Kyle Coleman

Jan 24, 2025 | nature.com | Kyle Coleman |Jeong Hwan Park |Hanying Yan |Isabel Barnfather |Joshua D. Rabinowitz |Yanxiang Deng | +5 more

Correction to: Nature Methods https://doi.org/10.1038/s41592-024-02574-2, published online 15 January 2025. In the version of the article initially published, the Data availability and Code availability sections were inadvertently omitted. These sections have now been included in the HTML and PDF versions of the article. About this articleColeman, K., Schroeder, A., Loth, M. et al. Author Correction: Resolving tissue complexity by multimodal spatial omics modeling with MISO. Nat Methods (2025).

Jan 15, 2025 | nature.com | Kyle Coleman |Jeong Hwan Park |Hanying Yan |Joshua D. Rabinowitz |Yanxiang Deng |Edward Lee | +5 more

AbstractSpatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology.

Aug 8, 2024 | nature.com | Kyle Coleman |Mingyao Li

Spatial omics technologies have transformed biomedical research by offering detailed, spatially resolved molecular profiles that elucidate tissue structure and function at unprecedented levels. AI can potentially unlock the full power of spatial omics, facilitating the integration of complex datasets and discovery of novel biomedical insights.

Apr 7, 2023 | nature.com | Kyle Coleman |Jian Hu

AbstractSpatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types in SRT, we present SpaDecon, a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution.