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@ARTICLE{Lian:279459,
      author       = {Lian, Bin and Zhang, Haohui and Wang, Tao and Wang,
                      Yongtian and Shang, Xuequn and Aziz, N Ahmad and Hu, Jialu},
      title        = {{I}nference of gene coexpression networks from single-cell
                      transcriptome data based on variance decomposition
                      analysis.},
      journal      = {Briefings in bioinformatics},
      volume       = {26},
      number       = {4},
      issn         = {1467-5463},
      address      = {Oxford [u.a.]},
      publisher    = {Oxford University Press},
      reportid     = {DZNE-2025-00786},
      pages        = {bbaf309},
      year         = {2025},
      abstract     = {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.},
      keywords     = {Single-Cell Analysis: methods / Gene Regulatory Networks /
                      Algorithms / Transcriptome / Gene Expression Profiling:
                      methods / Humans / Computational Biology: methods / Animals
                      / gene coexpression networks (Other) / gene functional
                      modules (Other) / linear mixed model (Other) / single-cell
                      RNA sequencing (Other)},
      cin          = {AG Aziz},
      ddc          = {004},
      cid          = {I:(DE-2719)5000071},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40618350},
      pmc          = {pmc:PMC12229094},
      doi          = {10.1093/bib/bbaf309},
      url          = {https://pub.dzne.de/record/279459},
}