ArticleSmall methods2025
Robust Spatial Cell-Type Deconvolution with Qualitative Reference for Spatial Transcriptomics.
Article in Small methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Deciphering microenvironmental heterogeneity by scalable Niche Guided Module Discovery.Communications biology · 2026Article
- Robust Spatial Cell-Type Deconvolution with Qualitative Reference for Spatial Transcriptomics.Small methods · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Many spatially resolved transcriptomic technologies have been developed to provide gene expression profiles for spots that may contain heterogeneous mixtures of cells. To decompose cellular composition and expression levels, various deconvolution methods have been developed using single-cell RNA sequencing (scRNA-seq) data with known cell-type labels as a reference. However, in the absence of a reliable reference dataset or in the presence of heterogeneous batch effects, these methods may introduce bias. Here, a Qualitative-Reference-based Spatially-Informed Deconvolution method (QR-SIDE) is developed for multi-cellular spatial transcriptomic data. Uniquely, QR-SIDE provides a detailed map of spatial heterogeneity for individual marker genes and performs robust deconvolution by adaptively adjusting the contributions of each marker gene. Simultaneously, QR-SIDE unifies cell-type deconvolution with spatial clustering and incorporates spatial information via a Potts model to promote spatial continuity. The identified spatial domains represent a meaningful biological effect in potential tissue segments. Using simulated data and three real spatial transcriptomic datasets from the 10x Visium and ST platforms, QR-SIDE demonstrates improved accuracy and robustness in cell-type deconvolution and its superiority over established methods in recognizing and delineating spatial structures within a given context. These results can facilitate a range of downstream analyses and provide a refined understanding of cellular heterogeneity.
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Registered trials
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