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000279459 041__ $$aEnglish
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000279459 1001_ $$aLian, Bin$$b0
000279459 245__ $$aInference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysis.
000279459 260__ $$aOxford [u.a.]$$bOxford University Press$$c2025
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000279459 520__ $$aGene 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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000279459 650_7 $$2Other$$agene coexpression networks
000279459 650_7 $$2Other$$agene functional modules
000279459 650_7 $$2Other$$alinear mixed model
000279459 650_7 $$2Other$$asingle-cell RNA sequencing
000279459 650_2 $$2MeSH$$aSingle-Cell Analysis: methods
000279459 650_2 $$2MeSH$$aGene Regulatory Networks
000279459 650_2 $$2MeSH$$aAlgorithms
000279459 650_2 $$2MeSH$$aTranscriptome
000279459 650_2 $$2MeSH$$aGene Expression Profiling: methods
000279459 650_2 $$2MeSH$$aHumans
000279459 650_2 $$2MeSH$$aComputational Biology: methods
000279459 650_2 $$2MeSH$$aAnimals
000279459 7001_ $$aZhang, Haohui$$b1
000279459 7001_ $$aWang, Tao$$b2
000279459 7001_ $$aWang, Yongtian$$b3
000279459 7001_ $$aShang, Xuequn$$b4
000279459 7001_ $$0P:(DE-2719)2812578$$aAziz, N Ahmad$$b5$$udzne
000279459 7001_ $$0P:(DE-2719)9002875$$aHu, Jialu$$b6$$eLast author$$udzne
000279459 773__ $$0PERI:(DE-600)2036055-1$$a10.1093/bib/bbaf309$$gVol. 26, no. 4, p. bbaf309$$n4$$pbbaf309$$tBriefings in bioinformatics$$v26$$x1467-5463$$y2025
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