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However, conventional statistical analyses often treat sub-measurements (pixels or cells) as independent, ignoring their nested origin from individual samples. This assumption inflates the effective sample size, increases false discoveries, and undermines biological interpretation. Pixel- or cell-level averaging avoids this but sacrifices spatial or cellular resolution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We evaluated block-SAM on both simulated and real-world datasets. In DESI-MSI data from kidney, lung, and ovarian tumors, block-SAM consistently identified fewer\u2014but more reliable\u2014differential features compared to traditional-SAM. For example, in the kidney dataset, traditional-SAM identified 569 features between tumor and normal tissue, while block-SAM identified 186\u2014all overlapping but excluding 383 likely false positives. Applied to a metastatic RCC scRNA-seq dataset comparing immune checkpoint blockade (ICB)-treated versus untreated patients, traditional-SAM identified over 19,000 differentially expressed genes in malignant cells; block-SAM reduced this to 19. These results demonstrate block-SAM\u2019s ability to reduce false discoveries while retaining biologically meaningful signals across diverse high-dimensional datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>All code and data associated with this study are deposited on Zenodo (https:\/\/doi.org\/10.5281\/zenodo.18273497). The samr package is freely available on the Comprehensive R Archive Network (CRAN).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag137","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T12:43:15Z","timestamp":1773924195000},"source":"Crossref","is-referenced-by-count":0,"title":["Structure-preserving multivariate hypothesis testing for mass spectrometry imaging and single-cell data"],"prefix":"10.1093","volume":"42","author":[{"given":"Keziah E","family":"Liebenberg","sequence":"first","affiliation":[{"name":"Department of Surgery, Baylor College of Medicine , Houston, TX 77030,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4893-6671","authenticated-orcid":false,"given":"Erin","family":"Craig","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan , Ann Arbor, MI 48109,","place":["United 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