activating and inhibitory edges, respectively. scSGL is then extended with kernels to account for non-linearity of co-expression and for effective handling of highly occurring zero values. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework. Performance assessment using simulated datasets demonstrates the superior performance of kernelized scSGL over existing state of the art methods in GRN recovery. The performance of scSGL is further investigated using human and mouse embryonic datasets.