SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.
SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.
复制标题
DOI：
10.1093/bib/bbab547
复制
发表时间：
2022-01-17
影响因子：
9.5
通讯作者：
Lan X
中科院分区：
文献类型：
--
作者：
Luo Q;Yu Y;Lan X
高通量单细胞RNA-seq数据为破译基因之间的调控相互作用提供了前所未有的机会。然而，这种相互作用是复杂的，通常是非线性或非单调的，这使得使用线性模型进行推理具有挑战性。我们提出了SIGNET，这是一个基于深度学习的框架，用于捕获基因之间复杂的调控关系，假设参与基因调控的转录因子的表达水平是其靶基因表达的有力预测因子。基于各种真实和模拟scRNA-seq数据集的评估表明，SIGNET对ChIP-seq验证的不同类型细胞，特别是罕见细胞的调节相互作用更敏感。因此，这一过程对于各种下游分析，如细胞聚类和基因调控网络推断更有效。我们证明了SIGNET是识别驱动各种生物过程的重要调控模块的有用工具。
High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using linear models challenging. We present SIGNET, a deep learning-based framework for capturing complex regulatory relationships between genes under the assumption that the expression levels of transcription factors participating in gene regulation are strong predictors of the expression of their target genes. Evaluations based on a variety of real and simulated scRNA-seq datasets showed that SIGNET is more sensitive to ChIP-seq validated regulatory interactions in different types of cells, particularly rare cells. Therefore, this process is more effective for various downstream analyses, such as cell clustering and gene regulatory network inference. We demonstrated that SIGNET is a useful tool for identifying important regulatory modules driving various biological processes.
登录
查看更多内容
甲状旁腺激素样激素是头颈癌的不良预后标志物，并通过RUNX2调节促进细胞生长。
影响因子：
4.6
作者：
Chang WM;Lin YF;Su CY;Peng HY;Chang YC;Hsiao JR;Chen CL;Chang JY;Shieh YS;Hsiao M;Shiah SG
通讯作者：
Shiah SG
DOI：
10.1007/978-1-4939-2627-5_30
发表时间：
2016-01-01
期刊：
SYSTEMS BIOLOGY OF ALZHEIMER'S DISEASE
影响因子：
--
作者：
Gitter, Anthony;Bar-Joseph, Ziv
通讯作者：
Bar-Joseph, Ziv
DOI：
10.1073/pnas.1610609114
发表时间：
2017-06-06
影响因子：
11.1
作者：
Hamey, Fiona K.;Nestorowa, Sonia;Gottgens, Berthold
通讯作者：
Gottgens, Berthold
影响因子：
64.5
作者：
Azizi E;Carr AJ;Plitas G;Cornish AE;Konopacki C;Prabhakaran S;Nainys J;Wu K;Kiseliovas V;Setty M;Choi K;Fromme RM;Dao P;McKenney PT;Wasti RC;Kadaveru K;Mazutis L;Rudensky AY;Pe'er D
通讯作者：
Pe'er D
影响因子：
5.2
作者：
Hashimi, S. M.;Yu, S.;Wei, M. Q.
通讯作者：
Wei, M. Q.