Mitigation of multi-scale biases in cell-type deconvolution for spatially resolved transcriptomics using HarmoDecon Article Swipe
Zirui Wang
,
Ke Xu
,
Yang Liu
,
Yu Xu
,
Lu Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btaf451
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btaf451
Motivation The advent of spatially resolved transcriptomics (SRT) has revolutionized our understanding of tissue molecular microenvironments by enabling the study of gene expression in its spatial context. However, many SRT platforms lack single-cell resolution, necessitating cell-type deconvolution methods to estimate cell-type proportions in SRT spots. Despite advancements in existing tools, these methods have not addressed biases occurring at three scales: individual spots, entire tissue samples, and discrepancies between SRT and reference scRNA-seq datasets. These biases result in overbalanced cell-type proportions for each spot, mismatched cell-type fractions at the sample level, and data distribution shifts across platforms. Results To mitigate these biases, we introduce HarmoDecon, a novel semi-supervised deep learning model for spatial cell-type deconvolution. HarmoDecon leverages pseudo-spots derived from scRNA-seq data and uses Gaussian Mixture Graph Convolutional Networks to address the aforementioned issues. Through extensive simulations on multi-cell spots from STARmap and osmFISH, HarmoDecon outperformed 11 state-of-the-art methods. Additionally, when applied to legacy SRT platforms and 10x Visium datasets, HarmoDecon achieved the highest accuracy in spatial domain clustering and maintained strong correlations between cancer marker genes and cancer cells in human breast cancer samples. These results highlight the utility of HarmoDecon in advancing spatial transcriptomics analysis. Availability and implementation The HarmoDecon scripts, with the detailed tutorials, are available at https://github.com/ericcombiolab/HarmoDecon/tree/main.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btaf451
- https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaf451/64014885/btaf451.pdf
- OA Status
- gold
- References
- 50
- Related Works
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- OpenAlex ID
- https://openalex.org/W4413111454
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413111454Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioinformatics/btaf451Digital Object Identifier
- Title
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Mitigation of multi-scale biases in cell-type deconvolution for spatially resolved transcriptomics using HarmoDeconWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-08Full publication date if available
- Authors
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Zirui Wang, Ke Xu, Yang Liu, Yu Xu, Lu ZhangList of authors in order
- Landing page
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https://doi.org/10.1093/bioinformatics/btaf451Publisher landing page
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaf451/64014885/btaf451.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaf451/64014885/btaf451.pdfDirect OA link when available
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Deconvolution, Scale (ratio), Computer science, Data mining, Artificial intelligence, Algorithm, Cartography, GeographyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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