Inference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysis Article Swipe
Bin Lian
,
Haohui Zhang
,
Tao Wang
,
Yongtian Wang
,
Xuequn Shang
,
N. Ahmad Aziz
,
Jialu Hu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbaf309
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbaf309
Gene regulation varies across different cell types and developmental stages, leading to distinct cellular roles across cellular populations. Investigating cell type-specific gene coexpression is therefore crucial for understanding gene functions and disease pathology. However, reconstructing gene coexpression networks from single-cell transcriptome data is challenging due to artifacts, noise, and data sparsity. Here, we present an efficient method for inference of gene coexpression networks via variance decomposition analysis (GCNVDA) to explore the underlying gene regulatory mechanisms from single-cell transcriptome data. Our model incorporates multiple sources of variability, including a random effect term $G$ to capture gene-level variance and a random effect term $E$ to account for residual errors. We applied GCNVDA to three real-world single-cell datasets, demonstrating that our method outperforms existing state-of-the-art algorithms in both sensitivity and specificity for identifying tissue- or state-specific gene regulations. Furthermore, GCNVDA facilitates the discovery of functional modules that play critical roles in key biological processes such as embryonic development. These findings provide new insights into cell-specific regulatory mechanisms and have the potential to significantly advance research in developmental biology and disease pathology.
Related Topics
Concepts
Transcriptome
Gene regulatory network
Computational biology
Biology
Inference
Gene
Gene expression profiling
Regulation of gene expression
Gene expression
Computer science
Genetics
Artificial intelligence
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbaf309
- https://academic.oup.com/bib/article-pdf/26/4/bbaf309/63676968/bbaf309.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412065402
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412065402Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bib/bbaf309Digital Object Identifier
- Title
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Inference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysisWork 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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2025-07-01Full publication date if available
- Authors
-
Bin Lian, Haohui Zhang, Tao Wang, Yongtian Wang, Xuequn Shang, N. Ahmad Aziz, Jialu HuList of authors in order
- Landing page
-
https://doi.org/10.1093/bib/bbaf309Publisher landing page
- PDF URL
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https://academic.oup.com/bib/article-pdf/26/4/bbaf309/63676968/bbaf309.pdfDirect link to full text PDF
- Open access
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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/bib/article-pdf/26/4/bbaf309/63676968/bbaf309.pdfDirect OA link when available
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Transcriptome, Gene regulatory network, Computational biology, Biology, Inference, Gene, Gene expression profiling, Regulation of gene expression, Gene expression, Computer science, Genetics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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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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