SIP-ONC-01A: The Scale Mismatch Problem in Spatial Transcriptomics of Cancer
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2026
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| _version_ | 1866901480847441920 |
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| author | Smith, John Richard SHAI / HATI3 |
| author_facet | Smith, John Richard SHAI / HATI3 |
| contents | <h2><span>Abstract</span></h2> <p><span>We report a failed empirical prediction and the methodological insight it produced. Using the Coherence-Decoherence Remodelling (CDR) framework, we predicted that spatial autocorrelation of gene expression — measured as Local Coherence Length (LCL) via variogram analysis — would be shorter in tumour tissue than in matched normal tissue, reflecting loss of regulatory coordination in cancer. We tested this prediction on 10x Visium spatial transcriptomics data from a treatment-naive NSCLC patient (E-MTAB-13530, De Zuani et al. 2024). The prediction was completely reversed: tumour tissue showed dramatically longer spatial autocorrelation than normal tissue across all four measured signalling channels (EMT, immune, proliferation, hypoxia), confirmed independently by Global Moran's I.</span></p> <p> </p> <p><span>Investigation revealed that the reversal reflects a fundamental scale mismatch. Standard-resolution spatial transcriptomics (Visium, 55 μm spot diameter) captures mesoscale pathophysiological organisation (hypoxic gradients, EMT fronts, immune exclusion zones) that emerges in tumours. It cannot resolve microscale developmental coordination (cell-cell junctions, basal-apical polarity, positional identity) that is maintained in healthy tissue. Healthy lung tissue is developmentally coordinated but spatially uniform at Visium resolution — no gradients to detect. Tumour tissue is developmentally decoherent but spatially structured at Visium resolution — abundant gradients from resource limitation, clonal expansion, and immune evasion.</span></p> <p> </p> <p><span>This scale mismatch is not specific to the CDR framework. Any study interpreting spatial autocorrelation in tumour atlases as a measure of tissue coordination risks the same conflation. We propose that the field distinguish two orthogonal properties: developmental coherence (microscale, decreases in cancer, requires single-cell resolution) and pathophysiological organisation (mesoscale, increases in cancer, captured by standard spatial transcriptomics). This distinction has implications for how spatial transcriptomics data from tumour atlases — including the Human Tumor Atlas Network, TRACERx, and disease-specific spatial studies — should be interpreted.</span></p> <p> </p> <p><span>Keywords:</span><span> spatial transcriptomics, spatial autocorrelation, tumour microenvironment, developmental coordination, scale mismatch, variogram analysis, Local Coherence Length, Visium, cancer biology, methodology</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19707519 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | SIP-ONC-01A: The Scale Mismatch Problem in Spatial Transcriptomics of Cancer Smith, John Richard SHAI / HATI3 spatial transcriptomics, spatial autocorrelation, tumour microenvironment, developmental coordination, scale mismatch, variogram analysis, Local Coherence Length, Visium, cancer biology, methodology <h2><span>Abstract</span></h2> <p><span>We report a failed empirical prediction and the methodological insight it produced. Using the Coherence-Decoherence Remodelling (CDR) framework, we predicted that spatial autocorrelation of gene expression — measured as Local Coherence Length (LCL) via variogram analysis — would be shorter in tumour tissue than in matched normal tissue, reflecting loss of regulatory coordination in cancer. We tested this prediction on 10x Visium spatial transcriptomics data from a treatment-naive NSCLC patient (E-MTAB-13530, De Zuani et al. 2024). The prediction was completely reversed: tumour tissue showed dramatically longer spatial autocorrelation than normal tissue across all four measured signalling channels (EMT, immune, proliferation, hypoxia), confirmed independently by Global Moran's I.</span></p> <p> </p> <p><span>Investigation revealed that the reversal reflects a fundamental scale mismatch. Standard-resolution spatial transcriptomics (Visium, 55 μm spot diameter) captures mesoscale pathophysiological organisation (hypoxic gradients, EMT fronts, immune exclusion zones) that emerges in tumours. It cannot resolve microscale developmental coordination (cell-cell junctions, basal-apical polarity, positional identity) that is maintained in healthy tissue. Healthy lung tissue is developmentally coordinated but spatially uniform at Visium resolution — no gradients to detect. Tumour tissue is developmentally decoherent but spatially structured at Visium resolution — abundant gradients from resource limitation, clonal expansion, and immune evasion.</span></p> <p> </p> <p><span>This scale mismatch is not specific to the CDR framework. Any study interpreting spatial autocorrelation in tumour atlases as a measure of tissue coordination risks the same conflation. We propose that the field distinguish two orthogonal properties: developmental coherence (microscale, decreases in cancer, requires single-cell resolution) and pathophysiological organisation (mesoscale, increases in cancer, captured by standard spatial transcriptomics). This distinction has implications for how spatial transcriptomics data from tumour atlases — including the Human Tumor Atlas Network, TRACERx, and disease-specific spatial studies — should be interpreted.</span></p> <p> </p> <p><span>Keywords:</span><span> spatial transcriptomics, spatial autocorrelation, tumour microenvironment, developmental coordination, scale mismatch, variogram analysis, Local Coherence Length, Visium, cancer biology, methodology</span></p> |
| title | SIP-ONC-01A: The Scale Mismatch Problem in Spatial Transcriptomics of Cancer |
| topic | spatial transcriptomics, spatial autocorrelation, tumour microenvironment, developmental coordination, scale mismatch, variogram analysis, Local Coherence Length, Visium, cancer biology, methodology |
| url | https://doi.org/10.5281/zenodo.19707519 |