A best-match approach for gene set analyses in embedding spaces

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dc.citation.firstpage1421en_US
dc.citation.issueNumber9en_US
dc.citation.journalTitleGenome Researchen_US
dc.citation.lastpage1433en_US
dc.citation.volumeNumber34en_US
dc.contributor.authorLi, Lechuanen_US
dc.contributor.authorDannenfelser, Ruthen_US
dc.contributor.authorCruz, Charlieen_US
dc.contributor.authorYao, Vickyen_US
dc.date.accessioned2024-10-29T14:11:22Zen_US
dc.date.available2024-10-29T14:11:22Zen_US
dc.date.issued2024en_US
dc.description.abstractEmbedding 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 an Algorithm for Network Data Embedding and Similarity (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- and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multiorganism 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.en_US
dc.identifier.citationLi, L., Dannenfelser, R., Cruz, C., & Yao, V. (2024). A best-match approach for gene set analyses in embedding spaces. Genome Research, 34(9), 1421–1433. https://doi.org/10.1101/gr.279141.124en_US
dc.identifier.digitalGenomeRes-2024-Li-1421-33en_US
dc.identifier.doihttps://doi.org/10.1101/gr.279141.124en_US
dc.identifier.urihttps://hdl.handle.net/1911/117955en_US
dc.language.isoengen_US
dc.publisherCold Spring Harbor Laboratory Pressen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial (CC BY-NC) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_US
dc.titleA best-match approach for gene set analyses in embedding spacesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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