{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T13:48:54Z","timestamp":1782308934291,"version":"3.54.5"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"crossref","award":["T32GM086287"],"award-info":[{"award-number":["T32GM086287"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Yale School of Medicine and the Yale Department of Anesthesiology"},{"DOI":"10.13039\/100000968","name":"American Heart Association","doi-asserted-by":"publisher","award":["26POST1553068"],"award-info":[{"award-number":["26POST1553068"]}],"id":[{"id":"10.13039\/100000968","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIGMS, NIH"},{"name":"United States government"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Intracellular signaling pathways regulate essential cellular functions and orchestrate complex biological processes, yet their dynamic activity remains challenging to quantify with precision. Advances in single-cell omics enable pathway activity inference at the transcriptional level; however, existing computational tools often overlook mechanistic features of signaling networks, failing to formally treat the expected directionality of transcriptional change due to signal transduction. To address this technological gap, we have engineered PathwayEmbed, an R-based computational framework for estimating intracellular signal transduction states from single-cell transcriptomic datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>PathwayEmbed integrates KEGG pathway information with perturbation-derived RNA sequencing data to assign directional coefficients that capture gene-specific transcriptional responses to pathway activation, repression, and\/or signal transduction. These coefficients, in combination with the input data, are used to compute hypothetic ON\/OFF range for each pathway. Each cell is then mapped to a specific location between these ON\/OFF states, and activity scores are then computed based on the distances to these reference states, providing a continuous and interpretable measure of signaling activity at single-cell resolution. This framework enables robust visualization and quantitative comparison of pathway activity across cell populations. Applied to spatial transcriptomic data, PathwayEmbed captures spatial variation in signaling transduction states and allows comparisons at both temporal and spatial scale. The framework takes tabular data as input and is broadly compatible with established single-cell analysis workflows, supports user-defined pathway ground-truths, and offers a flexible, mechanistically informed approach for quantifying and comparing intracellular signaling activity in a wide variety of contexts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>PathwayEmbed is an open-source R software under academic free license, and it is available at https:\/\/github.com\/raredonlab\/PathwayEmbed. Use-case vignettes are available at https:\/\/raredonlab.github.io\/PathwayEmbed\/.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag346","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T11:39:53Z","timestamp":1779968393000},"source":"Crossref","is-referenced-by-count":0,"title":["PathwayEmbed: a computational tool to quantify intracellular signaling transduction states from transcriptomic data"],"prefix":"10.1093","volume":"42","author":[{"given":"Yaqing","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Pathology, Yale University , New Haven, CT 06520,","place":["United States"]},{"name":"Vascular Biology and Therapeutics Program, Yale University , New Haven, CT 06520,","place":["United States"]},{"name":"Biomedical Engineering, Duke University , Durham, NC 27705,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharon","family":"Gerecht","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Duke University , Durham, NC 27705,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Themis","family":"Kyriakides","sequence":"additional","affiliation":[{"name":"Department of Pathology, Yale University , New Haven, CT 06520,","place":["United States"]},{"name":"Vascular Biology and Therapeutics Program, Yale University , New Haven, CT 06520,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1441-6122","authenticated-orcid":false,"given":"Micha Sam Brickman","family":"Raredon","sequence":"additional","affiliation":[{"name":"Vascular Biology and Therapeutics Program, Yale University , New Haven, CT 06520,","place":["United States"]},{"name":"Department of Anesthesiology, Yale School of Medicine , New Haven, CT 06520,","place":["United States"]},{"name":"Program in Translational Biomedicine, Biological and Biomedical Sciences, Yale University , New Haven, CT 06520,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"2026062409125336200_btag346-B1","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nat Methods"},{"key":"2026062409125336200_btag346-B2","author":"Bengtsson","year":"2017"},{"key":"2026062409125336200_btag346-B3","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1016\/j.cell.2022.04.003","article-title":"Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays","volume":"185","author":"Chen","year":"2022","journal-title":"Cell"},{"key":"2026062409125336200_btag346-B4","doi-asserted-by":"crossref","first-page":"4376","DOI":"10.1038\/s41467-019-12235-0","article-title":"Functional interpretation of single cell similarity maps","volume":"10","author":"DeTomaso","year":"2019","journal-title":"Nat Commun"},{"key":"2026062409125336200_btag346-B5","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1038\/nmeth.3734","article-title":"Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis","volume":"13","author":"Fan","year":"2016","journal-title":"Nat Methods"},{"key":"2026062409125336200_btag346-B6","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1471-2105-14-7","article-title":"GSVA: gene set variation analysis for microarray and RNA-seq data","volume":"14","author":"Hanzelmann","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2026062409125336200_btag346-B7","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1038\/s41587-023-01767-y","article-title":"Dictionary learning for integrative, multimodal and scalable single-cell analysis","volume":"42","author":"Hao","year":"2024","journal-title":"Nat Biotechnol"},{"key":"2026062409125336200_btag346-B8","doi-asserted-by":"crossref","first-page":"RP98238","DOI":"10.7554\/eLife.98238.3","article-title":"A Ctnnb1 enhancer transcriptionally regulates wnt signaling dosage to balance homeostasis and tumorigenesis of intestinal epithelia","volume":"13","author":"Hua","year":"2024","journal-title":"Elife"},{"key":"2026062409125336200_btag346-B9","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1038\/s41413-023-00283-8","article-title":"Loss of notch signaling in skeletal stem cells enhances bone formation with aging","volume":"11","author":"Remark","year":"2023","journal-title":"Bone Res"},{"key":"2026062409125336200_btag346-B10","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1038\/s41467-017-02391-6","article-title":"Perturbation-response genes reveal signaling footprints in cancer gene expression","volume":"9","author":"Schubert","year":"2018","journal-title":"Nat Commun"},{"key":"2026062409125336200_btag346-B11","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1093\/bioinformatics\/btn577","article-title":"A novel signaling pathway impact analysis","volume":"25","author":"Tarca","year":"2009","journal-title":"Bioinformatics"},{"key":"2026062409125336200_btag346-B12","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1126\/science.aad0501","article-title":"Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq","volume":"352","author":"Tirosh","year":"2016","journal-title":"Science"},{"key":"2026062409125336200_btag346-B13","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1186\/1471-2105-6-225","article-title":"Pathway level analysis of gene expression using singular value decomposition","volume":"6","author":"Tomfohr","year":"2005","journal-title":"BMC Bioinformatics"},{"key":"2026062409125336200_btag346-B14","author":"Torchiano","year":"2020"},{"key":"2026062409125336200_btag346-B15","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-24277-4","volume-title":"ggplot2: Elegant Graphics for Data Analysis","author":"Wickham","year":"2016"},{"key":"2026062409125336200_btag346-B16","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.21105\/joss.01686","article-title":"Welcome to the tidyverse","volume":"4","author":"Wickham","year":"2019","journal-title":"JOSS"},{"key":"2026062409125336200_btag346-B17","author":"Wickham","year":"2025"},{"key":"2026062409125336200_btag346-B18","doi-asserted-by":"crossref","first-page":"2953","DOI":"10.1016\/j.csbj.2020.10.007","article-title":"Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data","volume":"18","author":"Zhang","year":"2020","journal-title":"Comput Struct Biotechnol J"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag346\/68428296\/btag346.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/6\/btag346\/68428296\/btag346.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/6\/btag346\/68428296\/btag346.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T13:13:01Z","timestamp":1782306781000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btag346\/8697681"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":18,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag346","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,6]]},"published":{"date-parts":[[2026,5,28]]},"article-number":"btag346"}}