Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using DEGAS Spatial Smoothing.
1/5 보강
[SUMMARY] The examination of high-risk cells and regions in tissue samples from spatially resolved transcriptomics platforms offers meaningful insights into specific disease processes.
APA
Chatterjee D, Couetil JL, et al. (2026). Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using DEGAS Spatial Smoothing.. Bioinformatics (Oxford, England). https://doi.org/10.1093/bioinformatics/btag098
MLA
Chatterjee D, et al.. "Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using DEGAS Spatial Smoothing.." Bioinformatics (Oxford, England), 2026.
PMID
41934088
Abstract
[SUMMARY] The examination of high-risk cells and regions in tissue samples from spatially resolved transcriptomics platforms offers meaningful insights into specific disease processes. For existing methods, while cell types or clusters can be identified and associated with disease attributes, individual cells are unable to be associated in the same manner.
[METHOD] Diagnostic Evidence GAuge of Single-cells and spatial transcriptomics (DEGAS), solves the above problem by employing latent representations of gene expression data and domain adaptation to transfer disease attributes from patients to individual cells from single-cell RNA sequencing datasets. In this research, we present and evaluate DEGAS's versatility in adapting to data arising from various single-cell spatially resolved transcriptomics (scSRT) platforms. DEGAS successfully identified high-risk cells and regions in liver hepatocellular carcinoma and skin cutaneous melanoma, which were validated through known markers. Additionally, DEGAS was applied to our newly generated Type II Diabetes Xenium dataset revealing high-risk cells within the tissue samples.
[AVAILABILITY AND IMPLEMENTATION] The DEGAS software can be accessed at https://github.com/tsteelejohnson91/DEGAS. For the updated smoothing functions and associated codes, visit https://github.com/dchatter04/DEGAS-Spatial-Smoothing, which is archived at https://doi.org/10.5281/zenodo.18510221. Sources for the datasets reviewed are detailed in their respective sections. A description of some datasets, along with extra tables and figures, is provided in the Supplementary Materials file. Our newly generated Xenium data for Type II Diabetes can be found at https://doi.org/10.7303/syn68699752.
[SUPPLEMENTARY INFORMATION] A detailed description of some datasets, the relevant Algorithm, Tables, and Figures for the manuscript are presented in the Supplementary Materials file.
[METHOD] Diagnostic Evidence GAuge of Single-cells and spatial transcriptomics (DEGAS), solves the above problem by employing latent representations of gene expression data and domain adaptation to transfer disease attributes from patients to individual cells from single-cell RNA sequencing datasets. In this research, we present and evaluate DEGAS's versatility in adapting to data arising from various single-cell spatially resolved transcriptomics (scSRT) platforms. DEGAS successfully identified high-risk cells and regions in liver hepatocellular carcinoma and skin cutaneous melanoma, which were validated through known markers. Additionally, DEGAS was applied to our newly generated Type II Diabetes Xenium dataset revealing high-risk cells within the tissue samples.
[AVAILABILITY AND IMPLEMENTATION] The DEGAS software can be accessed at https://github.com/tsteelejohnson91/DEGAS. For the updated smoothing functions and associated codes, visit https://github.com/dchatter04/DEGAS-Spatial-Smoothing, which is archived at https://doi.org/10.5281/zenodo.18510221. Sources for the datasets reviewed are detailed in their respective sections. A description of some datasets, along with extra tables and figures, is provided in the Supplementary Materials file. Our newly generated Xenium data for Type II Diabetes can be found at https://doi.org/10.7303/syn68699752.
[SUPPLEMENTARY INFORMATION] A detailed description of some datasets, the relevant Algorithm, Tables, and Figures for the manuscript are presented in the Supplementary Materials file.