基于压缩的网络主题集推断。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-10-10 eCollection Date: 2024-10-01 DOI:10.1371/journal.pcbi.1012460
Alexis Bénichou, Jean-Baptiste Masson, Christian L Vestergaard
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引用次数: 0

摘要

生物网络在物理和功能上的限制导致其组织结构在多个尺度上出现复杂的拓扑模式。网络母题是一种特殊的高阶网络特征,它被定义为具有统计规律的子图,因而受到了广泛关注。它们可以实现基本的逻辑和计算电路,被称为 "复杂网络的构件"。它们定义明确的结构和较小的尺寸也有助于在合成和自然生物实验中测试它们的功能。在此,我们利用子图收缩技术开发了一种基于无损网络压缩的主题挖掘框架。这提供了图案重要性的另一种定义,使我们能够比较不同的图案,并根据它们对网络的综合压缩情况,选出一组最有意义的图案以及其他突出的网络特征。我们的方法从本质上考虑了多重测试和子图之间的相关性,并且不依赖于先验地指定一个适当的无效模型。因此,它克服了基于假设检验的主题分析中的常见问题,并保证了稳健的统计推断。我们在数值数据上验证了我们的方法,然后将其应用于作为比较连接组学媒介的突触分辨率生物神经网络,评估了它们各自的可压缩性,并描述了它们推断出的电路图案。
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Compression-based inference of network motif sets.

Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred to as "building blocks of complex networks". Their well-defined structures and small sizes also enable the testing of their functions in synthetic and natural biological experiments. Here, we develop a framework for motif mining based on lossless network compression using subgraph contractions. This provides an alternative definition of motif significance which allows us to compare different motifs and select the collectively most significant set of motifs as well as other prominent network features in terms of their combined compression of the network. Our approach inherently accounts for multiple testing and correlations between subgraphs and does not rely on a priori specification of an appropriate null model. It thus overcomes common problems in hypothesis testing-based motif analysis and guarantees robust statistical inference. We validate our methodology on numerical data and then apply it on synaptic-resolution biological neural networks, as a medium for comparative connectomics, by evaluating their respective compressibility and characterize their inferred circuit motifs.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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