Lechuan Li, Ruth Dannenfelser, Charlie Cruz, Vicky Yao
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引用次数: 0
摘要
嵌入方法是一类非常有价值的方法,可将复杂的高维数据中的基本信息提炼到更容易获取的低维空间中。嵌入方法在生物数据中的应用表明,基因嵌入能有效捕捉基因之间的物理、结构和功能关系。然而,这种效用主要是通过将基因嵌入用于下游机器学习任务来实现的。直接研究嵌入,特别是分析嵌入空间中的基因集的工作则少得多。在这里,我们提出了一种新颖的最佳匹配方法--ANDES,它可以与现有的基因嵌入一起使用,在比较基因集的同时协调基因集的多样性。这种直观的方法对于提高嵌入空间在各种任务中的实用性具有重要的下游意义。具体来说,我们展示了当 ANDES 应用于编码蛋白质-蛋白质相互作用的不同基因嵌入时,如何将其用作一种新型的基于过度代表性和基于等级的基因组富集分析方法,从而达到最先进的性能。此外,ANDES 还能利用多生物体联合基因嵌入促进跨生物体的功能知识转移,从而实现跨模型系统的表型映射。我们灵活、直接的最佳匹配方法可扩展到集合元素之间具有不同群落结构的其他嵌入空间。
A best-match approach for gene set analysis in embedding spaces
Embedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose ANDES, a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein-protein interactions, can be used as a novel overrepresentation-based and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multi-organism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.
期刊介绍:
Launched in 1995, Genome Research is an international, continuously published, peer-reviewed journal that focuses on research that provides novel insights into the genome biology of all organisms, including advances in genomic medicine.
Among the topics considered by the journal are genome structure and function, comparative genomics, molecular evolution, genome-scale quantitative and population genetics, proteomics, epigenomics, and systems biology. The journal also features exciting gene discoveries and reports of cutting-edge computational biology and high-throughput methodologies.
New data in these areas are published as research papers, or methods and resource reports that provide novel information on technologies or tools that will be of interest to a broad readership. Complete data sets are presented electronically on the journal''s web site where appropriate. The journal also provides Reviews, Perspectives, and Insight/Outlook articles, which present commentary on the latest advances published both here and elsewhere, placing such progress in its broader biological context.