The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene–gene correlations. Benchmarking demonstrates superior performance of NetID compared to imputation-based methods. By incorporating cell fate probability information, NetID facilitates the prediction of lineage-specific GRNs and recovers known network motifs governing bone marrow hematopoiesis, making it a powerful toolkit for deciphering gene regulatory control of cellular differentiation from large-scale single-cell transcriptome data.

中文翻译：

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使用 NetID 从元细胞中可扩展地鉴定谱系特异性基因调控网络


基因调控网络 （GRN） 的鉴定对于了解细胞分化至关重要。单细胞 RNA 测序数据以高分辨率编码基因水平协变，但数据稀疏性和高维数阻碍了准确和可扩展的 GRN 重建。为了克服这些挑战，我们引入了利用同质元胞的 NetID，同时避免了虚假的基因-基因相关性。基准测试表明，与基于插补的方法相比，NetID 的性能更胜一筹。通过整合细胞命运概率信息，NetID 有助于预测谱系特异性 GRN，并恢复控制骨髓造血的已知网络基序，使其成为从大规模单细胞转录组数据中破译细胞分化的基因调控的强大工具包。