PDF(Pubmed)
Abstract：
Identifying the causal interactions in gene-regulatory networks requires an accurate understanding of the time-lagged relationships between transcription factors and their target genes. Here we describe DELAY (short for Depicting Lagged Causality), a convolutional neural network for the inference of gene-regulatory relationships across pseudotime-ordered single-cell trajectories. We show that combining supervised deep learning with joint probability matrices of pseudotime-lagged trajectories allows the network to overcome important limitations of ordinary Granger causality-based methods, for example, the inability to infer cyclic relationships such as feedback loops. Our network outperforms several common methods for inferring gene regulation and, when given partial ground-truth labels, predicts novel regulatory networks from single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data sets. To validate this approach, we used DELAY to identify important genes and modules in the regulatory network of auditory hair cells, as well as likely DNA-binding partners for two hair cell cofactors (Hist1h1c and Ccnd1) and a novel binding sequence for the hair cell-specific transcription factor Fiz1. We provide an easy-to-use implementation of DELAY under an open-source license at https://github.com/calebclayreagor/DELAY.
摘要：
识别基因调控网络中的因果相互作用需要准确理解转录因子与其靶基因之间的时滞关系。在这里，我们描述了延迟(描述滞后因果关系的缩写)，一种卷积神经网络，用于在伪时间排序的单细胞轨迹上推断基因调控关系。我们表明，将有监督的深度学习与伪时间滞后轨迹的联合概率矩阵相结合，可以使网络克服基于普通Granger因果关系的方法的重要限制。例如,无法推断循环关系，如反馈循环。我们的网络优于几种常见的推断基因调控的方法，当给予部分真相标签时，从单细胞RNA测序（scRNA-seq）和单细胞ATAC测序（scATAC-seq）数据集预测新的调控网络。为了验证这种方法，我们使用DELAY来识别听觉毛细胞调节网络中的重要基因和模块，以及两种毛细胞辅因子（Hist1h1c和Ccnd1）的可能的DNA结合伴侣和毛细胞特异性转录因子Fiz1的新型结合序列。我们在https://github.com/calebclayreagor/DELAY的开源许可证下提供易于使用的DELAY实现。
