Highly accurate estimation of cell type abundance in bulk tissues based on single-cell reference and domain adaptive matching Article Swipe
Xin-Yang Guo
,
Zhaoyang Huang
,
Fen Ju
,
Chenguang Zhao
,
Liang Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.07.22.550132
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.07.22.550132
Accurately identifying the cellular composition of complex tissues 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. To overcome these limitations, we introduced SCROAM, a novel computational method that overcomes these challenges. SCROAM transforms scRNA-seq and bulk RNA-seq into a shared feature space, effectively eliminating distributional differences in the latent space. We then generate cell-type-specific expression matrices from scRNA-seq, enabling accurate identification of cell types in bulk tissues. We evaluated the performance of SCROAM by benchmarking it against simulated datasets and human breast cancer and peripheral blood datasets, demonstrating its accuracy and robustness. To further validate SCROAM’s performance, we conducted single-cell and bulk RNA-seq experiments on mouse spinal cord tissue and applied SCROAM to identify bulk tissue cell types. Our results indicate that SCROAM is a highly effective tool for identifying similar cell types, surpassing the performance of existing methods. We then performed an integrated analysis of liver cancer and primary glioblastoma to investigate the relationship between cell type composition and clinical outcomes in various tumor types, highlighting the significance of SCROAM for understanding cellular heterogeneity in complex diseases. Overall, our work presents a novel perspective to accurately infer cellular composition and expression in bulk RNA-seq, offering valuable insights into disease pathogenesis and potential therapeutic strategies.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.07.22.550132
- https://www.biorxiv.org/content/biorxiv/early/2023/07/25/2023.07.22.550132.full.pdf
- OA Status
- green
- Cited By
- 3
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385257906
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385257906Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.07.22.550132Digital 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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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-25Full publication date if available
- Authors
-
Xin-Yang Guo, Zhaoyang Huang, Fen Ju, Chenguang Zhao, Liang YuList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.07.22.550132Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/07/25/2023.07.22.550132.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2023/07/25/2023.07.22.550132.full.pdfDirect OA link when available
- Concepts
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Cell type, Computational biology, RNA, Computer science, Cell, Robustness (evolution), Biology, Algorithm, Gene, GeneticsTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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55Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2028426093, https://openalex.org/W2625559053, https://openalex.org/W2952001873, https://openalex.org/W2788357641, https://openalex.org/W2233603983, https://openalex.org/W4250589301, https://openalex.org/W2109816625, https://openalex.org/W1969096316, https://openalex.org/W2794480084, https://openalex.org/W2248907500, https://openalex.org/W2747643344, https://openalex.org/W1574447377, https://openalex.org/W4206581595, https://openalex.org/W3007172120, https://openalex.org/W3000010184, https://openalex.org/W3186021169, https://openalex.org/W2098454110, https://openalex.org/W2789878273, https://openalex.org/W1995086037, https://openalex.org/W2158549544, https://openalex.org/W3132971647, https://openalex.org/W2950536412, https://openalex.org/W123476658, https://openalex.org/W2799838493, https://openalex.org/W2307117515, https://openalex.org/W2790003308, https://openalex.org/W3019556496, https://openalex.org/W3119507732, https://openalex.org/W3155082085, https://openalex.org/W2105064756, https://openalex.org/W2102212449, https://openalex.org/W2896518632, https://openalex.org/W3112963314, https://openalex.org/W3044352330, https://openalex.org/W2056716515, https://openalex.org/W2251003032, https://openalex.org/W2911922390, https://openalex.org/W2942610007, https://openalex.org/W2068767807, https://openalex.org/W2739083307, https://openalex.org/W2470906679, https://openalex.org/W2069295116, https://openalex.org/W2949177718, https://openalex.org/W2953757801, https://openalex.org/W3179857518, https://openalex.org/W2329659234, https://openalex.org/W2899588238, https://openalex.org/W1983951224, https://openalex.org/W2952240309, https://openalex.org/W3044190151, https://openalex.org/W2782326402, https://openalex.org/W3173698767, https://openalex.org/W1977391294, https://openalex.org/W2766048952, https://openalex.org/W4399572897 |
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