DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks.
DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks.
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DNF：一种差分网络流方法，用于识别基因调控网络的重新布线驱动程序
DOI：
10.1016/j.neucom.2020.05.028
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发表时间：
2020-10-14
期刊：
影响因子：
6
通讯作者：
Nie Q
中科院分区：
文献类型：
--
作者：
Xie J;Yang F;Wang J;Karikomi M;Yin Y;Sun J;Wen T;Nie Q
差异网络分析已成为识别发育和疾病驱动基因的重要方法。然而，大多数研究仅捕获潜在基因调控网络拓扑结构的局部特征。这些方法容易受到噪声和其他掩盖驱动基因活性的变化的影响。因此，迫切需要的方法，可以从随机变化和下游效应分离的影响，真正的监管元件。我们提出了差分网络流（DNF）的方法，以确定发展或疾病的进展的关键调节。在给定连续生物状态的网络表示的情况下，DNF通过网络流分布的差异来量化每个节点的重要性，从而能够捕获从局部到全局特征域的全面拓扑差异。当应用于来自癌症基因组图谱的四个人类数据集和鼠神经和造血分化的三个单细胞RNA-seq数据集时，DNF实现了比其他最先进方法更准确的驱动基因识别。此外，我们预测的关键调节器之间的串扰独立的网络神经元分化和神经退行性疾病的进展，其中APP被预测为神经干细胞分化的驱动基因。我们的方法是一种新的方法，用于量化不同生物状态网络中基因的重要性。
Differential network analysis has become an important approach in identifying driver genes in development and disease. However, most studies capture only local features of the underlying gene-regulatory network topology. These approaches are vulnerable to noise and other changes which mask driver-gene activity. Therefore, methods are urgently needed which can separate the impact of true regulatory elements from stochastic changes and downstream effects. We propose the differential network flow (DNF) method to identify key regulators of progression in development or disease. Given the network representation of consecutive biological states, DNF quantifies the essentiality of each node by differences in the distribution of network flow, which are capable of capturing comprehensive topological differences from local to global feature domains. DNF achieves more accurate driver-gene identification than other state-of-the-art methods when applied to four human datasets from The Cancer Genome Atlas and three single-cell RNA-seq datasets of murine neural and hematopoietic differentiation. Furthermore, we predict key regulators of crosstalk between separate networks underlying both neuronal differentiation and the progression of neurodegenerative disease, among which APP is predicted as a driver gene of neural stem cell differentiation. Our method is a new approach for quantifying the essentiality of genes across networks of different biological states.
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影响因子：
64.5
作者：
Cho Y;Sloutsky R;Naegle KM;Cavalli V
通讯作者：
Cavalli V
影响因子：
9.5
作者：
Lichtblau, Yvonne;Zimmermann, Karin;Leser, Ulf
通讯作者：
Leser, Ulf
影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
影响因子：
14.9
作者：
Colaprico A;Silva TC;Olsen C;Garofano L;Cava C;Garolini D;Sabedot TS;Malta TM;Pagnotta SM;Castiglioni I;Ceccarelli M;Bontempi G;Noushmehr H
通讯作者：
Noushmehr H
影响因子：
2.5
作者：
Ahlswede, R;Cai, N;Yeung, RW
通讯作者：
Yeung, RW