Robust Spatial Cell‐Type Deconvolution with Qualitative Reference for Spatial Transcriptomics (Small Methods 5/2025)
Spatial Transcriptomics In article number 2401145, Xingjie Shi, Jin Liu, and co-workers present QR-SIDE, a robust method for deconvolving multicellular spatial transcriptomics (SRT) data without requiring matched scRNA-seq references. By integrating marker gene spatial heterogeneity via Poisson regression and modeling non-marker genes with a hierarchical factor model, QR-SIDE improves robust cell-type abundance estimation and spatial domain identification. Validated on diverse SRT datasets, QR- IDE enhances spatial tissue analysis at finer cellular resolution.
Authors
- Haipeng Shen (ORCID: https://orcid.org/0000-0002-0173-8504)
- Jin Liu (ORCID: https://orcid.org/0000-0002-5707-2078)
- Xingjie Shi (ORCID: https://orcid.org/0000-0002-9866-8599)
- Zuojie Luo (ORCID: https://orcid.org/0000-0003-2969-8329)
- Qishi Dong
- Yi Yang (ORCID: https://orcid.org/0000-0003-1665-5258)
Institutions
- Hong Kong Baptist University (HK)
- Chinese University of Hong Kong, Shenzhen (CN)
- Shenzhen Technology University (CN)
- East China Normal University (CN)
- Southeast University (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Small Methods
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1002/smtd.202570030
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00