Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data

被引:2
作者
Stock, Marco [1 ,2 ,3 ,4 ]
Losert, Corinna [2 ,5 ]
Zambon, Matteo [1 ,2 ,3 ]
Popp, Niclas [1 ,2 ,3 ]
Lubatti, Gabriele [1 ,2 ,3 ]
Hoermanseder, Eva [1 ]
Heinig, Matthias [2 ,5 ,6 ]
Scialdone, Antonio [1 ,2 ,3 ]
机构
[1] Helmholtz Ctr Munich, Inst Epigenet & Stem Cells, Munich, Germany
[2] Helmholtz Ctr Munich, Inst Computat Biol, Munich, Germany
[3] Helmholtz Ctr Munich, Inst Funct Epigenet, Munich, Germany
[4] Tech Univ Munich, TUM Sch Life Sci Weihenstephan, Freising Weihenstephan, Germany
[5] Tech Univ Munich, TUM Sch Computat Informat & Technol, Dept Comp Sci, Garching, Germany
[6] German Ctr Cardiovasc Res DZHK, Munich Heart Assoc, Partner Site Munich, Berlin, Germany
关键词
Gene Regulatory Network Inference; Prior Knowledge; Single-cell Transcriptomics; Single-cell Multiomics; Graph Learning; ACCESSIBILITY; CHROMATIN; MOUSE;
D O I
10.1038/s44320-025-00088-3
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Many studies have used single-cell RNA sequencing (scRNA-seq) to infer gene regulatory networks (GRNs), which are crucial for understanding complex cellular regulation. However, the inherent noise and sparsity of scRNA-seq data present significant challenges to accurate GRN inference. This review explores one promising approach that has been proposed to address these challenges: integrating prior knowledge into the inference process to enhance the reliability of the inferred networks. We categorize common types of prior knowledge, such as experimental data and curated databases, and discuss methods for representing priors, particularly through graph structures. In addition, we classify recent GRN inference algorithms based on their ability to incorporate these priors and assess their performance in different contexts. Finally, we propose a standardized benchmarking framework to evaluate algorithms more fairly, ensuring biologically meaningful comparisons. This review provides guidance for researchers selecting GRN inference methods and offers insights for developers looking to improve current approaches and foster innovation in the field.
引用
收藏
页码:214 / 230
页数:17
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