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001     279459
005     20250720001600.0
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037 _ _ |a DZNE-2025-00786
041 _ _ |a English
082 _ _ |a 004
100 1 _ |a Lian, Bin
|b 0
245 _ _ |a Inference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysis.
260 _ _ |a Oxford [u.a.]
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|b Oxford University Press
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520 _ _ |a 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.
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650 _ 7 |a gene coexpression networks
|2 Other
650 _ 7 |a gene functional modules
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650 _ 7 |a linear mixed model
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650 _ 7 |a single-cell RNA sequencing
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650 _ 2 |a Single-Cell Analysis: methods
|2 MeSH
650 _ 2 |a Gene Regulatory Networks
|2 MeSH
650 _ 2 |a Algorithms
|2 MeSH
650 _ 2 |a Transcriptome
|2 MeSH
650 _ 2 |a Gene Expression Profiling: methods
|2 MeSH
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Computational Biology: methods
|2 MeSH
650 _ 2 |a Animals
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700 1 _ |a Zhang, Haohui
|b 1
700 1 _ |a Wang, Tao
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700 1 _ |a Wang, Yongtian
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700 1 _ |a Shang, Xuequn
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700 1 _ |a Aziz, N Ahmad
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700 1 _ |a Hu, Jialu
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773 _ _ |a 10.1093/bib/bbaf309
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|t Briefings in bioinformatics
|v 26
|y 2025
|x 1467-5463
856 4 _ |u https://pub.dzne.de/record/279459/files/DZNE-2025-00786%20SUP.pdf
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