Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
Department of Computer Science, Purdue University, IN 47907, United States.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac389.
Computational recovery of gene regulatory network (GRN) has recently undergone a great shift from bulk-cell towards designing algorithms targeting single-cell data. In this work, we investigate whether the widely available bulk-cell data could be leveraged to assist the GRN predictions for single cells. We infer cell-type-specific GRNs from both the single-cell RNA sequencing data and the generic GRN derived from the bulk cells by constructing a weakly supervised learning framework based on the axial transformer. We verify our assumption that the bulk-cell transcriptomic data are a valuable resource, which could improve the prediction of single-cell GRN by conducting extensive experiments. Our GRN-transformer achieves the state-of-the-art prediction accuracy in comparison to existing supervised and unsupervised approaches. In addition, we show that our method can identify important transcription factors and potential regulations for Alzheimer's disease risk genes by using the predicted GRN. Availability: The implementation of GRN-transformer is available at https://github.com/HantaoShu/GRN-Transformer.
计算基因调控网络（GRN）的恢复最近经历了从批量细胞到针对单细胞数据设计算法的重大转变。在这项工作中，我们研究了广泛可用的批量细胞数据是否可以被利用来辅助单细胞的 GRN 预测。我们通过基于轴向转换器构建一个弱监督学习框架，从单细胞 RNA 测序数据和从批量细胞中得出的通用 GRN 中推断出细胞类型特异性的 GRN。我们通过进行广泛的实验验证了我们的假设，即批量细胞转录组数据是一种有价值的资源，可以通过它来提高单细胞 GRN 的预测。与现有的监督和无监督方法相比，我们的 GRN-Transformer 实现了最先进的预测准确性。此外，我们还表明，我们的方法可以通过预测的 GRN 识别阿尔茨海默病风险基因的重要转录因子和潜在调控。
GRN-Transformer 的实现可在 https://github.com/HantaoShu/GRN-Transformer 上获得。
