{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T18:40:44Z","timestamp":1792435244943,"version":"4.5.0"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T00:00:00Z","timestamp":1792368000000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Spatial transcriptomics (ST) facilitates the spatial resolution of gene expression profiling; however, many widely used spot-based sequencing platforms capture transcripts from multiple neighboring cells at each location, producing mixed profiles that require accurate cell-type deconvolution. The wavelet-denoised graph attention framework for spatial transcriptomics deconvolution (WDGTA), a graph attention-based framework for ST cell-type deconvolution, was developed and validated on five datasets from two species and four organs. Through the integration of real with pseudo-ST spots, WDGTA introduces a wavelet-based multiscale denoising and reconstruction module, constructs a weighted topological graph using random projection forests, and applies multi-head graph attention to infer cell-type proportions in spatial spots. WDGTA achieved optimal performance in comparison with seven established deconvolution methods. Ablation and discrete wavelet transform (DWT)-specific analyses confirmed the contributions of wavelet denoising, weighted graph construction, and semi-supervised learning. Furthermore, DWT performance remained stable after feature-order perturbation. In mouse brain, cerebellum and ultraviolet radiation (UVR) skin data, WDGTA recovered biologically consistent spatial cell-type patterns and demonstrated robust cross-platform applicability and improved resolution of low-abundance melanocyte signals. Furthermore, it accurately captured melanocyte-associated spatial changes under conditions of UVR exposure and drug intervention. WDGTA provides a unified framework for ST deconvolution across the evaluated tissue contexts. In UVR-exposed skin, it resolved localized melanocyte-associated patterns that were concordant with marker-gene evidence and detected changes associated with UVR exposure and drug treatment. These findings support its use for characterizing spatial cell-type organization and low-abundance cell populations in heterogeneous tissues.<\/jats:p>","DOI":"10.1093\/bib\/bbag584","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T18:19:07Z","timestamp":1792433947000},"source":"Crossref","is-referenced-by-count":0,"title":["WDGTA: a wavelet-denoised graph attention framework for spatial transcriptomics deconvolution"],"prefix":"10.1093","volume":"27","author":[{"given":"Hao","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, No. 206 Guanggu 1st Road, East Lake High-Tech Development Zone , Wuhan, Hubei 430205 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenggong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, No. 206 Guanggu 1st Road, East Lake High-Tech Development Zone , Wuhan, Hubei 430205 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenchun","family":"Ran","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, No. 206 Guanggu 1st Road, East Lake High-Tech Development Zone , Wuhan, Hubei 430205 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xumeng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, No. 206 Guanggu 1st Road, East Lake High-Tech Development Zone , Wuhan, Hubei 430205 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