Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single‐Cell Reference and Domain Adaptive Matching Article Swipe
Xinyang Guo
,
Zhaoyang Huang
,
Fen Ju
,
Chenguang Zhao
,
Liang Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202306329
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202306329
Accurately identifies the cellular composition of complex tissues, which is critical for understanding disease pathogenesis, early diagnosis, and prevention. However, current methods for deconvoluting bulk RNA sequencing (RNA‐seq) typically rely on matched single‐cell RNA sequencing (scRNA‐seq) as a reference, which can be limiting due to differences in sequencing distribution and the potential for invalid information from single‐cell references. Hence, a novel computational method named SCROAM is introduced to address these challenges. SCROAM transforms scRNA‐seq and bulk RNA‐seq into a shared feature space, effectively eliminating distributional differences in the latent space. Subsequently, cell‐type‐specific expression matrices are generated from the scRNA‐seq data, facilitating the precise identification of cell types within bulk tissues. The performance of SCROAM is assessed through benchmarking against simulated and real datasets, demonstrating its accuracy and robustness. To further validate SCROAM's performance, single‐cell and bulk RNA‐seq experiments are conducted on mouse spinal cord tissue, with SCROAM applied to identify cell types in bulk tissue. Results indicate that SCROAM is a highly effective tool for identifying similar cell types. An integrated analysis of liver cancer and primary glioblastoma is then performed. Overall, this research offers a novel perspective for delivering precise insights into disease pathogenesis and potential therapeutic strategies.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/advs.202306329
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202306329
- OA Status
- gold
- Cited By
- 39
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389526560
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Raw OpenAlex JSON
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https://openalex.org/W4389526560Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/advs.202306329Digital Object Identifier
- Title
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Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single‐Cell Reference and Domain Adaptive MatchingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-12-10Full publication date if available
- Authors
-
Xinyang Guo, Zhaoyang Huang, Fen Ju, Chenguang Zhao, Liang YuList of authors in order
- Landing page
-
https://doi.org/10.1002/advs.202306329Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202306329Direct 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://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202306329Direct OA link when available
- Concepts
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Matching (statistics), Domain (mathematical analysis), Abundance (ecology), Type (biology), Biological system, Computer science, Cell, Estimation, Algorithm, Mathematics, Chemistry, Biology, Statistics, Mathematical analysis, Engineering, Biochemistry, Ecology, Systems engineering, FisheryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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39Total citation count in OpenAlex
- Citations by year (recent)
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2025: 27, 2024: 11, 2023: 1Per-year citation counts (last 5 years)
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56Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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