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However, we show that there is a prevalent transcript leakage problem in spatial transcriptomics data, where transcripts expressed by a cell diffuse to its neighborhood and are recurrently detected in the nearby cells. By analyzing published data sets, we show that this problem is general across data produced from different tissues and different species using different imaging-based and sequencing-based spatial transcriptomics platforms. It affects both upstream tasks such as expression quantification as well as downstream tasks such as cell-type annotation and detection of spatially-dependent gene expression. To tackle the transcript leakage problem, we propose a reference-free Bayesian model-based method, DeLeakage, which cleans up the data much more effectively than existing denoising methods. DeLeakage also improves cell-type annotation and avoids false detection of spatially dependent expression.<\/jats:p>","DOI":"10.64898\/2026.06.13.732076","type":"posted-content","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T21:05:39Z","timestamp":1781730339000},"source":"Crossref","is-referenced-by-count":1,"title":["Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues"],"prefix":"10.64898","author":[{"given":"Christina Huan","family":"Shi","sequence":"first","affiliation":[{"name":"Center for Data Science and Artificial Intelligence, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibo","family":"Zhai","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, The Chinese University of Hong Kong, Shatin, Hong 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