SFUMATO: Bayesian probabilistic clustering for uncertainty-aware spatialtranscriptomics analysis and mapping
Abstract
In situ sequencing methods provide subcellular-resolution gene expression data while preserving spatial context, enabling the integrated analysis of genomics and tissue morphology. However, many clustering workflows represent spatial transcriptomic structure through hard labels, imposing sharp boundaries between transcriptional domains and limiting the visualisation of gradual transitions, mixed signals, and uncertainty in cluster assignment, resulting in potentially misleading interpretations of tissue organisation. Here we present SFUMATO (Segmentation-Free Uncertainty Mapping and Analysis of Transcriptomic Organisation), a GPU-accelerated Bayesian probabilistic clustering and visualisation framework for spatial transcriptomics. SFUMATO builds on segmentation-free, multi-scale transcript binning and applies a Bayesian Gaussian mixture model to infer posterior probabilities over transcriptional components at each spatial location. These posterior probabilities provide a unified representation for hierarchical clustering, uncertainty-aware visualisation, and quantitative estimation of transcript-defined area fractions. SFUMATO assigns related colours to transcriptionally related components and blends colours according to posterior probabilities, producing maps in which sharp boundaries, gradual transitions, and ambiguous regions are represented directly in the visualisation. Classical hard labels remain recoverable from the same posterior distributions, preserving compatibility with downstream analyses that require discrete clusters. We evaluate SFUMATO on public 10x Genomics Xenium mouse brain and human breast cancer datasets. In mouse brain, SFUMATO preserves local transcriptional similarity in its colour encoding, maintains hierarchically consistent visualisations across different numbers of clusters, and recovers cell-associated spatial organisation without requiring prior cell segmentation. In breast cancer, SFUMATO-derived posterior maps can be converted into semantic masks concordant with pathology-associated regions, particularly invasive carcinoma, while highlighting the greater heterogeneity of DCIS-associated tissue. Together, these results show that SFUMATO provides an interpretable probabilistic representation of spatial transcriptomics that connects clustering, visualisation, and semantic segmentation across cell-associated and niche-level scales. More broadly, SFUMATO also provides a compact and continuous representation of highly multiplexed spatial transcriptomic information, offering a flexible input for future downstream analyses and multimodal integration.
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BibTeX
@misc{giustolisi2026sfumato,
title = {{SFUMATO: Bayesian probabilistic clustering for uncertainty-aware spatialtranscriptomics analysis and mapping}},
author = {A. Giustolisi and C. Avenel and C. Wählby},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.25.754409},
url = {https://www.biorxiv.org/content/10.64898/2026.09.25.754409v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - SFUMATO: Bayesian probabilistic clustering for uncertainty-aware spatialtranscriptomics analysis and mapping
AU - A. Giustolisi
AU - C. Avenel
AU - C. Wählby
PY - 2026
DA - 2026/09/25
PB - bioRxiv
DO - 10.64898/2026.09.25.754409
UR - https://www.biorxiv.org/content/10.64898/2026.09.25.754409v1
AB - In situ sequencing methods provide subcellular-resolution gene expression data while preserving spatial context, enabling the integrated analysis of genomics and tissue morphology. However, many clustering workflows represent spatial transcriptomic structure through hard labels, imposing sharp boundaries between transcriptional domains and limiting the visualisation of gradual transitions, mixed signals, and uncertainty in cluster assignment, resulting in potentially misleading interpretations of tissue organisation. Here we present SFUMATO (Segmentation-Free Uncertainty Mapping and Analysis of Transcriptomic Organisation), a GPU-accelerated Bayesian probabilistic clustering and visualisation framework for spatial transcriptomics. SFUMATO builds on segmentation-free, multi-scale transcript binning and applies a Bayesian Gaussian mixture model to infer posterior probabilities over transcriptional components at each spatial location. These posterior probabilities provide a unified representation for hierarchical clustering, uncertainty-aware visualisation, and quantitative estimation of transcript-defined area fractions. SFUMATO assigns related colours to transcriptionally related components and blends colours according to posterior probabilities, producing maps in which sharp boundaries, gradual transitions, and ambiguous regions are represented directly in the visualisation. Classical hard labels remain recoverable from the same posterior distributions, preserving compatibility with downstream analyses that require discrete clusters. We evaluate SFUMATO on public 10x Genomics Xenium mouse brain and human breast cancer datasets. In mouse brain, SFUMATO preserves local transcriptional similarity in its colour encoding, maintains hierarchically consistent visualisations across different numbers of clusters, and recovers cell-associated spatial organisation without requiring prior cell segmentation. In breast cancer, SFUMATO-derived posterior maps can be converted into semantic masks concordant with pathology-associated regions, particularly invasive carcinoma, while highlighting the greater heterogeneity of DCIS-associated tissue. Together, these results show that SFUMATO provides an interpretable probabilistic representation of spatial transcriptomics that connects clustering, visualisation, and semantic segmentation across cell-associated and niche-level scales. More broadly, SFUMATO also provides a compact and continuous representation of highly multiplexed spatial transcriptomic information, offering a flexible input for future downstream analyses and multimodal integration.
ER -