Mukherjee Sumit, Carignano Alberto, Seelig Georg, Lee Su-In
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5034-5040. doi: 10.1109/EMBC.2018.8513444.
Identifying the gene regulatory networks that control development or disease is one of the most important problems in biology. Here, we introduce a computational approach, called PIPER (ProgressIve network PERturbation), to identify the perturbed genes that drive differences in the gene regulatory network across different points in a biological progression. PIPER employs algorithms tailor-made for single cell RNA sequencing (scRNA-seq) data to jointly identify gene networks for multiple progressive conditions. It then performs differential network analysis along the identified gene networks to identify master regulators. We demonstrate that PIPER outperforms state-of-the-art alternative methods on simulated data and is able to predict known key regulators of differentiation on real scRNA-Seq datasets.
识别控制发育或疾病的基因调控网络是生物学中最重要的问题之一。在此,我们引入一种名为PIPER(渐进式网络扰动)的计算方法,以识别在生物进程的不同点驱动基因调控网络差异的受扰动基因。PIPER采用为单细胞RNA测序(scRNA-seq)数据量身定制的算法,来共同识别多种渐进条件下的基因网络。然后,它沿着已识别的基因网络进行差异网络分析,以识别主要调控因子。我们证明,PIPER在模拟数据上优于当前最先进的替代方法,并且能够在真实的scRNA-Seq数据集上预测已知的分化关键调控因子。