On the consistent and scalable detection of spatial patterns
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912871145799680 |
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| author | Su, Jiayu Fung, Jun Hou Wang, Haoyu Yang, Dian Knowles, David A. Rabadan, Raul |
| author_facet | Su, Jiayu Fung, Jun Hou Wang, Haoyu Yang, Dian Knowles, David A. Rabadan, Raul |
| contents | Detecting spatial patterns is fundamental to scientific discovery, yet current methods lack statistical consensus and face computational barriers when applied to large-scale spatial omics datasets. We unify major approaches through a single quadratic form and derive general consistency conditions. We reveal that several widely used methods, including Moran's I, are inconsistent, and propose scalable corrections. The resulting test enables robust pattern detection across millions of spatial locations and single-cell lineage-tracing datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02825 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On the consistent and scalable detection of spatial patterns Su, Jiayu Fung, Jun Hou Wang, Haoyu Yang, Dian Knowles, David A. Rabadan, Raul Applications Quantitative Methods Methodology Detecting spatial patterns is fundamental to scientific discovery, yet current methods lack statistical consensus and face computational barriers when applied to large-scale spatial omics datasets. We unify major approaches through a single quadratic form and derive general consistency conditions. We reveal that several widely used methods, including Moran's I, are inconsistent, and propose scalable corrections. The resulting test enables robust pattern detection across millions of spatial locations and single-cell lineage-tracing datasets. |
| title | On the consistent and scalable detection of spatial patterns |
| topic | Applications Quantitative Methods Methodology |
| url | https://arxiv.org/abs/2602.02825 |