单细胞基因调控网络方法的基准方法。

IF 2.3 Q3 BIOCHEMICAL RESEARCH METHODS
Bioinformatics and Biology Insights Pub Date : 2024-11-04 eCollection Date: 2024-01-01 DOI:10.1177/11779322241287120
Karamveer, Yasin Uzun
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引用次数: 0

摘要

基因调控网络是建模控制驱动细胞分化基因表达的基因相互作用的强大工具,而单细胞测序为利用高分辨率基因组数据构建这些网络提供了独特的机会。目前有很多利用单细胞数据构建这些网络的计算方法,而且有不同的方法用于对这些方法进行基准测试。然而,目前还缺少专门针对基准测试方法的全面讨论。在本文中,我们介绍了 GRN 术语,概述了常见的黄金标准研究和数据集,并定义了网络构建方法基准的性能指标。我们还指出了不同基准测试方法的优势和局限性,提出了可用于基准测试的其他地面实况数据集,并具体说明了这方面的其他注意事项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.

Gene regulatory networks are powerful tools for modeling genetic interactions that control the expression of genes driving cell differentiation, and single-cell sequencing offers a unique opportunity to build these networks with high-resolution genomic data. There are many proposed computational methods to build these networks using single-cell data, and different approaches are used to benchmark these methods. However, a comprehensive discussion specifically focusing on benchmarking approaches is missing. In this article, we lay the GRN terminology, present an overview of common gold-standard studies and data sets, and define the performance metrics for benchmarking network construction methodologies. We also point out the advantages and limitations of different benchmarking approaches, suggest alternative ground truth data sets that can be used for benchmarking, and specify additional considerations in this context.

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来源期刊
Bioinformatics and Biology Insights
Bioinformatics and Biology Insights BIOCHEMICAL RESEARCH METHODS-
CiteScore
6.80
自引率
1.70%
发文量
36
审稿时长
8 weeks
期刊介绍: Bioinformatics and Biology Insights is an open access, peer-reviewed journal that considers articles on bioinformatics methods and their applications which must pertain to biological insights. All papers should be easily amenable to biologists and as such help bridge the gap between theories and applications.
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